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Adaptive Hierarchical Representation Learning for Long-Tailed Object Detection | Banghuai Li | General object detectors are always evaluated on hand-designed datasets, e.g., MS COCO and Pascal VOC, which tend to maintain balanced data distribution over different classes. However, it goes against the practical applications in the real world which suffer from a heavy class imbalance problem, known as the long-tailed object detection. In this paper, we propose a novel method, named Adaptive Hierarchical Representation Learning (AHRL), from a metric learning perspective to address long-tailed object detection. We visualize each learned class representation in the feature space, and observe that some classes, especially under-represented scarce classes, are prone to cluster with analogous ones due to the lack of discriminative representation. Inspired by this, we propose to split the whole feature space into a hierarchical structure and eliminate the problem in a divide-and-conquer way. AHRL contains a two-stage training paradigm. First, we train a normal baseline model and construct the hierarchical structure under the unsupervised clustering method. Then, we design an AHR loss that consists of two optimization objectives. On the one hand, AHR loss retains the hierarchical structure and keeps representation clusters away from each other. On the other hand, AHR loss adopts adaptive margins according to specific class pairs in the same cluster to further optimize locally. We conduct extensive experiments on the challenging LVIS dataset and AHRL outperforms all the existing state-of-the-art(SOTA) methods, with 29.1% segmentation AP and 29.3% box AP on LVIS v0.5 and 27.6% segmentation AP and 28.7% box AP on LVIS v1.0 based on ResNet-101. We hope our simple yet effective approach will serve as a solid baseline to help stimulate future research in long-tailed object detection. Code will be released soon. | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_Adaptive_Hierarchical_Representation_Learning_for_Long-Tailed_Object_Detection_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Li_Adaptive_Hierarchical_Representation_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Adaptive_Hierarchical_Representation_Learning_for_Long-Tailed_Object_Detection_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Adaptive_Hierarchical_Representation_Learning_for_Long-Tailed_Object_Detection_CVPR_2022_paper.html | CVPR 2022 | null |
FlexIT: Towards Flexible Semantic Image Translation | Guillaume Couairon, Asya Grechka, Jakob Verbeek, Holger Schwenk, Matthieu Cord | Deep generative models, like GANs, have considerably improved the state of the art in image synthesis, and are able to generate near photo-realistic images in structured domains such as human faces. Based on this success, recent work on image editing proceeds by projecting images to the GAN latent space and manipulating the latent vector. However, these approaches are limited in that only images from a narrow domain can be transformed, and with only a limited number of editing operations. We propose FlexIT, a novel method which can take any input image and a user-defined text instruction for editing. Our method achieves flexible and natural editing, pushing the limits of semantic image translation. First, FlexIT combines the input image and text into a single target point in the CLIP multimodal embedding space. Via the latent space of an autoencoder, we iteratively transform the input image toward the target point, ensuring coherence and quality with a variety of novel regularization terms. We propose an evaluation protocol for semantic image translation, and thoroughly evaluate our method on ImageNet. Code will be available at https://github.com/facebookresearch/SemanticImageTranslation/. | https://openaccess.thecvf.com/content/CVPR2022/papers/Couairon_FlexIT_Towards_Flexible_Semantic_Image_Translation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Couairon_FlexIT_Towards_Flexible_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.04705 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Couairon_FlexIT_Towards_Flexible_Semantic_Image_Translation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Couairon_FlexIT_Towards_Flexible_Semantic_Image_Translation_CVPR_2022_paper.html | CVPR 2022 | null |
Face2Exp: Combating Data Biases for Facial Expression Recognition | Dan Zeng, Zhiyuan Lin, Xiao Yan, Yuting Liu, Fei Wang, Bo Tang | Facial expression recognition (FER) is challenging due to the class imbalance caused by data collection. Existing studies tackle the data bias problem using only labeled facial expression dataset. Orthogonal to existing FER methods, we propose to utilize large unlabeled face recognition (FR) datasets to enhance FER. However, this raises another data bias problem---the distribution mismatch between FR and FER data. To combat the mismatch, we propose the Meta-Face2Exp framework, which consists of a base network and an adaptation network. The base network learns prior expression knowledge on class-balanced FER data while the adaptation network is trained to fit the pseudo labels of FR data generated by the base model. To combat the mismatch between FR and FER data, Meta-Face2Exp uses a circuit feedback mechanism, which improves the base network with the feedback from the adaptation network. Experiments show that our Meta-Face2Exp achieves comparable accuracy to state-of-the-art FER methods with 10% of the labeled FER data utilized by the baselines. We also demonstrate that the circuit feedback mechanism successfully eliminates data bias. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zeng_Face2Exp_Combating_Data_Biases_for_Facial_Expression_Recognition_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zeng_Face2Exp_Combating_Data_Biases_for_Facial_Expression_Recognition_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zeng_Face2Exp_Combating_Data_Biases_for_Facial_Expression_Recognition_CVPR_2022_paper.html | CVPR 2022 | null |
SAR-Net: Shape Alignment and Recovery Network for Category-Level 6D Object Pose and Size Estimation | Haitao Lin, Zichang Liu, Chilam Cheang, Yanwei Fu, Guodong Guo, Xiangyang Xue | Given a single scene image, this paper proposes a method of Category-level 6D Object Pose and Size Estimation (COPSE) from the point cloud of the target object, without external real pose-annotated training data. Specifically, beyond the visual cues in RGB images, we rely on the shape information predominately from the depth (D) channel. The key idea is to explore the shape alignment of each instance against its corresponding category-level template shape, and the symmetric correspondence of each object category for estimating a coarse 3D object shape. Our framework deforms the point cloud of the category-level template shape to align the observed instance point cloud for implicitly representing its 3D rotation. Then we model the symmetric correspondence by predicting symmetric point cloud from the partially observed point cloud. The concatenation of the observed point cloud and symmetric one reconstructs a coarse object shape, thus facilitating object center (3D translation) and 3D size estimation. Extensive experiments on the category-level NOCS benchmark demonstrate that our lightweight model still competes with state-of-the-art approaches that require labeled real-world images. We also deploy our approach to a physical Baxter robot to perform grasping tasks on unseen but category-known instances, and the results further validate the efficacy of our proposed model. Code and pre-trained models are available on the project webpage. | https://openaccess.thecvf.com/content/CVPR2022/papers/Lin_SAR-Net_Shape_Alignment_and_Recovery_Network_for_Category-Level_6D_Object_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Lin_SAR-Net_Shape_Alignment_CVPR_2022_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Lin_SAR-Net_Shape_Alignment_and_Recovery_Network_for_Category-Level_6D_Object_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Lin_SAR-Net_Shape_Alignment_and_Recovery_Network_for_Category-Level_6D_Object_CVPR_2022_paper.html | CVPR 2022 | null |
Whose Hands Are These? Hand Detection and Hand-Body Association in the Wild | Supreeth Narasimhaswamy, Thanh Nguyen, Mingzhen Huang, Minh Hoai | We study a new problem of detecting hands and finding the location of the corresponding person for each detected hand. This task is helpful for many downstream tasks such as hand tracking and hand contact estimation. Associating hands with people is challenging in unconstrained conditions since multiple people can be present in the scene with varying overlaps and occlusions. We propose a novel end-to-end trainable convolutional network that can jointly detect hands and the body location for the corresponding person. Our method first detects a set of hands and bodies and uses a novel Hand-Body Association Network to predict association scores between them. We use these association scores to find the body location for each detected hand. We also introduce a new challenging dataset called BodyHands containing unconstrained images with hand and their corresponding body locations annotations. We conduct extensive experiments on BodyHands and another public dataset to show the effectiveness of our method. Finally, we demonstrate the benefits of hand-body association in two critical applications: hand tracking and hand contact estimation. Our experiments show that hand tracking and hand contact estimation methods can be improved significantly by reasoning about the hand-body association. | https://openaccess.thecvf.com/content/CVPR2022/papers/Narasimhaswamy_Whose_Hands_Are_These_Hand_Detection_and_Hand-Body_Association_in_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Narasimhaswamy_Whose_Hands_Are_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Narasimhaswamy_Whose_Hands_Are_These_Hand_Detection_and_Hand-Body_Association_in_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Narasimhaswamy_Whose_Hands_Are_These_Hand_Detection_and_Hand-Body_Association_in_CVPR_2022_paper.html | CVPR 2022 | null |
Mega-NERF: Scalable Construction of Large-Scale NeRFs for Virtual Fly-Throughs | Haithem Turki, Deva Ramanan, Mahadev Satyanarayanan | We use neural radiance fields (NeRFs) to build interactive 3D environments from large-scale visual captures spanning buildings or even multiple city blocks collected primarily from drones. In contrast to single object scenes (on which NeRFs are traditionally evaluated), our scale poses multiple challenges including (1) the need to model thousands of images with varying lighting conditions, each of which capture only a small subset of the scene, (2) prohibitively large model capacities that make it infeasible to train on a single GPU, and (3) significant challenges for fast rendering that would enable interactive fly-throughs. To address these challenges, we begin by analyzing visibility statistics for large-scale scenes, motivating a sparse network structure where parameters are specialized to different regions of the scene. We introduce a simple geometric clustering algorithm for data parallelism that partitions training images (or rather pixels) into different NeRF submodules that can be trained in parallel. We evaluate our approach on existing datasets (Quad 6k and UrbanScene3D) as well as against our own drone footage, improving training speed by 3x and PSNR by 12%. We also evaluate recent NeRF fast renderers on top of Mega-NeRF and introduce a novel method that exploits temporal coherence. Our technique achieves a 40x speedup over conventional NeRF rendering while remaining within 0.8 db in PSNR quality, exceeding the fidelity of existing fast renderers. | https://openaccess.thecvf.com/content/CVPR2022/papers/Turki_Mega-NERF_Scalable_Construction_of_Large-Scale_NeRFs_for_Virtual_Fly-Throughs_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Turki_Mega-NERF_Scalable_Construction_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Turki_Mega-NERF_Scalable_Construction_of_Large-Scale_NeRFs_for_Virtual_Fly-Throughs_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Turki_Mega-NERF_Scalable_Construction_of_Large-Scale_NeRFs_for_Virtual_Fly-Throughs_CVPR_2022_paper.html | CVPR 2022 | null |
PINA: Learning a Personalized Implicit Neural Avatar From a Single RGB-D Video Sequence | Zijian Dong, Chen Guo, Jie Song, Xu Chen, Andreas Geiger, Otmar Hilliges | We present a novel method to learn Personalized Implicit Neural Avatars (PINA) from a short RGB-D sequence. This allows non-expert users to create a detailed and personalized virtual copy of themselves, which can be animated with realistic clothing deformations. PINA does not require complete scans, nor does it require a prior learned from large datasets of clothed humans. Learning a complete avatar in this setting is challenging, since only few depth observations are available, which are noisy and incomplete (i.e. only partial visibility of the body per frame). We propose a method to learn the shape and non-rigid deformations via a pose-conditioned implicit surface and a deformation field, defined in canonical space. This allows us to fuse all partial observations into a single consistent canonical representation. Fusion is formulated as a global optimization problem over the pose, shape and skinning parameters. The method can learn neural avatars from real noisy RGB-D sequences for a diverse set of people and clothing styles and these avatars can be animated given unseen motion sequences. | https://openaccess.thecvf.com/content/CVPR2022/papers/Dong_PINA_Learning_a_Personalized_Implicit_Neural_Avatar_From_a_Single_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Dong_PINA_Learning_a_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Dong_PINA_Learning_a_Personalized_Implicit_Neural_Avatar_From_a_Single_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Dong_PINA_Learning_a_Personalized_Implicit_Neural_Avatar_From_a_Single_CVPR_2022_paper.html | CVPR 2022 | null |
Forecasting From LiDAR via Future Object Detection | Neehar Peri, Jonathon Luiten, Mengtian Li, Aljoša Ošep, Laura Leal-Taixé, Deva Ramanan | Object detection and forecasting are fundamental components of embodied perception. These two problems, however, are largely studied in isolation by the community. In this paper, we propose an end-to-end approach for motion forecasting based on raw sensor measurement as opposed to ground truth tracks. Instead of predicting the current frame locations and forecast forward in time, we directly predict future object locations and backcast in time to determine where each trajectory began. Our approach not only greatly boosts the overall accuracy compared to modular or other end-to-end baselines, it also prompts us to rethink the role of explicit tracking for embodied perception. Additionally, by linking future and current locations in a multiple-to-one manner, our approach is able to reason about multiple futures, a capability that was previously considered difficult for end-to-end approaches. We conduct extensive experiments on the popular autonomous driving dataset nuScenes and demonstrate the empirical effectiveness of our approach. In addition, we investigate the appropriateness of reusing standard forecasting metrics for an end-to-end setup, and find a number of flaws which allow us to build simple baselines to game these metrics. We address this issue with a novel set of joint forecasting and detection metrics that extend the commonly used AP metrics used in the detection community to measuring forecasting accuracy. | https://openaccess.thecvf.com/content/CVPR2022/papers/Peri_Forecasting_From_LiDAR_via_Future_Object_Detection_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Peri_Forecasting_From_LiDAR_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.16297 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Peri_Forecasting_From_LiDAR_via_Future_Object_Detection_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Peri_Forecasting_From_LiDAR_via_Future_Object_Detection_CVPR_2022_paper.html | CVPR 2022 | null |
CRAFT: Cross-Attentional Flow Transformer for Robust Optical Flow | Xiuchao Sui, Shaohua Li, Xue Geng, Yan Wu, Xinxing Xu, Yong Liu, Rick Goh, Hongyuan Zhu | Optical flow estimation aims to find the 2D motion field by identifying corresponding pixels between two images. Despite the tremendous progress of deep learning-based optical flow methods, it remains a challenge to accurately estimate large displacements with motion blur. This is mainly because the correlation volume, the basis of pixel matching, is computed as the dot product of the convolutional features of the two images. The locality of convolutional features makes the computed correlations susceptible to various noises. On large displacements with motion blur, noisy correlations could cause severe errors in the estimated flow. To overcome this challenge, we propose a new architecture "CRoss-Attentional Flow Transformer" (CRAFT), aiming to revitalize the correlation volume computation. In CRAFT, a Semantic Smoothing Transformer layer transforms the features of one frame, making them more global and semantically stable. In addition, the dot-product correlations are replaced with transformer Cross-Frame Attention. This layer filters out feature noises through the Query and Key projections, and computes more accurate correlations. On Sintel (Final) and KITTI (foreground) benchmarks, CRAFT has achieved new state-of-the-art performance. Moreover, to test the robustness of different models on large motions, we designed an image shifting attack that shifts input images to generate large artificial motions. Under this attack, CRAFT performs much more robustly than two representative methods, RAFT and GMA. The code of CRAFT is is available at https://github.com/askerlee/craft. | https://openaccess.thecvf.com/content/CVPR2022/papers/Sui_CRAFT_Cross-Attentional_Flow_Transformer_for_Robust_Optical_Flow_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Sui_CRAFT_Cross-Attentional_Flow_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.16896 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Sui_CRAFT_Cross-Attentional_Flow_Transformer_for_Robust_Optical_Flow_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Sui_CRAFT_Cross-Attentional_Flow_Transformer_for_Robust_Optical_Flow_CVPR_2022_paper.html | CVPR 2022 | null |
Adversarial Eigen Attack on Black-Box Models | Linjun Zhou, Peng Cui, Xingxuan Zhang, Yinan Jiang, Shiqiang Yang | Black-box adversarial attack has aroused much research attention for its difficulty on nearly no available information of the attacked model and the additional constraint on the query budget. A common way to improve attack efficiency is to transfer the gradient information of a white-box substitute model trained on an extra dataset. In this paper, we deal with a more practical setting where a pre-trained white-box model with network parameters is provided without extra training data. To solve the model mismatch problem between the white-box and black-box models, we propose a novel algorithm EigenBA by systematically integrating gradient-based white-box method and zeroth-order optimization in black-box methods. We theoretically show the optimal directions of perturbations for each step are closely related to the right singular vectors of the Jacobian matrix of the pretrained white-box model. Extensive experiments on ImageNet, CIFAR-10 and WebVision show that EigenBA can consistently and significantly outperform state-of-the-art baselines in terms of success rate and attack efficiency. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhou_Adversarial_Eigen_Attack_on_Black-Box_Models_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhou_Adversarial_Eigen_Attack_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhou_Adversarial_Eigen_Attack_on_Black-Box_Models_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhou_Adversarial_Eigen_Attack_on_Black-Box_Models_CVPR_2022_paper.html | CVPR 2022 | null |
Training Quantised Neural Networks With STE Variants: The Additive Noise Annealing Algorithm | Matteo Spallanzani, Gian Paolo Leonardi, Luca Benini | Training quantised neural networks (QNNs) is a non-differentiable optimisation problem since weights and features are output by piecewise constant functions. The standard solution is to apply the straight-through estimator (STE), using different functions during the inference and gradient computation steps. Several STE variants have been proposed in the literature aiming to maximise the task accuracy of the trained network. In this paper, we analyse STE variants and study their impact on QNN training. We first observe that most such variants can be modelled as stochastic regularisations of stair functions; although this intuitive interpretation is not new, our rigorous discussion generalises to further variants. Then, we analyse QNNs mixing different regularisations, finding that some suitably synchronised smoothing of each layer map is required to guarantee pointwise compositional convergence to the target discontinuous function. Based on these theoretical insights, we propose additive noise annealing (ANA), a new algorithm to train QNNs encompassing standard STE and its variants as special cases. When testing ANA on the CIFAR-10 image classification benchmark, we find that the major impact on task accuracy is not due to the qualitative shape of the regularisations but to the proper synchronisation of the different STE variants used in a network, in accordance with the theoretical results. | https://openaccess.thecvf.com/content/CVPR2022/papers/Spallanzani_Training_Quantised_Neural_Networks_With_STE_Variants_The_Additive_Noise_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Spallanzani_Training_Quantised_Neural_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.11323 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Spallanzani_Training_Quantised_Neural_Networks_With_STE_Variants_The_Additive_Noise_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Spallanzani_Training_Quantised_Neural_Networks_With_STE_Variants_The_Additive_Noise_CVPR_2022_paper.html | CVPR 2022 | null |
Split Hierarchical Variational Compression | Tom Ryder, Chen Zhang, Ning Kang, Shifeng Zhang | Variational autoencoders (VAEs) have witnessed great success in performing the compression of image datasets. This success, made possible by the bits-back coding framework, has produced competitive compression performance across many benchmarks. However, despite this, VAE architectures are currently limited by a combination of coding practicalities and compression ratios. That is, not only do state-of-the-art methods, such as normalizing flows, often demonstrate out-performance, but the initial bits required in coding makes single and parallel image compression challenging. To remedy this, we introduce Split Hierarchical Variational Compression (SHVC). SHVC introduces two novelties. Firstly, we propose an efficient autoregressive prior, the autoregressive sub-pixel convolution, that allows a generalisation between per-pixel autoregressions and fully factorised probability models. Secondly, we define our coding framework, the autoregressive initial bits, that flexibly supports parallel coding and avoids -- for the first time -- many of the practicalities commonly associated with bits-back coding. In our experiments, we demonstrate SHVC is able to achieve state-of-the-art compression performance across full-resolution lossless image compression tasks, with up to 100x fewer model parameters than competing VAE approaches. | https://openaccess.thecvf.com/content/CVPR2022/papers/Ryder_Split_Hierarchical_Variational_Compression_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Ryder_Split_Hierarchical_Variational_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.02071 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Ryder_Split_Hierarchical_Variational_Compression_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Ryder_Split_Hierarchical_Variational_Compression_CVPR_2022_paper.html | CVPR 2022 | null |
Video Swin Transformer | Ze Liu, Jia Ning, Yue Cao, Yixuan Wei, Zheng Zhang, Stephen Lin, Han Hu | The vision community is witnessing a modeling shift from CNNs to Transformers, where pure Transformer architectures have attained top accuracy on the major video recognition benchmarks. These video models are all built on Transformer layers that globally connect patches across the spatial and temporal dimensions. In this paper, we instead advocate an inductive bias of locality in video Transformers, which leads to a better speed-accuracy trade-off compared to previous approaches which compute self-attention globally even with spatial-temporal factorization. The locality of the proposed video architecture is realized by adapting the Swin Transformer designed for the image domain, while continuing to leverage the power of pre-trained image models. Our approach achieves state-of-the-art accuracy on a broad range of video recognition benchmarks, including on action recognition (84.9 top-1 accuracy on Kinetics-400 and 85.9 top-1 accuracy on Kinetics-600 with ~20xless pre-training data and ~3xsmaller model size) and temporal modeling (69.6 top-1 accuracy on Something-Something v2). | https://openaccess.thecvf.com/content/CVPR2022/papers/Liu_Video_Swin_Transformer_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2106.13230 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Video_Swin_Transformer_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Video_Swin_Transformer_CVPR_2022_paper.html | CVPR 2022 | null |
Privacy Preserving Partial Localization | Marcel Geppert, Viktor Larsson, Johannes L. Schönberger, Marc Pollefeys | Recently proposed privacy preserving solutions for cloud-based localization rely on lifting traditional point-based maps to randomized 3D line clouds. While the lifted representation is effective in concealing private information, there are two fundamental limitations. First, without careful construction of the line clouds, the representation is vulnerable to density-based inversion attacks. Secondly, after successful localization, the precise camera orientation and position is revealed to the server. However, in many scenarios, the pose itself might be sensitive information. We propose a principled approach overcoming these limitations, based on two observations. First, a full 6 DoF pose is not always necessary, and in combination with egomotion tracking even a one dimensional localization can reduce uncertainty and correct drift. Secondly, by lifting to parallel planes instead of lines, the map only provides partial constraints on the query pose, preventing the server from knowing the exact query location. If the client requires a full 6 DoF pose, it can be obtained by fusing the result from multiple queries, which can be temporally and spatially disjoint. We demonstrate the practical feasibility of this approach and show a small performance drop compared to both the conventional and privacy preserving approaches. | https://openaccess.thecvf.com/content/CVPR2022/papers/Geppert_Privacy_Preserving_Partial_Localization_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Geppert_Privacy_Preserving_Partial_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Geppert_Privacy_Preserving_Partial_Localization_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Geppert_Privacy_Preserving_Partial_Localization_CVPR_2022_paper.html | CVPR 2022 | null |
Cross-Modal Background Suppression for Audio-Visual Event Localization | Yan Xia, Zhou Zhao | Audiovisual Event (AVE) localization requires the model to jointly localize an event by observing audio and visual information. However, in unconstrained videos, both information types may be inconsistent or suffer from severe background noise. Hence this paper proposes a novel cross-modal background suppression network for AVE task, operating at the time- and event-level, aiming to improve localization performance through suppressing asynchronous audiovisual background frames from the examined events and reducing redundant noise. Specifically, the time-level background suppression scheme forces the audio and visual modality to focus on the related information in the temporal dimension that the opposite modality considers essential, and reduces attention to the segments that the other modal considers as background. The event-level background suppression scheme uses the class activation sequences predicted by audio and visual modalities to control the final event category prediction, which can effectively suppress noise events occurring accidentally in a single modality. Furthermore, we introduce a cross-modal gated attention scheme to extract relevant visual regions from complex scenes exploiting both global visual and audio signals. Extensive experiments show our method outperforms the state-of-the-art methods by a large margin in both supervised and weakly supervised AVE settings. | https://openaccess.thecvf.com/content/CVPR2022/papers/Xia_Cross-Modal_Background_Suppression_for_Audio-Visual_Event_Localization_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Xia_Cross-Modal_Background_Suppression_for_Audio-Visual_Event_Localization_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Xia_Cross-Modal_Background_Suppression_for_Audio-Visual_Event_Localization_CVPR_2022_paper.html | CVPR 2022 | null |
Mutual Quantization for Cross-Modal Search With Noisy Labels | Erkun Yang, Dongren Yao, Tongliang Liu, Cheng Deng | Deep cross-modal hashing has become an essential tool for supervised multimodal search. These models tend to be optimized with large, curated multimodal datasets, where most labels have been manually verified. Unfortunately, in many scenarios, such accurate labeling may not be available. In contrast, datasets with low-quality annotations may be acquired, which inevitably introduce numerous mistakes or label noise and therefore degrade the search performance. To address the challenge, we present a general robust cross-modal hashing framework to correlate distinct modalities and combat noisy labels simultaneously. More specifically, we propose a proxy-based contrastive (PC) loss to mitigate the gap between different modalities and train networks for different modalities jointly with small-loss samples that are selected with the PC loss and a mutual quantization loss. The small-loss sample selection from such joint loss can help choose confident examples to guide the model training, and the mutual quantization loss can maximize the agreement between different modalities and is beneficial to improve the effectiveness of sample selection. Experiments on three widely-used multimodal datasets show that our method significantly outperforms existing state-of-the-art methods. | https://openaccess.thecvf.com/content/CVPR2022/papers/Yang_Mutual_Quantization_for_Cross-Modal_Search_With_Noisy_Labels_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Yang_Mutual_Quantization_for_Cross-Modal_Search_With_Noisy_Labels_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Yang_Mutual_Quantization_for_Cross-Modal_Search_With_Noisy_Labels_CVPR_2022_paper.html | CVPR 2022 | null |
Lagrange Motion Analysis and View Embeddings for Improved Gait Recognition | Tianrui Chai, Annan Li, Shaoxiong Zhang, Zilong Li, Yunhong Wang | Gait is considered the walking pattern of human body, which includes both shape and motion cues. However, the main-stream appearance-based methods for gait recognition rely on the shape of silhouette. It is unclear whether motion can be explicitly represented in the gait sequence modeling. In this paper, we analyzed human walking using the Lagrange's equation and come to the conclusion that second-order information in the temporal dimension is necessary for identification. We designed a second-order motion extraction module based on the conclusions drawn. Also, a light weight view-embedding module is designed by analyzing the problem that current methods to cross-view task do not take view itself into consideration explicitly. Experiments on CASIA-B and OU-MVLP datasets show the effectiveness of our method and some visualization for extracted motion are done to show the interpretability of our motion extraction module. | https://openaccess.thecvf.com/content/CVPR2022/papers/Chai_Lagrange_Motion_Analysis_and_View_Embeddings_for_Improved_Gait_Recognition_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Chai_Lagrange_Motion_Analysis_CVPR_2022_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Chai_Lagrange_Motion_Analysis_and_View_Embeddings_for_Improved_Gait_Recognition_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Chai_Lagrange_Motion_Analysis_and_View_Embeddings_for_Improved_Gait_Recognition_CVPR_2022_paper.html | CVPR 2022 | null |
SphereSR: 360deg Image Super-Resolution With Arbitrary Projection via Continuous Spherical Image Representation | Youngho Yoon, Inchul Chung, Lin Wang, Kuk-Jin Yoon | The 360deg imaging has recently gained much attention; however, its angular resolution is relatively lower than that of a narrow field-of-view (FOV) perspective image as it is captured using a fisheye lens with the same sensor size. Therefore, it is beneficial to super-resolve a 360deg image. Several attempts have been made, but mostly considered equirectangular projection (ERP) as one of the ways for 360deg image representation despite the latitude-dependent distortions. In that case, as the output high-resolution (HR) image is always in the same ERP format as the lowresolution (LR) input, additional information loss may occur when transforming the HR image to other projection types. In this paper, we propose SphereSR, a novel framework to generate a continuous spherical image representation from an LR 360deg image, with the goal of predicting the RGB values at given spherical coordinates for superresolution with an arbitrary 360deg image projection. Specifically, first we propose a feature extraction module that represents the spherical data based on an icosahedron and that efficiently extracts features on the spherical surface. We then propose a spherical local implicit image function (SLIIF) to predict RGB values at the spherical coordinates. As such, SphereSR flexibly reconstructs an HR image given an arbitrary projection type. Experiments on various benchmark datasets show that the proposed method significantly surpasses existing methods in terms of performance. | https://openaccess.thecvf.com/content/CVPR2022/papers/Yoon_SphereSR_360deg_Image_Super-Resolution_With_Arbitrary_Projection_via_Continuous_Spherical_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Yoon_SphereSR_360deg_Image_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Yoon_SphereSR_360deg_Image_Super-Resolution_With_Arbitrary_Projection_via_Continuous_Spherical_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Yoon_SphereSR_360deg_Image_Super-Resolution_With_Arbitrary_Projection_via_Continuous_Spherical_CVPR_2022_paper.html | CVPR 2022 | null |
Neural Mesh Simplification | Rolandos Alexandros Potamias, Stylianos Ploumpis, Stefanos Zafeiriou | Despite the advent in rendering, editing and preprocessing methods of 3D meshes, their real-time execution remains still infeasible for large-scale meshes. To ease and accelerate such processes, mesh simplification methods have been introduced with the aim to reduce the mesh resolution while preserving its appearance. In this work we attempt to tackle the novel task of learnable and differentiable mesh simplification. Compared to traditional simplification approaches that collapse edges in a greedy iterative manner, we propose a fast and scalable method that simplifies a given mesh in one-pass. The proposed method unfolds in three steps. Initially, a subset of the input vertices is sampled using a sophisticated extension of random sampling. Then, we train a sparse attention network to propose candidate triangles based on the edge connectivity of the sampled vertices. Finally, a classification network estimates the probability that a candidate triangle will be included in the final mesh. The fast, lightweight and differentiable properties of the proposed method makes it possible to be plugged in every learnable pipeline without introducing a significant overhead. We evaluate both the sampled vertices and the generated triangles under several appearance error measures and compare its performance against several state-of-the-art baselines. Furthermore, we showcase that the running performance can be up to 10-times faster than traditional methods. | https://openaccess.thecvf.com/content/CVPR2022/papers/Potamias_Neural_Mesh_Simplification_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Potamias_Neural_Mesh_Simplification_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Potamias_Neural_Mesh_Simplification_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Potamias_Neural_Mesh_Simplification_CVPR_2022_paper.html | CVPR 2022 | null |
Cloth-Changing Person Re-Identification From a Single Image With Gait Prediction and Regularization | Xin Jin, Tianyu He, Kecheng Zheng, Zhiheng Yin, Xu Shen, Zhen Huang, Ruoyu Feng, Jianqiang Huang, Zhibo Chen, Xian-Sheng Hua | Cloth-Changing person re-identification (CC-ReID) aims at matching the same person across different locations over a long-duration, e.g., over days, and therefore inevitably has cases of changing clothing. In this paper, we focus on handling well the CC-ReID problem under a more challenging setting, i.e., just from a single image, which enables an efficient and latency-free person identity matching for surveillance. Specifically, we introduce Gait recognition as an auxiliary task to drive the Image ReID model to learn cloth-agnostic representations by leveraging personal unique and cloth-independent gait information, we name this framework as GI-ReID. GI-ReID adopts a two-stream architecture that consists of an image ReID-Stream and an auxiliary gait recognition stream (Gait-Stream). The Gait-Stream, that is discarded in the inference for high efficiency, acts as a regulator to encourage the ReID-Stream to capture cloth-invariant biometric motion features during the training. To get temporal continuous motion cues from a single image, we design a Gait Sequence Prediction (GSP) module for Gait-Stream to enrich gait information. Finally, a semantics consistency constraint over two streams is enforced for effective knowledge regularization. Extensive experiments on multiple image-based Cloth-Changing ReID benchmarks, e.g., LTCC, PRCC, Real28, and VC-Clothes, demonstrate that GI-ReID performs favorably against the state-of-the-art methods. | https://openaccess.thecvf.com/content/CVPR2022/papers/Jin_Cloth-Changing_Person_Re-Identification_From_a_Single_Image_With_Gait_Prediction_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Jin_Cloth-Changing_Person_Re-Identification_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2103.15537 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Jin_Cloth-Changing_Person_Re-Identification_From_a_Single_Image_With_Gait_Prediction_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Jin_Cloth-Changing_Person_Re-Identification_From_a_Single_Image_With_Gait_Prediction_CVPR_2022_paper.html | CVPR 2022 | null |
BoxeR: Box-Attention for 2D and 3D Transformers | Duy-Kien Nguyen, Jihong Ju, Olaf Booij, Martin R. Oswald, Cees G. M. Snoek | In this paper, we propose a simple attention mechanism, we call Box-Attention. It enables spatial interaction between grid features, as sampled from boxes of interest, and improves the learning capability of transformers for several vision tasks. Specifically, we present BoxeR, short for Box Transformer, which attends to a set of boxes by predicting their transformation from a reference window on an input feature map. The BoxeR computes attention weights on these boxes by considering its grid structure. Notably, BoxeR-2D naturally reasons about box information within its attention module, making it suitable for end-to-end instance detection and segmentation tasks. By learning invariance to rotation in the box-attention module, BoxeR-3D is capable of generating discriminative information from a bird's-eye view plane for 3D end-to-end object detection. Our experiments demonstrate that the proposed BoxeR-2D achieves state-of-the-art results on COCO detection and instance segmentation. Besides, BoxeR-3D improves over the end-to-end 3D object detection baseline and already obtains a compelling performance for the vehicle category of Waymo Open, without any class-specific optimization. Code is available at https://github.com/kienduynguyen/BoxeR. | https://openaccess.thecvf.com/content/CVPR2022/papers/Nguyen_BoxeR_Box-Attention_for_2D_and_3D_Transformers_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Nguyen_BoxeR_Box-Attention_for_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2111.13087 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Nguyen_BoxeR_Box-Attention_for_2D_and_3D_Transformers_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Nguyen_BoxeR_Box-Attention_for_2D_and_3D_Transformers_CVPR_2022_paper.html | CVPR 2022 | null |
Neural Architecture Search With Representation Mutual Information | Xiawu Zheng, Xiang Fei, Lei Zhang, Chenglin Wu, Fei Chao, Jianzhuang Liu, Wei Zeng, Yonghong Tian, Rongrong Ji | Performance evaluation strategy is one of the most important factors that determine the effectiveness and efficiency in Neural Architecture Search (NAS). Existing strategies, such as employing standard training or performance predictor, often suffer from high computational complexity and low generality. To address this issue, we propose to rank architectures by Representation Mutual Information (RMI). Specifically, given an arbitrary architecture that has decent accuracy, architectures that have high RMI with it always yield good accuracies. As an accurate performance indicator to facilitate NAS, RMI not only generalizes well to different search spaces, but is also efficient enough to evaluate architectures using only one batch of data. Building upon RMI, we further propose a new search algorithm termed RMI-NAS, facilitating with a theorem to guarantee the global optimal of the searched architecture. In particular, RMI-NAS first randomly samples architectures from the search space, which are then effectively classified as positive or negative samples by RMI. We then use these samples to train a random forest to explore new regions, while keeping track of the distribution of positive architectures. When the sample size is sufficient, the architecture with the largest probability from the aforementioned distribution is selected, which is theoretically proved to be the optimal solution. The architectures searched by our method achieve remarkable top-1 accuracies with the magnitude times faster search process. Besides, RMI-NAS also generalizes to different datasets and search spaces. Our code has been made available at https://git.openi.org.cn/PCL_AutoML/XNAS. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zheng_Neural_Architecture_Search_With_Representation_Mutual_Information_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zheng_Neural_Architecture_Search_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zheng_Neural_Architecture_Search_With_Representation_Mutual_Information_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zheng_Neural_Architecture_Search_With_Representation_Mutual_Information_CVPR_2022_paper.html | CVPR 2022 | null |
Deep Hyperspectral-Depth Reconstruction Using Single Color-Dot Projection | Chunyu Li, Yusuke Monno, Masatoshi Okutomi | Depth reconstruction and hyperspectral reflectance reconstruction are two active research topics in computer vision and image processing. Conventionally, these two topics have been studied separately using independent imaging setups and there is no existing method which can acquire depth and spectral reflectance simultaneously in one shot without using special hardware. In this paper, we propose a novel single-shot hyperspectral-depth reconstruction method using an off-the-shelf RGB camera and projector. Our method is based on a single color-dot projection, which simultaneously acts as structured light for depth reconstruction and spatially-varying color illuminations for hyperspectral reflectance reconstruction. To jointly reconstruct the depth and the hyperspectral reflectance from a single color-dot image, we propose a novel end-to-end network architecture that effectively incorporates a geometric color-dot pattern loss and a photometric hyperspectral reflectance loss. Through the experiments, we demonstrate that our hyperspectral-depth reconstruction method outperforms the combination of an existing state-of-the-art single-shot hyperspectral reflectance reconstruction method and depth reconstruction method. | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_Deep_Hyperspectral-Depth_Reconstruction_Using_Single_Color-Dot_Projection_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Li_Deep_Hyperspectral-Depth_Reconstruction_CVPR_2022_supplemental.zip | http://arxiv.org/abs/2204.03929 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Deep_Hyperspectral-Depth_Reconstruction_Using_Single_Color-Dot_Projection_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Deep_Hyperspectral-Depth_Reconstruction_Using_Single_Color-Dot_Projection_CVPR_2022_paper.html | CVPR 2022 | null |
M3T: Three-Dimensional Medical Image Classifier Using Multi-Plane and Multi-Slice Transformer | Jinseong Jang, Dosik Hwang | In this study, we propose a three-dimensional Medical image classifier using Multi-plane and Multi-slice Transformer (M3T) network to classify Alzheimer's disease (AD) in 3D MRI images. The proposed network synergically combines 3D CNN, 2D CNN, and Transformer for accurate AD classification. The 3D CNN is used to perform natively 3D representation learning, while 2D CNN is used to utilize the pre-trained weights on large 2D databases and 2D representation learning. It is possible to efficiently extract the locality information for AD-related abnormalities in the local brain using CNN networks with inductive bias. The transformer network is also used to obtain attention relationships among multi-plane (axial, coronal, and sagittal) and multi-slice images after CNN. It is also possible to learn the abnormalities distributed over the wider region in the brain using the transformer without inductive bias. In this experiment, we used a training dataset from the Alzheimer's Disease Neuroimaging Initiative (ADNI) which contains a total of 4,786 3D T1-weighted MRI images. For the validation data, we used dataset from three different institutions: The Australian Imaging, Biomarker and Lifestyle Flagship Study of Ageing (AIBL), The Open Access Series of Imaging Studies (OASIS), and some set of ADNI data independent from the training dataset. Our proposed M3T is compared to conventional 3D classification networks based on an area under the curve (AUC) and classification accuracy for AD classification. This study represents that the proposed network M3T achieved the highest performance in multi-institutional validation database, and demonstrates the feasibility of the method to efficiently combine CNN and Transformer for 3D medical images. | https://openaccess.thecvf.com/content/CVPR2022/papers/Jang_M3T_Three-Dimensional_Medical_Image_Classifier_Using_Multi-Plane_and_Multi-Slice_Transformer_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Jang_M3T_Three-Dimensional_Medical_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Jang_M3T_Three-Dimensional_Medical_Image_Classifier_Using_Multi-Plane_and_Multi-Slice_Transformer_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Jang_M3T_Three-Dimensional_Medical_Image_Classifier_Using_Multi-Plane_and_Multi-Slice_Transformer_CVPR_2022_paper.html | CVPR 2022 | null |
3MASSIV: Multilingual, Multimodal and Multi-Aspect Dataset of Social Media Short Videos | Vikram Gupta, Trisha Mittal, Puneet Mathur, Vaibhav Mishra, Mayank Maheshwari, Aniket Bera, Debdoot Mukherjee, Dinesh Manocha | We present 3MASSIV, a multilingual, multimodal and multi-aspect, expertly-annotated dataset of diverse short videos extracted from a social media platform. 3MASSIV comprises of 50k short videos (20 seconds average duration) and 100K unlabeled videos in 11 different languages and captures popular short video trends like pranks, fails, romance, comedy expressed via unique audio-visual formats like self-shot videos, reaction videos, lip-synching, self-sung songs, etc. 3MASSIV presents an opportunity for multimodal and multilingual semantic understanding on these unique videos by annotating them for concepts, affective states, media types, and audio language. We present a thorough analysis of 3MASSIV and highlight the variety and unique aspects of our dataset compared to other contemporary popular datasets with strong baselines. We also show how the social media content in 3MASSIV is dynamic and temporal in nature which can be used for various semantic understanding tasks and cross-lingual analysis. | https://openaccess.thecvf.com/content/CVPR2022/papers/Gupta_3MASSIV_Multilingual_Multimodal_and_Multi-Aspect_Dataset_of_Social_Media_Short_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Gupta_3MASSIV_Multilingual_Multimodal_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.14456 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Gupta_3MASSIV_Multilingual_Multimodal_and_Multi-Aspect_Dataset_of_Social_Media_Short_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Gupta_3MASSIV_Multilingual_Multimodal_and_Multi-Aspect_Dataset_of_Social_Media_Short_CVPR_2022_paper.html | CVPR 2022 | null |
Can Neural Nets Learn the Same Model Twice? Investigating Reproducibility and Double Descent From the Decision Boundary Perspective | Gowthami Somepalli, Liam Fowl, Arpit Bansal, Ping Yeh-Chiang, Yehuda Dar, Richard Baraniuk, Micah Goldblum, Tom Goldstein | We discuss methods for visualizing neural network decision boundaries and decision regions. We use these visualizations to investigate issues related to reproducibility and generalization in neural network training. We observe that changes in model architecture (and its associate inductive bias) cause visible changes in decision boundaries, while multiple runs with the same architecture yield results with strong similarities, especially in the case of wide architectures. We also use decision boundary methods to visualize double descent phenomena. We see that decision boundary reproducibility depends strongly on model width. Near the threshold of interpolation, neural network decision boundaries become fragmented into many small decision regions, and these regions are non-reproducible. Meanwhile, very narrows and very wide networks have high levels of reproducibility in their decision boundaries with relatively few decision regions. We discuss how our observations relate to the theory of double descent phenomena in convex models. Code is available at https://github.com/somepago/dbViz. | https://openaccess.thecvf.com/content/CVPR2022/papers/Somepalli_Can_Neural_Nets_Learn_the_Same_Model_Twice_Investigating_Reproducibility_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Somepalli_Can_Neural_Nets_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.08124 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Somepalli_Can_Neural_Nets_Learn_the_Same_Model_Twice_Investigating_Reproducibility_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Somepalli_Can_Neural_Nets_Learn_the_Same_Model_Twice_Investigating_Reproducibility_CVPR_2022_paper.html | CVPR 2022 | null |
Cross Domain Object Detection by Target-Perceived Dual Branch Distillation | Mengzhe He, Yali Wang, Jiaxi Wu, Yiru Wang, Hanqing Li, Bo Li, Weihao Gan, Wei Wu, Yu Qiao | Cross domain object detection is a realistic and challenging task in the wild. It suffers from performance degradation due to large shift of data distributions and lack of instance-level annotations in the target domain. Existing approaches mainly focus on either of these two difficulties, even though they are closely coupled in cross domain object detection. To solve this problem, we propose a novel Target-perceived Dual-branch Distillation (TDD) framework. By integrating detection branches of both source and target domains in a unified teacher-student learning scheme, it can reduce domain shift and generate reliable supervision effectively. In particular, we first introduce a distinct Target Proposal Perceiver between two domains. It can adaptively enhance source detector to perceive objects in a target image, by leveraging target proposal contexts from iterative cross-attention. Afterwards, we design a concise Dual Branch Self Distillation strategy for model training, which can progressively integrate complementary object knowledge from different domains via self-distillation in two branches. Finally, we conduct extensive experiments on a number of widely-used scenarios in cross domain object detection. The results show that our TDD significantly outperforms the state-of-the-art methods on all the benchmarks. The codes and models will be released afterwards. | https://openaccess.thecvf.com/content/CVPR2022/papers/He_Cross_Domain_Object_Detection_by_Target-Perceived_Dual_Branch_Distillation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/He_Cross_Domain_Object_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2205.01291 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/He_Cross_Domain_Object_Detection_by_Target-Perceived_Dual_Branch_Distillation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/He_Cross_Domain_Object_Detection_by_Target-Perceived_Dual_Branch_Distillation_CVPR_2022_paper.html | CVPR 2022 | null |
A Proposal-Based Paradigm for Self-Supervised Sound Source Localization in Videos | Hanyu Xuan, Zhiliang Wu, Jian Yang, Yan Yan, Xavier Alameda-Pineda | Humans can easily recognize where and how the sound is produced via watching a scene and listening to corresponding audio cues. To achieve such cross-modal perception on machines, existing methods only use the maps generated by interpolation operations to localize the sound source. As semantic object-level localization is more attractive for potential practical applications, we argue that these existing map-based approaches only provide a coarse-grained and indirect description of the sound source. In this paper, we advocate a novel proposal-based paradigm that can directly perform semantic object-level localization, without any manual annotations. We incorporate the global response map as an unsupervised spatial constraint to weight the proposals according to how well they cover the estimated global shape of the sound source. As a result, our proposal-based sound source localization can be cast into a simpler Multiple Instance Learning (MIL) problem by filtering those instances corresponding to large sound-unrelated regions. Our method achieves state-of-the-art (SOTA) performance when compared to several baselines on multiple datasets. | https://openaccess.thecvf.com/content/CVPR2022/papers/Xuan_A_Proposal-Based_Paradigm_for_Self-Supervised_Sound_Source_Localization_in_Videos_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Xuan_A_Proposal-Based_Paradigm_for_Self-Supervised_Sound_Source_Localization_in_Videos_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Xuan_A_Proposal-Based_Paradigm_for_Self-Supervised_Sound_Source_Localization_in_Videos_CVPR_2022_paper.html | CVPR 2022 | null |
Overcoming Catastrophic Forgetting in Incremental Object Detection via Elastic Response Distillation | Tao Feng, Mang Wang, Hangjie Yuan | Traditional object detectors are ill-equipped for incremental learning. However, fine-tuning directly on a well-trained detection model with only new data will lead to catastrophic forgetting. Knowledge distillation is a flexible way to mitigate catastrophic forgetting. In Incremental Object Detection (IOD), previous work mainly focuses on distilling for the combination of features and responses. However, they under-explore the information that contains in responses. In this paper, we propose a response-based incremental distillation method, dubbed Elastic Response Distillation (ERD), which focuses on elastically learning responses from the classification head and the regression head. Firstly, our method transfers category knowledge while equipping student detector with the ability to retain localization information during incremental learning. In addition, we further evaluate the quality of all locations and provide valuable responses by the Elastic Response Selection (ERS) strategy. Finally, we elucidate that the knowledge from different responses should be assigned with different importance during incremental distillation. Extensive experiments conducted on MS COCO demonstrate the proposed method achieves state-of-the-art performance, which substantially narrows the performance gap towards full training. | https://openaccess.thecvf.com/content/CVPR2022/papers/Feng_Overcoming_Catastrophic_Forgetting_in_Incremental_Object_Detection_via_Elastic_Response_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2204.02136 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Feng_Overcoming_Catastrophic_Forgetting_in_Incremental_Object_Detection_via_Elastic_Response_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Feng_Overcoming_Catastrophic_Forgetting_in_Incremental_Object_Detection_via_Elastic_Response_CVPR_2022_paper.html | CVPR 2022 | null |
GroupNet: Multiscale Hypergraph Neural Networks for Trajectory Prediction With Relational Reasoning | Chenxin Xu, Maosen Li, Zhenyang Ni, Ya Zhang, Siheng Chen | Demystifying the interactions among multiple agents from their past trajectories is fundamental to precise and interpretable trajectory prediction. However, previous works only consider pair-wise interactions with limited relational reasoning. To promote more comprehensive interaction modeling for relational reasoning, we propose GroupNet, a multiscale hypergraph neural network, which is novel in terms of both interaction capturing and representation learning. From the aspect of interaction capturing, we propose a trainable multiscale hypergraph to capture both pair-wise and group-wise interactions at multiple group sizes. From the aspect of interaction representation learning, we propose a three-element format that can be learnt end-to-end and explicitly reason some relational factors including the interaction strength and category. We apply GroupNet into both CVAE-based prediction system and previous state-of-the-art prediction systems for predicting socially plausible trajectories with relational reasoning. To validate the ability of relational reasoning, we experiment with synthetic physics simulations to reflect the ability to capture group behaviors, reason interaction strength and interaction category. To validate the effectiveness of prediction, we conduct extensive experiments on three real-world trajectory prediction datasets, including NBA, SDD and ETH-UCY; and we show that with GroupNet, the CVAE-based prediction system outperforms state-of-the-art methods. We also show that adding GroupNet will further improve the performance of previous state-of-the-art prediction systems. | https://openaccess.thecvf.com/content/CVPR2022/papers/Xu_GroupNet_Multiscale_Hypergraph_Neural_Networks_for_Trajectory_Prediction_With_Relational_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Xu_GroupNet_Multiscale_Hypergraph_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.08770 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Xu_GroupNet_Multiscale_Hypergraph_Neural_Networks_for_Trajectory_Prediction_With_Relational_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Xu_GroupNet_Multiscale_Hypergraph_Neural_Networks_for_Trajectory_Prediction_With_Relational_CVPR_2022_paper.html | CVPR 2022 | null |
Unbiased Subclass Regularization for Semi-Supervised Semantic Segmentation | Dayan Guan, Jiaxing Huang, Aoran Xiao, Shijian Lu | Semi-supervised semantic segmentation learns from small amounts of labelled images and large amounts of unlabelled images, which has witnessed impressive progress with the recent advance of deep neural networks. However, it often suffers from severe class-bias problem while exploring the unlabelled images, largely due to the clear pixel-wise class imbalance in the labelled images. This paper presents an unbiased subclass regularization network (USRN) that alleviates the class imbalance issue by learning class-unbiased segmentation from balanced subclass distributions. We build the balanced subclass distributions by clustering pixels of each original class into multiple subclasses of similar sizes, which provide class-balanced pseudo supervision to regularize the class-biased segmentation. In addition, we design an entropy-based gate mechanism to coordinate learning between the original classes and the clustered subclasses which facilitates subclass regularization effectively by suppressing unconfident subclass predictions. Extensive experiments over multiple public benchmarks show that USRN achieves superior performance as compared with the state-of-the-art. The code will be made available on Github. | https://openaccess.thecvf.com/content/CVPR2022/papers/Guan_Unbiased_Subclass_Regularization_for_Semi-Supervised_Semantic_Segmentation_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2203.10026 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Guan_Unbiased_Subclass_Regularization_for_Semi-Supervised_Semantic_Segmentation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Guan_Unbiased_Subclass_Regularization_for_Semi-Supervised_Semantic_Segmentation_CVPR_2022_paper.html | CVPR 2022 | null |
P3IV: Probabilistic Procedure Planning From Instructional Videos With Weak Supervision | He Zhao, Isma Hadji, Nikita Dvornik, Konstantinos G. Derpanis, Richard P. Wildes, Allan D. Jepson | In this paper, we study the problem of procedure planning in instructional videos. Here, an agent must produce a plausible sequence of actions that can transform the environment from a given start to a desired goal state. When learning procedure planning from instructional videos, most recent work leverages intermediate visual observations as supervision, which requires expensive annotation efforts to localize precisely all the instructional steps in training videos. In contrast, we remove the need for expensive temporal video annotations and propose a weakly supervised approach by learning from natural language instructions. Our model is based on a transformer equipped with a memory module, which maps the start and goal observations to a sequence of plausible actions. Furthermore, we augment our model with a probabilistic generative module to capture the uncertainty inherent to procedure planning, an aspect largely overlooked by previous work. We evaluate our model on three datasets and show our weakly-supervised approach outperforms previous fully supervised state-of-the-art models on multiple metrics. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhao_P3IV_Probabilistic_Procedure_Planning_From_Instructional_Videos_With_Weak_Supervision_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhao_P3IV_Probabilistic_Procedure_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2205.02300 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhao_P3IV_Probabilistic_Procedure_Planning_From_Instructional_Videos_With_Weak_Supervision_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhao_P3IV_Probabilistic_Procedure_Planning_From_Instructional_Videos_With_Weak_Supervision_CVPR_2022_paper.html | CVPR 2022 | null |
Hierarchical Nearest Neighbor Graph Embedding for Efficient Dimensionality Reduction | Saquib Sarfraz, Marios Koulakis, Constantin Seibold, Rainer Stiefelhagen | Dimensionality reduction is crucial both for visualization and preprocessing high dimensional data for machine learning. We introduce a novel method based on a hierarchy built on 1-nearest neighbor graphs in the original space which is used to preserve the grouping properties of the data distribution on multiple levels. The core of the proposal is an optimization-free projection that is competitive with the latest versions of t-SNE and UMAP in performance and visualization quality while being an order of magnitude faster at run-time. Furthermore, its interpretable mechanics, the ability to project new data, and the natural separation of data clusters in visualizations make it a general purpose unsupervised dimension reduction technique. In the paper, we argue about the soundness of the proposed method and evaluate it on a diverse collection of datasets with sizes varying from 1K to 11M samples and dimensions from 28 to 16K. We perform comparisons with other state-of-the-art methods on multiple metrics and target dimensions highlighting its efficiency and performance. Code is available at https://github.com/koulakis/h-nne | https://openaccess.thecvf.com/content/CVPR2022/papers/Sarfraz_Hierarchical_Nearest_Neighbor_Graph_Embedding_for_Efficient_Dimensionality_Reduction_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Sarfraz_Hierarchical_Nearest_Neighbor_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.12997 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Sarfraz_Hierarchical_Nearest_Neighbor_Graph_Embedding_for_Efficient_Dimensionality_Reduction_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Sarfraz_Hierarchical_Nearest_Neighbor_Graph_Embedding_for_Efficient_Dimensionality_Reduction_CVPR_2022_paper.html | CVPR 2022 | null |
Coupled Iterative Refinement for 6D Multi-Object Pose Estimation | Lahav Lipson, Zachary Teed, Ankit Goyal, Jia Deng | We address the task of 6D multi-object pose: given a set of known 3D objects and an RGB or RGB-D input image, we detect and estimate the 6D pose of each object. We propose a new approach to 6D object pose estimation which consists of an end-to-end differentiable architecture that makes use of geometric knowledge. Our approach iteratively refines both pose and correspondence in a tightly coupled manner, allowing us to dynamically remove outliers to improve accuracy. We use a novel differentiable layer to perform pose refinement by solving an optimization problem we refer to as Bidirectional Depth-Augmented Perspective-N-Point (BD-PnP). Our method achieves state-of-the-art accuracy on standard 6D Object Pose benchmarks. | https://openaccess.thecvf.com/content/CVPR2022/papers/Lipson_Coupled_Iterative_Refinement_for_6D_Multi-Object_Pose_Estimation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Lipson_Coupled_Iterative_Refinement_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.12516 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Lipson_Coupled_Iterative_Refinement_for_6D_Multi-Object_Pose_Estimation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Lipson_Coupled_Iterative_Refinement_for_6D_Multi-Object_Pose_Estimation_CVPR_2022_paper.html | CVPR 2022 | null |
Multi-View Transformer for 3D Visual Grounding | Shijia Huang, Yilun Chen, Jiaya Jia, Liwei Wang | The 3D visual grounding task aims to ground a natural language description to the targeted object in a 3D scene, which is usually represented in 3D point clouds. Previous works studied visual grounding under specific views. The vision-language correspondence learned by this way can easily fail once the view changes. In this paper, we propose a Multi-View Transformer (MVT) for 3D visual grounding. We project the 3D scene to a multi-view space, in which the position information of the 3D scene under different views are modeled simultaneously and aggregated together. The multi-view space enables the network to learn a more robust multi-modal representation for 3D visual grounding and eliminates the dependence on specific views. Extensive experiments show that our approach significantly outperforms all state-of-the-art methods. Specifically, on Nr3D and Sr3D datasets, our method outperforms the best competitor by 11.2% and 7.1% and even surpasses recent work with extra 2D assistance by 5.9% and 6.6%. Our code is available at https://github.com/sega-hsj/MVT-3DVG. | https://openaccess.thecvf.com/content/CVPR2022/papers/Huang_Multi-View_Transformer_for_3D_Visual_Grounding_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2204.02174 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Huang_Multi-View_Transformer_for_3D_Visual_Grounding_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Huang_Multi-View_Transformer_for_3D_Visual_Grounding_CVPR_2022_paper.html | CVPR 2022 | null |
Structured Sparse R-CNN for Direct Scene Graph Generation | Yao Teng, Limin Wang | Scene graph generation (SGG) is to detect object pairs with their relations in an image. Existing SGG approaches often use multi-stage pipelines to decompose this task into object detection, relation graph construction, and dense or dense-to-sparse relation prediction. Instead, from a perspective on SGG as a direct set prediction, this paper presents a simple, sparse, and unified framework, termed as Structured Sparse R-CNN. The key to our method is a set of learnable triplet queries and a structured triplet detector which could be jointly optimized from the training set in an end-to-end manner. Specifically, the triplet queries encode the general prior for object pairs with their relations, and provide an initial guess of scene graphs for subsequent refinement. The triplet detector presents a cascaded architecture to progressively refine the detected scene graphs with the customized dynamic heads. In addition, to relieve the training difficulty of our method, we propose a relaxed and enhanced training strategy based on knowledge distillation from a Siamese Sparse R-CNN. We perform experiments on several datasets: Visual Genome and Open Images V4/V6, and the results demonstrate that our method achieves the state-of-the-art performance. In addition, we also perform in-depth ablation studies to provide insights on our structured modeling in triplet detector design and training strategies. The code and models are made available at https://github.com/MCG-NJU/Structured-Sparse-RCNN. | https://openaccess.thecvf.com/content/CVPR2022/papers/Teng_Structured_Sparse_R-CNN_for_Direct_Scene_Graph_Generation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Teng_Structured_Sparse_R-CNN_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Teng_Structured_Sparse_R-CNN_for_Direct_Scene_Graph_Generation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Teng_Structured_Sparse_R-CNN_for_Direct_Scene_Graph_Generation_CVPR_2022_paper.html | CVPR 2022 | null |
Multi-Grained Spatio-Temporal Features Perceived Network for Event-Based Lip-Reading | Ganchao Tan, Yang Wang, Han Han, Yang Cao, Feng Wu, Zheng-Jun Zha | Automatic lip-reading (ALR) aims to recognize words using visual information from the speaker's lip movements. In this work, we introduce a novel type of sensing device, event cameras, for the task of ALR. Event cameras have both technical and application advantages over conventional cameras for the ALR task because they have higher temporal resolution, less redundant visual information, and lower power consumption. To recognize words from the event data, we propose a novel Multi-grained Spatio-Temporal Features Perceived Network (MSTP) to perceive fine-grained spatio-temporal features from microsecond time-resolved event data. Specifically, a multi-branch network architecture is designed, in which different grained spatio-temporal features are learned by operating at different frame rates. The branch operating on the low frame rate can perceive spatial complete but temporal coarse features. While the branch operating on the high frame rate can perceive spatial coarse but temporal refinement features. And a message flow module is devised to integrate the features from different branches, leading to perceiving more discriminative spatio-temporal features. In addition, we present the first event-based lip-reading dataset (DVS-Lip) captured by the event camera. Experimental results demonstrated the superiority of the proposed model compared to the state-of-the-art event-based action recognition models and video-based lip-reading models. | https://openaccess.thecvf.com/content/CVPR2022/papers/Tan_Multi-Grained_Spatio-Temporal_Features_Perceived_Network_for_Event-Based_Lip-Reading_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Tan_Multi-Grained_Spatio-Temporal_Features_CVPR_2022_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Tan_Multi-Grained_Spatio-Temporal_Features_Perceived_Network_for_Event-Based_Lip-Reading_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Tan_Multi-Grained_Spatio-Temporal_Features_Perceived_Network_for_Event-Based_Lip-Reading_CVPR_2022_paper.html | CVPR 2022 | null |
Semi-Supervised Video Paragraph Grounding With Contrastive Encoder | Xun Jiang, Xing Xu, Jingran Zhang, Fumin Shen, Zuo Cao, Heng Tao Shen | Video events grounding aims at retrieving the most relevant moments from an untrimmed video in terms of a given natural language query. Most previous works focus on Video Sentence Grounding (VSG), which localizes the moment with a sentence query. Recently, researchers extended this task to Video Paragraph Grounding (VPG) by retrieving multiple events with a paragraph. However, we find the existing VPG methods may not perform well on context modeling and highly rely on video-paragraph annotations. To tackle this problem, we propose a novel VPG method termed Semi-supervised Video-Paragraph TRansformer (SVPTR), which can more effectively exploit contextual information in paragraphs and significantly reduce the dependency on annotated data. Our SVPTR method consists of two key components: (1) a base model VPTR that learns the video-paragraph alignment with contrastive encoders and tackles the lack of sentence-level contextual interactions and (2) a semi-supervised learning framework with multimodal feature perturbations that reduces the requirements of annotated training data. We evaluate our model on three widely-used video grounding datasets, i.e., ActivityNet-Caption, Charades-CD-OOD, and TACoS. The experimental results show that our SVPTR method establishes the new state-of-the-art performance on all datasets. Even under the conditions of fewer annotations, it can also achieve competitive results compared with recent VPG methods. | https://openaccess.thecvf.com/content/CVPR2022/papers/Jiang_Semi-Supervised_Video_Paragraph_Grounding_With_Contrastive_Encoder_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Jiang_Semi-Supervised_Video_Paragraph_Grounding_With_Contrastive_Encoder_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Jiang_Semi-Supervised_Video_Paragraph_Grounding_With_Contrastive_Encoder_CVPR_2022_paper.html | CVPR 2022 | null |
Continual Predictive Learning From Videos | Geng Chen, Wendong Zhang, Han Lu, Siyu Gao, Yunbo Wang, Mingsheng Long, Xiaokang Yang | Predictive learning ideally builds the world model of physical processes in one or more given environments. Typical setups assume that we can collect data from all environments at all times. In practice, however, different prediction tasks may arrive sequentially so that the environments may change persistently throughout the training procedure. Can we develop predictive learning algorithms that can deal with more realistic, non-stationary physical environments? In this paper, we study a new continual learning problem in the context of video prediction, and observe that most existing methods suffer from severe catastrophic forgetting in this setup. To tackle this problem, we propose the continual predictive learning (CPL) approach, which learns a mixture world model via predictive experience replay and performs test-time adaptation with non-parametric task inference. We construct two new benchmarks based on RoboNet and KTH, in which different tasks correspond to different physical robotic environments or human actions. Our approach is shown to effectively mitigate forgetting and remarkably outperform the naive combinations of previous art in video prediction and continual learning. | https://openaccess.thecvf.com/content/CVPR2022/papers/Chen_Continual_Predictive_Learning_From_Videos_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Chen_Continual_Predictive_Learning_CVPR_2022_supplemental.zip | http://arxiv.org/abs/2204.05624 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_Continual_Predictive_Learning_From_Videos_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_Continual_Predictive_Learning_From_Videos_CVPR_2022_paper.html | CVPR 2022 | null |
Weakly Paired Associative Learning for Sound and Image Representations via Bimodal Associative Memory | Sangmin Lee, Hyung-Il Kim, Yong Man Ro | Data representation learning without labels has attracted increasing attention due to its nature that does not require human annotation. Recently, representation learning has been extended to bimodal data, especially sound and image which are closely related to basic human senses. Existing sound and image representation learning methods necessarily require a large number of sound and image with corresponding pairs. Therefore, it is difficult to ensure the effectiveness of the methods in the weakly paired condition, which lacks paired bimodal data. In fact, according to human cognitive studies, the cognitive functions in the human brain for a certain modality can be enhanced by receiving other modalities, even not directly paired ones. Based on the observation, we propose a new problem to deal with the weakly paired condition: How to boost a certain modal representation even by using other unpaired modal data. To address the issue, we introduce a novel bimodal associative memory (BMA-Memory) with key-value switching. It enables to build sound-image association with small paired bimodal data and to boost the built association with the easily obtainable large amount of unpaired data. Through the proposed associative learning, it is possible to reinforce the representation of a certain modality (e.g., sound) even by using other unpaired modal data (e.g., images). | https://openaccess.thecvf.com/content/CVPR2022/papers/Lee_Weakly_Paired_Associative_Learning_for_Sound_and_Image_Representations_via_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Lee_Weakly_Paired_Associative_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Lee_Weakly_Paired_Associative_Learning_for_Sound_and_Image_Representations_via_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Lee_Weakly_Paired_Associative_Learning_for_Sound_and_Image_Representations_via_CVPR_2022_paper.html | CVPR 2022 | null |
BARC: Learning To Regress 3D Dog Shape From Images by Exploiting Breed Information | Nadine Rüegg, Silvia Zuffi, Konrad Schindler, Michael J. Black | Our goal is to recover the 3D shape and pose of dogs from a single image. This is a challenging task because dogs exhibit a wide range of shapes and appearances, and are highly articulated. Recent work has proposed to directly regress the SMAL animal model, with additional limb scale parameters, from images. Our method, called BARC (Breed-Augmented Regression using Classification), goes beyond prior work in several important ways. First, we modify the SMAL shape space to be more appropriate for representing dog shape. But, even with a better shape model, the problem of regressing dog shape from an image is still challenging because we lack paired images with 3D ground truth. To compensate for the lack of paired data, we formulate novel losses that exploit information about dog breeds. In particular, we exploit the fact that dogs of the same breed have similar body shapes. We formulate a novel breed similarity loss consisting of two parts: One term encourages the shape of dogs from the same breed to be more similar than dogs of different breeds. The second one, a breed classification loss, helps to produce recognizable breed-specific shapes. Through ablation studies, we find that our breed losses significantly improve shape accuracy over a baseline without them. We also compare BARC qualitatively to WLDO with a perceptual study and find that our approach produces dogs that are significantly more realistic. This work shows that a-priori information about genetic similarity can help to compensate for the lack of 3D training data. This concept may be applicable to other animal species or groups of species. Our code is publicly available for research purposes at https://barc.is.tue.mpg.de/. | https://openaccess.thecvf.com/content/CVPR2022/papers/Ruegg_BARC_Learning_To_Regress_3D_Dog_Shape_From_Images_by_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Ruegg_BARC_Learning_To_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Ruegg_BARC_Learning_To_Regress_3D_Dog_Shape_From_Images_by_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Ruegg_BARC_Learning_To_Regress_3D_Dog_Shape_From_Images_by_CVPR_2022_paper.html | CVPR 2022 | null |
Knowledge Distillation: A Good Teacher Is Patient and Consistent | Lucas Beyer, Xiaohua Zhai, Amélie Royer, Larisa Markeeva, Rohan Anil, Alexander Kolesnikov | There is a growing discrepancy in computer vision between large-scale models that achieve state-of-the-art performance and models that are affordable in practical applications. In this paper we address this issue and significantly bridge the gap between these two types of models. Throughout our empirical investigation we do not aim to necessarily propose a new method, but strive to identify a robust and effective recipe for making state-of-the-art large scale models affordable in practice. We demonstrate that, when performed correctly, knowledge distillation can be a powerful tool for reducing the size of large models without compromising their performance. In particular, we uncover that there are certain implicit design choices, which may drastically affect the effectiveness of distillation. Our key contribution is the explicit identification of these design choices, which were not previously articulated in the literature. We back up our findings by a comprehensive empirical study, demonstrate compelling results on a wide range of vision datasets and, in particular, obtain a state-of-the-art ResNet-50 model for ImageNet, which achieves 82.8% top-1 accuracy. | https://openaccess.thecvf.com/content/CVPR2022/papers/Beyer_Knowledge_Distillation_A_Good_Teacher_Is_Patient_and_Consistent_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Beyer_Knowledge_Distillation_A_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2106.05237 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Beyer_Knowledge_Distillation_A_Good_Teacher_Is_Patient_and_Consistent_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Beyer_Knowledge_Distillation_A_Good_Teacher_Is_Patient_and_Consistent_CVPR_2022_paper.html | CVPR 2022 | null |
PCA-Based Knowledge Distillation Towards Lightweight and Content-Style Balanced Photorealistic Style Transfer Models | Tai-Yin Chiu, Danna Gurari | Photorealistic style transfer entails transferring the style of a reference image to another image so the result seems like a plausible photo. Our work is inspired by the observation that existing models are slow due to their large sizes. We introduce PCA-based knowledge distillation to distill lightweight models and show it is motivated by theory. To our knowledge, this is the first knowledge distillation method for photorealistic style transfer. Our experiments demonstrate its versatility for use with different backbone architectures, VGG and MobileNet, across six image resolutions. Compared to existing models, our top-performing model runs at speeds 5-20x faster using at most 1% of the parameters. Additionally, our distilled models achieve a better balance between stylization strength and content preservation than existing models. To support reproducing our method and models, we share the code at https://github.com/chiutaiyin/PCA-Knowledge-Distillation. | https://openaccess.thecvf.com/content/CVPR2022/papers/Chiu_PCA-Based_Knowledge_Distillation_Towards_Lightweight_and_Content-Style_Balanced_Photorealistic_Style_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Chiu_PCA-Based_Knowledge_Distillation_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.13452 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Chiu_PCA-Based_Knowledge_Distillation_Towards_Lightweight_and_Content-Style_Balanced_Photorealistic_Style_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Chiu_PCA-Based_Knowledge_Distillation_Towards_Lightweight_and_Content-Style_Balanced_Photorealistic_Style_CVPR_2022_paper.html | CVPR 2022 | null |
Frame Averaging for Equivariant Shape Space Learning | Matan Atzmon, Koki Nagano, Sanja Fidler, Sameh Khamis, Yaron Lipman | The task of shape space learning involves mapping a train set of shapes to and from a latent representation space with good generalization properties. Often, real-world collections of shapes have symmetries, which can be defined as transformations that do not change the essence of the shape. A natural way to incorporate symmetries in shape space learning is to ask that the mapping to the shape space (encoder) and mapping from the shape space (decoder) are equivariant to the relevant symmetries. In this paper, we present a framework for incorporating equivariance in encoders and decoders by introducing two contributions: (i) adapting the recent Frame Averaging (FA) framework for building generic, efficient, and maximally expressive Equivariant autoencoders; and (ii) constructing autoencoders equivariant to piecewise Euclidean motions applied to different parts of the shape. To the best of our knowledge, this is the first fully piecewise Euclidean equivariant autoencoder construction. Training our framework is simple: it uses standard reconstruction losses, and does not require the introduction of new losses. Our architectures are built of standard (backbone) architectures with the appropriate frame averaging to make them equivariant. Testing our framework on both rigid shapes dataset using implicit neural representations, and articulated shape datasets using mesh-based neural networks show state-of-the-art generalization to unseen test shapes, improving relevant baselines by a large margin. In particular, our method demonstrates significant improvement in generalizing to unseen articulated poses. | https://openaccess.thecvf.com/content/CVPR2022/papers/Atzmon_Frame_Averaging_for_Equivariant_Shape_Space_Learning_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Atzmon_Frame_Averaging_for_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.01741 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Atzmon_Frame_Averaging_for_Equivariant_Shape_Space_Learning_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Atzmon_Frame_Averaging_for_Equivariant_Shape_Space_Learning_CVPR_2022_paper.html | CVPR 2022 | null |
Transformer Tracking With Cyclic Shifting Window Attention | Zikai Song, Junqing Yu, Yi-Ping Phoebe Chen, Wei Yang | Transformer architecture has been showing its great strength in visual object tracking, for its effective attention mechanism. Existing transformer-based approaches adopt the pixel-to-pixel attention strategy on flattened image features and unavoidably ignore the integrity of objects. In this paper, we propose a new transformer architecture with multi-scale cyclic shifting window attention for visual object tracking, elevating the attention from pixel to window level. The cross-window multi-scale attention has the advantage of aggregating attention at different scales and generates the best fine-scale match for the target object. Furthermore, the cyclic shifting strategy brings greater accuracy by expanding the window samples with positional information, and at the same time saves huge amounts of computational power by removing redundant calculations. Extensive experiments demonstrate the superior performance of our method, which also sets the new state-of-the-art records on five challenging datasets, along with the VOT2020, UAV123, LaSOT, TrackingNet, and GOT-10k benchmarks. | https://openaccess.thecvf.com/content/CVPR2022/papers/Song_Transformer_Tracking_With_Cyclic_Shifting_Window_Attention_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Song_Transformer_Tracking_With_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2205.03806 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Song_Transformer_Tracking_With_Cyclic_Shifting_Window_Attention_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Song_Transformer_Tracking_With_Cyclic_Shifting_Window_Attention_CVPR_2022_paper.html | CVPR 2022 | null |
ProposalCLIP: Unsupervised Open-Category Object Proposal Generation via Exploiting CLIP Cues | Hengcan Shi, Munawar Hayat, Yicheng Wu, Jianfei Cai | Object proposal generation is an important and fundamental task in computer vision. In this paper, we propose ProposalCLIP, a method towards unsupervised open-category object proposal generation. Unlike previous works which require a large number of bounding box annotations and/or can only generate proposals for limited object categories, our ProposalCLIP is able to predict proposals for a large variety of object categories without annotations, by exploiting CLIP (contrastive language-image pre-training) cues. Firstly, we analyze CLIP for unsupervised open-category proposal generation and design an objectness score based on our empirical analysis on proposal selection. Secondly, a graph-based merging module is proposed to solve the limitations of CLIP cues and merge fragmented proposals. Finally, we present a proposal regression module that extracts pseudo labels based on CLIP cues and trains a lightweight network to further refine proposals. Extensive experiments on PASCAL VOC, COCO and Visual Genome datasets show that our ProposalCLIP can better generate proposals than previous state-of-the-art methods. Our ProposalCLIP also shows benefits for downstream tasks, such as unsupervised object detection. | https://openaccess.thecvf.com/content/CVPR2022/papers/Shi_ProposalCLIP_Unsupervised_Open-Category_Object_Proposal_Generation_via_Exploiting_CLIP_Cues_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2201.06696 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Shi_ProposalCLIP_Unsupervised_Open-Category_Object_Proposal_Generation_via_Exploiting_CLIP_Cues_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Shi_ProposalCLIP_Unsupervised_Open-Category_Object_Proposal_Generation_via_Exploiting_CLIP_Cues_CVPR_2022_paper.html | CVPR 2022 | null |
Towards Understanding Adversarial Robustness of Optical Flow Networks | Simon Schrodi, Tonmoy Saikia, Thomas Brox | Recent work demonstrated the lack of robustness of optical flow networks to physical patch-based adversarial attacks. The possibility to physically attack a basic component of automotive systems is a reason for serious concerns. In this paper, we analyze the cause of the problem and show that the lack of robustness is rooted in the classical aperture problem of optical flow estimation in combination with bad choices in the details of the network architecture. We show how these mistakes can be rectified in order to make optical flow networks robust to physical patch-based attacks. Additionally, we take a look at global white-box attacks in the scope of optical flow. We find that targeted white-box attacks can be crafted to bias flow estimation models towards any desired output, but this requires access to the input images and model weights. However, in the case of universal attacks, we find that optical flow networks are robust. Code is available at https://github.com/lmb-freiburg/understanding_flow_robustness. | https://openaccess.thecvf.com/content/CVPR2022/papers/Schrodi_Towards_Understanding_Adversarial_Robustness_of_Optical_Flow_Networks_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Schrodi_Towards_Understanding_Adversarial_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2103.16255 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Schrodi_Towards_Understanding_Adversarial_Robustness_of_Optical_Flow_Networks_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Schrodi_Towards_Understanding_Adversarial_Robustness_of_Optical_Flow_Networks_CVPR_2022_paper.html | CVPR 2022 | null |
Panoptic SegFormer: Delving Deeper Into Panoptic Segmentation With Transformers | Zhiqi Li, Wenhai Wang, Enze Xie, Zhiding Yu, Anima Anandkumar, Jose M. Alvarez, Ping Luo, Tong Lu | Panoptic segmentation involves a combination of joint semantic segmentation and instance segmentation, where image contents are divided into two types: things and stuff. We present Panoptic SegFormer, a general framework for panoptic segmentation with transformers. It contains three innovative components: an efficient deeply-supervised mask decoder, a query decoupling strategy, and an improved post-processing method. We also use Deformable DETR to efficiently process multi-scale features, which is a fast and efficient version of DETR. Specifically, we supervise the attention modules in the mask decoder in a layer-wise manner. This deep supervision strategy lets the attention modules quickly focus on meaningful semantic regions. It improves performance and reduces the number of required training epochs by half compared to Deformable DETR. Our query decoupling strategy decouples the responsibilities of the query set and avoids mutual interference between things and stuff. In addition, our post-processing strategy improves performance without additional costs by jointly considering classification and segmentation qualities to resolve conflicting mask overlaps. Our approach increases the accuracy 6.2% PQ over the baseline DETR model. Panoptic SegFormer achieves state-of-the-art results on COCO test-dev with 56.2% PQ. It also shows stronger zero-shot robustness over existing methods. | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_Panoptic_SegFormer_Delving_Deeper_Into_Panoptic_Segmentation_With_Transformers_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Li_Panoptic_SegFormer_Delving_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2109.03814 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Panoptic_SegFormer_Delving_Deeper_Into_Panoptic_Segmentation_With_Transformers_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Panoptic_SegFormer_Delving_Deeper_Into_Panoptic_Segmentation_With_Transformers_CVPR_2022_paper.html | CVPR 2022 | null |
Training High-Performance Low-Latency Spiking Neural Networks by Differentiation on Spike Representation | Qingyan Meng, Mingqing Xiao, Shen Yan, Yisen Wang, Zhouchen Lin, Zhi-Quan Luo | Spiking Neural Network (SNN) is a promising energy-efficient AI model when implemented on neuromorphic hardware. However, it is a challenge to efficiently train SNNs due to their non-differentiability. Most existing methods either suffer from high latency (i.e., long simulation time steps), or cannot achieve as high performance as Artificial Neural Networks (ANNs). In this paper, we propose the Differentiation on Spike Representation (DSR) method, which could achieve high performance that is competitive to ANNs yet with low latency. First, we encode the spike trains into spike representation using (weighted) firing rate coding. Based on the spike representation, we systematically derive that the spiking dynamics with common neural models can be represented as some sub-differentiable mapping. With this viewpoint, our proposed DSR method trains SNNs through gradients of the mapping and avoids the common non-differentiability problem in SNN training. Then we analyze the error when representing the specific mapping with the forward computation of the SNN. To reduce such error, we propose to train the spike threshold in each layer, and to introduce a new hyperparameter for the neural models. With these components, the DSR method can achieve state-of-the-art SNN performance with low latency on both static and neuromorphic datasets, including CIFAR-10, CIFAR-100, ImageNet, and DVS-CIFAR10. | https://openaccess.thecvf.com/content/CVPR2022/papers/Meng_Training_High-Performance_Low-Latency_Spiking_Neural_Networks_by_Differentiation_on_Spike_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Meng_Training_High-Performance_Low-Latency_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2205.00459 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Meng_Training_High-Performance_Low-Latency_Spiking_Neural_Networks_by_Differentiation_on_Spike_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Meng_Training_High-Performance_Low-Latency_Spiking_Neural_Networks_by_Differentiation_on_Spike_CVPR_2022_paper.html | CVPR 2022 | null |
AnyFace: Free-Style Text-To-Face Synthesis and Manipulation | Jianxin Sun, Qiyao Deng, Qi Li, Muyi Sun, Min Ren, Zhenan Sun | Existing text-to-image synthesis methods generally are only applicable to words in the training dataset. However, human faces are so variable to be described with limited words. So this paper proposes the first free-style text-to-face method namely AnyFace enabling much wider open world applications such as metaverse, social media, cosmetics, forensics, etc. AnyFace has a novel two-stream framework for face image synthesis and manipulation given arbitrary descriptions of the human face. Specifically, one stream performs text-to-face generation and the other conducts face image reconstruction. Facial text and image features are extracted using the CLIP (Contrastive Language-Image Pre-training) encoders. And a collaborative Cross Modal Distillation (CMD) module is designed to align the linguistic and visual features across these two streams. Furthermore, a Diverse Triplet Loss (DT loss) is developed to model fine-grained features and improve facial diversity. Extensive experiments on Multi-modal CelebA-HQ and CelebAText-HQ demonstrate significant advantages of AnyFace over state-of-the-art methods. AnyFace can achieve high-quality, high-resolution, and high-diversity face synthesis and manipulation results without any constraints on the number and content of input captions. | https://openaccess.thecvf.com/content/CVPR2022/papers/Sun_AnyFace_Free-Style_Text-To-Face_Synthesis_and_Manipulation_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2203.15334 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Sun_AnyFace_Free-Style_Text-To-Face_Synthesis_and_Manipulation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Sun_AnyFace_Free-Style_Text-To-Face_Synthesis_and_Manipulation_CVPR_2022_paper.html | CVPR 2022 | null |
HL-Net: Heterophily Learning Network for Scene Graph Generation | Xin Lin, Changxing Ding, Yibing Zhan, Zijian Li, Dacheng Tao | Scene graph generation (SGG) aims to detect objects and predict their pairwise relationships within an image. Current SGG methods typically utilize graph neural networks (GNNs) to acquire context information between objects/relationships. Despite their effectiveness, however, current SGG methods only assume scene graph homophily while ignoring heterophily. Accordingly, in this paper, we propose a novel Heterophily Learning Network (HL-Net) to comprehensively explore the homophily and heterophily between objects/relationships in scene graphs. More specifically, HL-Net comprises the following 1) an adaptive reweighting transformer module, which adaptively integrates the information from different layers to exploit both the heterophily and homophily in objects; 2) a relationship feature propagation module that efficiently explores the connections between relationships by considering heterophily in order to refine the relationship representation; 3) a heterophily-aware message-passing scheme to further distinguish the heterophily and homophily between objects/relationships, thereby facilitating improved message passing in graphs. We conducted extensive experiments on two public datasets: Visual Genome (VG) and Open Images (OI). The experimental results demonstrate the superiority of our proposed HL-Net over existing state-of-the-art approaches. In more detail, HL-Net outperforms the second-best competitors by 2.1% on the VG dataset for scene graph classification and 1.2% on the IO dataset for the final score. | https://openaccess.thecvf.com/content/CVPR2022/papers/Lin_HL-Net_Heterophily_Learning_Network_for_Scene_Graph_Generation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Lin_HL-Net_Heterophily_Learning_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Lin_HL-Net_Heterophily_Learning_Network_for_Scene_Graph_Generation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Lin_HL-Net_Heterophily_Learning_Network_for_Scene_Graph_Generation_CVPR_2022_paper.html | CVPR 2022 | null |
Lifelong Graph Learning | Chen Wang, Yuheng Qiu, Dasong Gao, Sebastian Scherer | Graph neural networks (GNN) are powerful models for many graph-structured tasks. Existing models often assume that the complete structure of the graph is available during training. In practice, however, graph-structured data is usually formed in a streaming fashion so that learning a graph continuously is often necessary. In this paper, we bridge GNN and lifelong learning by converting a continual graph learning problem to a regular graph learning problem so GNN can inherit the lifelong learning techniques developed for convolutional neural networks (CNN). We propose a new topology, the feature graph, which takes features as new nodes and turns nodes into independent graphs. This successfully converts the original problem of node classification to graph classification. In the experiments, we demonstrate the efficiency and effectiveness of feature graph networks (FGN) by continuously learning a sequence of classical graph datasets. We also show that FGN achieves superior performance in two applications, i.e., lifelong human action recognition with wearable devices and feature matching. To the best of our knowledge, FGN is the first method to bridge graph learning and lifelong learning via a novel graph topology. Source code is available at https://github.com/wang-chen/LGL | https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_Lifelong_Graph_Learning_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wang_Lifelong_Graph_Learning_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2009.00647 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Lifelong_Graph_Learning_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Lifelong_Graph_Learning_CVPR_2022_paper.html | CVPR 2022 | null |
Hypergraph-Induced Semantic Tuplet Loss for Deep Metric Learning | Jongin Lim, Sangdoo Yun, Seulki Park, Jin Young Choi | In this paper, we propose Hypergraph-Induced Semantic Tuplet (HIST) loss for deep metric learning that leverages the multilateral semantic relations of multiple samples to multiple classes via hypergraph modeling. We formulate deep metric learning as a hypergraph node classification problem in which each sample in a mini-batch is regarded as a node and each hyperedge models class-specific semantic relations represented by a semantic tuplet. Unlike previous graph-based losses that only use a bundle of pairwise relations, our HIST loss takes advantage of the multilateral semantic relations provided by the semantic tuplets through hypergraph modeling. Notably, by leveraging the rich multilateral semantic relations, HIST loss guides the embedding model to learn class-discriminative visual semantics, contributing to better generalization performance and model robustness against input corruptions. Extensive experiments and ablations provide a strong motivation for the proposed method and show that our HIST loss leads to improved feature learning, achieving state-of-the-art results on three widely used benchmarks. Code is available at https://github.com/ljin0429/HIST. | https://openaccess.thecvf.com/content/CVPR2022/papers/Lim_Hypergraph-Induced_Semantic_Tuplet_Loss_for_Deep_Metric_Learning_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Lim_Hypergraph-Induced_Semantic_Tuplet_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Lim_Hypergraph-Induced_Semantic_Tuplet_Loss_for_Deep_Metric_Learning_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Lim_Hypergraph-Induced_Semantic_Tuplet_Loss_for_Deep_Metric_Learning_CVPR_2022_paper.html | CVPR 2022 | null |
Computing Wasserstein-p Distance Between Images With Linear Cost | Yidong Chen, Chen Li, Zhonghua Lu | When the images are formulated as discrete measures, computing Wasserstein-p distance between them is challenging due to the complexity of solving the corresponding Kantorovich's problem. In this paper, we propose a novel algorithm to compute the Wasserstein-p distance between discrete measures by restricting the optimal transport (OT) problem on a subset. First, we define the restricted OT problem and prove the solution of the restricted problem converges to antorovich's OT solution. Second, we propose the SparseSinkhorn algorithm for the restricted problem and provide a multi-scale algorithm to estimate the subset. Finally, we implement the proposed algorithm on CUDA and illustrate the linear computational cost in terms of time and memory requirements. We compute Wasserstein-p distance, estimate the transport mapping, and transfer color between color images with size ranges from 64x64 to 1920x1200. (Our code is available at https://github.com/ucascnic/CudaOT) | https://openaccess.thecvf.com/content/CVPR2022/papers/Chen_Computing_Wasserstein-p_Distance_Between_Images_With_Linear_Cost_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Chen_Computing_Wasserstein-p_Distance_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_Computing_Wasserstein-p_Distance_Between_Images_With_Linear_Cost_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_Computing_Wasserstein-p_Distance_Between_Images_With_Linear_Cost_CVPR_2022_paper.html | CVPR 2022 | null |
DLFormer: Discrete Latent Transformer for Video Inpainting | Jingjing Ren, Qingqing Zheng, Yuanyuan Zhao, Xuemiao Xu, Chen Li | Video inpainting remains a challenging problem to fill with plausible and coherent content in unknown areas in video frames despite the prevalence of data-driven methods. Although various transformer-based architectures yield promising result for this task, they still suffer from hallucinating blurry contents and long-term spatial-temporal inconsistency. While noticing the capability of discrete representation for complex reasoning and predictive learning, we propose a novel Discrete Latent Transformer (DLFormer) to reformulate video inpainting tasks into the discrete latent space rather the previous continuous feature space. Specifically, we first learn a unique compact discrete codebook and the corresponding autoencoder to represent the target video. Built upon these representative discrete codes obtained from the entire target video, the subsequent discrete latent transformer is capable to infer proper codes for unknown areas under a self-attention mechanism, and thus produces fine-grained content with long-term spatial-temporal consistency. Moreover, we further explicitly enforce the short-term consistency to relieve temporal visual jitters via a temporal aggregation block among adjacent frames. We conduct comprehensive quantitative and qualitative evaluations to demonstrate that our method significantly outperforms other state-of-the-art approaches in reconstructing visually-plausible and spatial-temporal coherent content with fine-grained details | https://openaccess.thecvf.com/content/CVPR2022/papers/Ren_DLFormer_Discrete_Latent_Transformer_for_Video_Inpainting_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Ren_DLFormer_Discrete_Latent_Transformer_for_Video_Inpainting_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Ren_DLFormer_Discrete_Latent_Transformer_for_Video_Inpainting_CVPR_2022_paper.html | CVPR 2022 | null |
Unsupervised Representation Learning for Binary Networks by Joint Classifier Learning | Dahyun Kim, Jonghyun Choi | Self-supervised learning is a promising unsupervised learning framework that has achieved success with large floating point networks. But such networks are not readily deployable to edge devices. To accelerate deployment of models with the benefit of unsupervised representation learning to such resource limited devices for various downstream tasks, we propose a self-supervised learning method for binary networks that uses a moving target network. In particular, we propose to jointly train a randomly initialized classifier, attached to a pretrained floating point feature extractor, with a binary network. Additionally, we propose a feature similarity loss, a dynamic loss balancing and modified multi-stage training to further improve the accuracy, and call our method BURN. Our empirical validations over five downstream tasks using seven datasets show that BURN outperforms self-supervised baselines for binary networks and sometimes outperforms supervised pretraining. Code is availabe at https://github.com/naver-ai/burn. | https://openaccess.thecvf.com/content/CVPR2022/papers/Kim_Unsupervised_Representation_Learning_for_Binary_Networks_by_Joint_Classifier_Learning_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Kim_Unsupervised_Representation_Learning_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2110.08851 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Kim_Unsupervised_Representation_Learning_for_Binary_Networks_by_Joint_Classifier_Learning_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Kim_Unsupervised_Representation_Learning_for_Binary_Networks_by_Joint_Classifier_Learning_CVPR_2022_paper.html | CVPR 2022 | null |
High Quality Segmentation for Ultra High-Resolution Images | Tiancheng Shen, Yuechen Zhang, Lu Qi, Jason Kuen, Xingyu Xie, Jianlong Wu, Zhe Lin, Jiaya Jia | To segment 4K or 6K ultra high-resolution images needs extra computation consideration in image segmentation. Common strategies, such as down-sampling, patch cropping, and cascade model, cannot address well the balance issue between accuracy and computation cost. Motivated by the fact that humans distinguish among objects continuously from coarse to precise levels, we propose the Continuous Refinement Model(CRM) for the ultra high-resolution segmentation refinement task. CRM continuously aligns the feature map with the refinement target and aggregates features to reconstruct these images' details. Besides, our CRM shows its significant generalization ability to fill the resolution gap between low-resolution training images and ultra high-resolution testing ones. We present quantitative performance evaluation and visualization to show that our proposed method is fast and effective on image segmentation refinement. | https://openaccess.thecvf.com/content/CVPR2022/papers/Shen_High_Quality_Segmentation_for_Ultra_High-Resolution_Images_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Shen_High_Quality_Segmentation_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2111.14482 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Shen_High_Quality_Segmentation_for_Ultra_High-Resolution_Images_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Shen_High_Quality_Segmentation_for_Ultra_High-Resolution_Images_CVPR_2022_paper.html | CVPR 2022 | null |
Investigating Tradeoffs in Real-World Video Super-Resolution | Kelvin C.K. Chan, Shangchen Zhou, Xiangyu Xu, Chen Change Loy | The diversity and complexity of degradations in real-world video super-resolution (VSR) pose non-trivial challenges in inference and training. First, while long-term propagation leads to improved performance in cases of mild degradations, severe in-the-wild degradations could be exaggerated through propagation, impairing output quality. To balance the tradeoff between detail synthesis and artifact suppression, we found an image pre-cleaning stage indispensable to reduce noises and artifacts prior to propagation. Equipped with a carefully designed cleaning module, our RealBasicVSR outperforms existing methods in both quality and efficiency. Second, real-world VSR models are often trained with diverse degradations to improve generalizability, requiring increased batch size to produce a stable gradient. Inevitably, the increased computational burden results in various problems, including 1) speed-performance tradeoff and 2) batch-length tradeoff. To alleviate the first tradeoff, we propose a stochastic degradation scheme that reduces up to 40% of training time without sacrificing performance. We then analyze different training settings and suggest that employing longer sequences rather than larger batches during training allows more effective uses of temporal information, leading to more stable performance during inference. To facilitate fair comparisons, we propose the new VideoLQ dataset, which contains a large variety of real-world low-quality video sequences containing rich textures and patterns. Our dataset can serve as a common ground for benchmarking. Code, models, and the dataset will be made publicly available. | https://openaccess.thecvf.com/content/CVPR2022/papers/Chan_Investigating_Tradeoffs_in_Real-World_Video_Super-Resolution_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Chan_Investigating_Tradeoffs_in_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2111.12704 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Chan_Investigating_Tradeoffs_in_Real-World_Video_Super-Resolution_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Chan_Investigating_Tradeoffs_in_Real-World_Video_Super-Resolution_CVPR_2022_paper.html | CVPR 2022 | null |
MERLOT Reserve: Neural Script Knowledge Through Vision and Language and Sound | Rowan Zellers, Jiasen Lu, Ximing Lu, Youngjae Yu, Yanpeng Zhao, Mohammadreza Salehi, Aditya Kusupati, Jack Hessel, Ali Farhadi, Yejin Choi | As humans, we navigate a multimodal world, building a holistic understanding from all our senses. We introduce MERLOT Reserve, a model that represents videos jointly over time -- through a new training objective that learns from audio, subtitles, and video frames. Given a video, we replace snippets of text and audio with a MASK token; the model learns by choosing the correct masked-out snippet. Our objective learns faster than alternatives, and performs well at scale: we pretrain on 20 million YouTube videos. Empirical results show that MERLOT Reserve learns strong multimodal representations. When finetuned, it sets state-of-the-art on Visual Commonsense Reasoning (VCR), TVQA, and Kinetics-600; outperforming prior work by 5%, 7%, and 1.5% respectively. Ablations show that these tasks benefit from audio pretraining -- even VCR, a QA task centered around images (without sound). Moreover, our objective enables out-of-the-box prediction, revealing strong multimodal commonsense understanding. In a fully zero-shot setting, our model obtains competitive results on four video tasks, even outperforming supervised approaches on the recently proposed Situated Reasoning (STAR) benchmark. We analyze why audio enables better vision-language representations, suggesting significant opportunities for future research. We conclude by discussing ethical and societal implications of multimodal pretraining. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zellers_MERLOT_Reserve_Neural_Script_Knowledge_Through_Vision_and_Language_and_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zellers_MERLOT_Reserve_Neural_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2201.02639 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zellers_MERLOT_Reserve_Neural_Script_Knowledge_Through_Vision_and_Language_and_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zellers_MERLOT_Reserve_Neural_Script_Knowledge_Through_Vision_and_Language_and_CVPR_2022_paper.html | CVPR 2022 | null |
Differentiable Stereopsis: Meshes From Multiple Views Using Differentiable Rendering | Shubham Goel, Georgia Gkioxari, Jitendra Malik | We propose Differentiable Stereopsis, a multi-view stereo approach that reconstructs shape and texture from few input views and noisy cameras. We pair traditional stereopsis and modern differentiable rendering to build an end-to-end model which predicts textured 3D meshes of objects with varying topologies and shape. We frame stereopsis as an optimization problem and simultaneously update shape and cameras via simple gradient descent. We run an extensive quantitative analysis and compare to traditional multi-view stereo techniques and state-of-the-art learning based methods. We show compelling reconstructions on challenging real-world scenes and for an abundance of object types with complex shape, topology and texture. | https://openaccess.thecvf.com/content/CVPR2022/papers/Goel_Differentiable_Stereopsis_Meshes_From_Multiple_Views_Using_Differentiable_Rendering_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Goel_Differentiable_Stereopsis_Meshes_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2110.05472 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Goel_Differentiable_Stereopsis_Meshes_From_Multiple_Views_Using_Differentiable_Rendering_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Goel_Differentiable_Stereopsis_Meshes_From_Multiple_Views_Using_Differentiable_Rendering_CVPR_2022_paper.html | CVPR 2022 | null |
Towards Practical Certifiable Patch Defense With Vision Transformer | Zhaoyu Chen, Bo Li, Jianghe Xu, Shuang Wu, Shouhong Ding, Wenqiang Zhang | Patch attacks, one of the most threatening forms of physical attack in adversarial examples, can lead networks to induce misclassification by modifying pixels arbitrarily in a continuous region. Certifiable patch defense can guarantee robustness that the classifier is not affected by patch attacks. Existing certifiable patch defenses sacrifice the clean accuracy of classifiers and only obtain a low certified accuracy on toy datasets. Furthermore, the clean and certified accuracy of these methods is still significantly lower than the accuracy of normal classification networks, which limits their application in practice. To move towards a practical certifiable patch defense, we introduce Vision Transformer (ViT) into the framework of Derandomized Smoothing (DS). Specifically, we propose a progressive smoothed image modeling task to train Vision Transformer, which can capture the more discriminable local context of an image while preserving the global semantic information. For efficient inference and deployment in the real world, we innovatively reconstruct the global self-attention structure of the original ViT into isolated band unit self-attention. On ImageNet, under 2% area patch attacks our method achieves 41.70% certified accuracy, a nearly 1-fold increase over the previous best method (26.00%). Simultaneously, our method achieves 78.58% clean accuracy, which is quite close to the normal ResNet-101 accuracy. Extensive experiments show that our method obtains state-of-the-art clean and certified accuracy with inferring efficiently on CIFAR-10 and ImageNet. | https://openaccess.thecvf.com/content/CVPR2022/papers/Chen_Towards_Practical_Certifiable_Patch_Defense_With_Vision_Transformer_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2203.08519 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_Towards_Practical_Certifiable_Patch_Defense_With_Vision_Transformer_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_Towards_Practical_Certifiable_Patch_Defense_With_Vision_Transformer_CVPR_2022_paper.html | CVPR 2022 | null |
A Conservative Approach for Unbiased Learning on Unknown Biases | Myeongho Jeon, Daekyung Kim, Woochul Lee, Myungjoo Kang, Joonseok Lee | Although convolutional neural networks (CNNs) achieve state-of-the-art in image classification, recent works address their unreliable predictions due to their excessive dependence on biased training data. Existing unbiased modeling postulates that the bias in the dataset is obvious to know, but it is actually unsuited for image datasets including countless sensory attributes. To mitigate this issue, we present a new scenario that does not necessitate a predefined bias. Under the observation that CNNs do have multi-variant and unbiased representations in the model, we propose a conservative framework that employs this internal information for unbiased learning. Specifically, this mechanism is implemented via hierarchical features captured along the multiple layers and orthogonal regularization. Extensive evaluations on public benchmarks demonstrate our method is effective for unbiased learning. | https://openaccess.thecvf.com/content/CVPR2022/papers/Jeon_A_Conservative_Approach_for_Unbiased_Learning_on_Unknown_Biases_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Jeon_A_Conservative_Approach_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Jeon_A_Conservative_Approach_for_Unbiased_Learning_on_Unknown_Biases_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Jeon_A_Conservative_Approach_for_Unbiased_Learning_on_Unknown_Biases_CVPR_2022_paper.html | CVPR 2022 | null |
Large-Scale Video Panoptic Segmentation in the Wild: A Benchmark | Jiaxu Miao, Xiaohan Wang, Yu Wu, Wei Li, Xu Zhang, Yunchao Wei, Yi Yang | In this paper, we present a new large-scale dataset for the video panoptic segmentation task, which aims to assign semantic classes and track identities to all pixels in a video. As the ground truth for this task is difficult to annotate, previous datasets for video panoptic segmentation are limited by either small scales or the number of scenes. In contrast, our large-scale VIdeo Panoptic Segmentation in the Wild (VIPSeg) dataset provides 3,536 videos and 84,750 frames with pixel-level panoptic annotations, covering a wide range of real-world scenarios and categories. To the best of our knowledge, our VIPSeg is the first attempt to tackle the challenging video panoptic segmentation task in the wild by considering diverse scenarios. Based on VIPSeg, we evaluate existing video panoptic segmentation approaches and propose an efficient and effective clip-based baseline method to analyze our VIPSeg dataset. Our dataset is available at https://github.com/VIPSeg-Dataset/VIPSeg-Dataset/. | https://openaccess.thecvf.com/content/CVPR2022/papers/Miao_Large-Scale_Video_Panoptic_Segmentation_in_the_Wild_A_Benchmark_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Miao_Large-Scale_Video_Panoptic_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Miao_Large-Scale_Video_Panoptic_Segmentation_in_the_Wild_A_Benchmark_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Miao_Large-Scale_Video_Panoptic_Segmentation_in_the_Wild_A_Benchmark_CVPR_2022_paper.html | CVPR 2022 | null |
Label, Verify, Correct: A Simple Few Shot Object Detection Method | Prannay Kaul, Weidi Xie, Andrew Zisserman | The objective of this paper is few-shot object detection (FSOD) - the task of expanding an object detector for a new category given only a few instances as training. We introduce a simple pseudo-labelling method to source high-quality pseudo-annotations from the training set, for each new category, vastly increasing the number of training instances and reducing class imbalance; our method finds previously unlabelled instances. Naively training with model predictions yields sub-optimal performance; we present two novel methods to improve the precision of the pseudo-labelling process: first, we introduce a verification technique to remove candidate detections with incorrect class labels; second, we train a specialised model to correct poor quality bounding boxes. After these two novel steps, we obtain a large set of high-quality pseudo-annotations that allow our final detector to be trained end-to-end. Additionally, we demonstrate our method maintains base class performance, and the utility of simple augmentations in FSOD. While benchmarking on PASCAL VOC and MS-COCO, our method achieves state-of-the-art or second-best performance compared to existing approaches across all number of shots. | https://openaccess.thecvf.com/content/CVPR2022/papers/Kaul_Label_Verify_Correct_A_Simple_Few_Shot_Object_Detection_Method_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2112.05749 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Kaul_Label_Verify_Correct_A_Simple_Few_Shot_Object_Detection_Method_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Kaul_Label_Verify_Correct_A_Simple_Few_Shot_Object_Detection_Method_CVPR_2022_paper.html | CVPR 2022 | null |
Aesthetic Text Logo Synthesis via Content-Aware Layout Inferring | Yizhi Wang, Guo Pu, Wenhan Luo, Yexin Wang, Pengfei Xiong, Hongwen Kang, Zhouhui Lian | Text logo design heavily relies on the creativity and expertise of professional designers, in which arranging element layouts is one of the most important procedures. However, few attention has been paid to this task which needs to take many factors (e.g., fonts, linguistics, topics, etc.) into consideration. In this paper, we propose a content-aware layout generation network which takes glyph images and their corresponding text as input and synthesizes aesthetic layouts for them automatically. Specifically, we develop a dual-discriminator module, including a sequence discriminator and an image discriminator, to evaluate both the character placing trajectories and rendered shapes of synthesized text logos, respectively. Furthermore, we fuse the information of linguistics from texts and visual semantics from glyphs to guide layout prediction, which both play important roles in professional layout design. To train and evaluate our approach, we construct a dataset named as TextLogo3K, consisting of about 3,500 text logos and their pixel-level segmentation. Experimental studies on this dataset demonstrate the effectiveness of our approach for synthesizing visually-pleasing text logos and verify its superiority against the state of the art. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_Aesthetic_Text_Logo_Synthesis_via_Content-Aware_Layout_Inferring_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wang_Aesthetic_Text_Logo_CVPR_2022_supplemental.zip | http://arxiv.org/abs/2204.02701 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Aesthetic_Text_Logo_Synthesis_via_Content-Aware_Layout_Inferring_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Aesthetic_Text_Logo_Synthesis_via_Content-Aware_Layout_Inferring_CVPR_2022_paper.html | CVPR 2022 | null |
Global Tracking via Ensemble of Local Trackers | Zikun Zhou, Jianqiu Chen, Wenjie Pei, Kaige Mao, Hongpeng Wang, Zhenyu He | The crux of long-term tracking lies in the difficulty of tracking the target with discontinuous moving caused by out-of-view or occlusion. Existing long-term tracking methods follow two typical strategies. The first strategy employs a local tracker to perform smooth tracking and uses another re-detector to detect the target when the target is lost. While it can exploit the temporal context like historical appearances and locations of the target, a potential limitation of such strategy is that the local tracker tends to misidentify a nearby distractor as the target instead of activating the re-detector when the real target is out of view. The other long-term tracking strategy tracks the target in the entire image globally instead of local tracking based on the previous tracking results. Unfortunately, such global tracking strategy cannot leverage the temporal context effectively. In this work, we combine the advantages of both strategies: tracking the target in a global view while exploiting the temporal context. Specifically, we perform global tracking via ensemble of local trackers spreading the full image. The smooth moving of the target can be handled steadily by one local tracker. When the local tracker accidentally loses the target due to suddenly discontinuous moving, another local tracker close to the target is then activated and can readily take over the tracking to locate the target. While the activated local tracker performs tracking locally by leveraging the temporal context, the ensemble of local trackers renders our model the global view for tracking. Extensive experiments on six datasets demonstrate that our method performs favorably against state-of-the-art algorithms. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhou_Global_Tracking_via_Ensemble_of_Local_Trackers_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhou_Global_Tracking_via_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.16092 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhou_Global_Tracking_via_Ensemble_of_Local_Trackers_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhou_Global_Tracking_via_Ensemble_of_Local_Trackers_CVPR_2022_paper.html | CVPR 2022 | null |
Autoregressive Image Generation Using Residual Quantization | Doyup Lee, Chiheon Kim, Saehoon Kim, Minsu Cho, Wook-Shin Han | For autoregressive (AR) modeling of high-resolution images, vector quantization (VQ) represents an image as a sequence of discrete codes. A short sequence length is important for an AR model to reduce its computational costs to consider long-range interactions of codes. However, we postulate that previous VQ cannot shorten the code sequence and generate high-fidelity images together in terms of the rate-distortion trade-off. In this study, we propose the two-stage framework, which consists of Residual-Quantized VAE (RQ-VAE) and RQ-Transformer, to effectively generate high-resolution images. Given a fixed codebook size, RQ-VAE can precisely approximate a feature map of an image and represent the image as a stacked map of discrete codes. Then, RQ-Transformer learns to predict the quantized feature vector at the next position by predicting the next stack of codes. Thanks to the precise approximation of RQ-VAE, we can represent a 256x256 image as 8x8 resolution of the feature map, and RQ-Transformer can efficiently reduce the computational costs. Consequently, our framework outperforms the existing AR models on various benchmarks of unconditional and conditional image generation. Our approach also has a significantly faster sampling speed than previous AR models to generate high-quality images. | https://openaccess.thecvf.com/content/CVPR2022/papers/Lee_Autoregressive_Image_Generation_Using_Residual_Quantization_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Lee_Autoregressive_Image_Generation_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.01941 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Lee_Autoregressive_Image_Generation_Using_Residual_Quantization_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Lee_Autoregressive_Image_Generation_Using_Residual_Quantization_CVPR_2022_paper.html | CVPR 2022 | null |
MPC: Multi-View Probabilistic Clustering | Junjie Liu, Junlong Liu, Shaotian Yan, Rongxin Jiang, Xiang Tian, Boxuan Gu, Yaowu Chen, Chen Shen, Jianqiang Huang | Despite the promising progress having been made, the two challenges of multi-view clustering (MVC) are still waiting for better solutions: i) Most existing methods are either not qualified or require additional steps for incomplete multi-view clustering and ii) noise or outliers might significantly degrade the overall clustering performance. In this paper, we propose a novel unified framework for incomplete and complete MVC named multi-view probabilistic clustering (MPC). MPC equivalently transforms multi-view pairwise posterior matching probability into composition of each view's individual distribution, which tolerates data missing and might extend to any number of views. Then graph-context-aware refinement with path propagation and co-neighbor propagation is used to refine pairwise probability, which alleviates the impact of noise and outliers. Finally, MPC also equivalently transforms probabilistic clustering's objective to avoid complete pairwise computation and adjusts clustering assignments by maximizing joint probability iteratively. Extensive experiments on multiple benchmarks for incomplete and complete MVC show that MPC significantly outperforms previous state-of-the-art methods in both effectiveness and efficiency. | https://openaccess.thecvf.com/content/CVPR2022/papers/Liu_MPC_Multi-View_Probabilistic_Clustering_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Liu_MPC_Multi-View_Probabilistic_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_MPC_Multi-View_Probabilistic_Clustering_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_MPC_Multi-View_Probabilistic_Clustering_CVPR_2022_paper.html | CVPR 2022 | null |
End-to-End Compressed Video Representation Learning for Generic Event Boundary Detection | Congcong Li, Xinyao Wang, Longyin Wen, Dexiang Hong, Tiejian Luo, Libo Zhang | Generic event boundary detection aims to localize the generic, taxonomy-free event boundaries that segment videos into chunks. Existing methods typically require video frames to be decoded before feeding into the network, which demands considerable computational power and storage space. To that end, we propose a new end-to-end compressed video representation learning for event boundary detection that leverages the rich information in the compressed domain, i.e., RGB, motion vectors, residuals, and the internal group of pictures (GOP) structure, without fully decoding the video. Specifically, we first use the ConvNets to extract features of the I-frames in the GOPs. After that, a light-weight spatial-channel compressed encoder is designed to compute the feature representations of the P-frames based on the motion vectors, residuals and representations of their dependent I-frames. A temporal contrastive module is proposed to determine the event boundaries of video sequences. To remedy the ambiguities of annotations and speed up the training process, we use the Gaussian kernel to preprocess the ground-truth event boundaries. Extensive experiments conducted on the Kinetics-GEBD dataset demonstrate that the proposed method achieves comparable results to the state-of-the-art methods with 4.5x faster running speed. | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_End-to-End_Compressed_Video_Representation_Learning_for_Generic_Event_Boundary_Detection_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2203.15336 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Li_End-to-End_Compressed_Video_Representation_Learning_for_Generic_Event_Boundary_Detection_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Li_End-to-End_Compressed_Video_Representation_Learning_for_Generic_Event_Boundary_Detection_CVPR_2022_paper.html | CVPR 2022 | null |
GrainSpace: A Large-Scale Dataset for Fine-Grained and Domain-Adaptive Recognition of Cereal Grains | Lei Fan, Yiwen Ding, Dongdong Fan, Donglin Di, Maurice Pagnucco, Yang Song | Cereal grains are a vital part of human diets and are important commodities for people's livelihood and international trade. Grain Appearance Inspection (GAI) serves as one of the crucial steps for the determination of grain quality and grain stratification for proper circulation, storage and food processing, etc. GAI is routinely performed manually by qualified inspectors with the aid of some hand tools. Automated GAI has the benefit of greatly assisting inspectors with their jobs but has been limited due to the lack of datasets and clear definitions of the tasks. In this paper we formulate GAI as three ubiquitous computer vision tasks: fine-grained recognition, domain adaptation and out-of-distribution recognition. We present a large-scale and publicly available cereal grains dataset called GrainSpace. Specifically, we construct three types of device prototypes for data acquisition, and a total of 5.25 million images determined by professional inspectors. The grain samples including wheat, maize and rice are collected from five countries and more than 30 regions. We also develop a comprehensive benchmark based on semi-supervised learning and self-supervised learning techniques. To the best of our knowledge, GrainSpace is the first publicly released dataset for cereal grain inspection, https://github.com/hellodfan/GrainSpace. | https://openaccess.thecvf.com/content/CVPR2022/papers/Fan_GrainSpace_A_Large-Scale_Dataset_for_Fine-Grained_and_Domain-Adaptive_Recognition_of_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Fan_GrainSpace_A_Large-Scale_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.05306 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Fan_GrainSpace_A_Large-Scale_Dataset_for_Fine-Grained_and_Domain-Adaptive_Recognition_of_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Fan_GrainSpace_A_Large-Scale_Dataset_for_Fine-Grained_and_Domain-Adaptive_Recognition_of_CVPR_2022_paper.html | CVPR 2022 | null |
BokehMe: When Neural Rendering Meets Classical Rendering | Juewen Peng, Zhiguo Cao, Xianrui Luo, Hao Lu, Ke Xian, Jianming Zhang | We propose BokehMe, a hybrid bokeh rendering framework that marries a neural renderer with a classical physically motivated renderer. Given a single image and a potentially imperfect disparity map, BokehMe generates high-resolution photo-realistic bokeh effects with adjustable blur size, focal plane, and aperture shape. To this end, we analyze the errors from the classical scattering-based method and derive a formulation to calculate an error map. Based on this formulation, we implement the classical renderer by a scattering-based method and propose a two-stage neural renderer to fix the erroneous areas from the classical renderer. The neural renderer employs a dynamic multi-scale scheme to efficiently handle arbitrary blur sizes, and it is trained to handle imperfect disparity input. Experiments show that our method compares favorably against previous methods on both synthetic image data and real image data with predicted disparity. A user study is further conducted to validate the advantage of our method. | https://openaccess.thecvf.com/content/CVPR2022/papers/Peng_BokehMe_When_Neural_Rendering_Meets_Classical_Rendering_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Peng_BokehMe_When_Neural_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Peng_BokehMe_When_Neural_Rendering_Meets_Classical_Rendering_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Peng_BokehMe_When_Neural_Rendering_Meets_Classical_Rendering_CVPR_2022_paper.html | CVPR 2022 | null |
Learning Modal-Invariant and Temporal-Memory for Video-Based Visible-Infrared Person Re-Identification | Xinyu Lin, Jinxing Li, Zeyu Ma, Huafeng Li, Shuang Li, Kaixiong Xu, Guangming Lu, David Zhang | Thanks for the cross-modal retrieval techniques, visible-infrared (RGB-IR) person re-identification (Re-ID) is achieved by projecting them into a common space, allowing person Re-ID in 24-hour surveillance systems. However, with respect to the "probe-to-gallery", almost all existing RGB-IR based cross-modal person Re-ID methods focus on image-to-image matching, while the video-to-video matching which contains much richer spatial- and temporal-information remains under-explored. In this paper, we primarily study the video-based cross-modal person Re-ID method. To achieve this task, a video-based RGB-IR dataset is constructed, in which 927 valid identities with 463,259 frames and 21,863 tracklets captured by 12 RGB/IR cameras are collected. Based on our constructed dataset, we prove that with the increase of frames in a tracklet, the performance does meet more enhancement, demonstrating the significance of video-to-video matching in RGB-IR person Re-ID. Additionally, a novel method is further proposed, which not only projects two modalities to a modal-invariant subspace, but also extracts the temporal-memory for motion-invariant. Thanks to these two strategies, much better results are achieved on our video-based cross-modal person Re-ID. The code is released at: https://github.com/VCM-project233/MITML. | https://openaccess.thecvf.com/content/CVPR2022/papers/Lin_Learning_Modal-Invariant_and_Temporal-Memory_for_Video-Based_Visible-Infrared_Person_Re-Identification_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Lin_Learning_Modal-Invariant_and_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Lin_Learning_Modal-Invariant_and_Temporal-Memory_for_Video-Based_Visible-Infrared_Person_Re-Identification_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Lin_Learning_Modal-Invariant_and_Temporal-Memory_for_Video-Based_Visible-Infrared_Person_Re-Identification_CVPR_2022_paper.html | CVPR 2022 | null |
MSDN: Mutually Semantic Distillation Network for Zero-Shot Learning | Shiming Chen, Ziming Hong, Guo-Sen Xie, Wenhan Yang, Qinmu Peng, Kai Wang, Jian Zhao, Xinge You | The key challenge of zero-shot learning (ZSL) is how to infer the latent semantic knowledge between visual and attribute features on seen classes, and thus achieving a desirable knowledge transfer to unseen classes. Prior works either simply align the global features of an image with its associated class semantic vector or utilize unidirectional attention to learn the limited latent semantic representations, which could not effectively discover the intrinsic semantic knowledge (e.g., attribute semantics) between visual and attribute features. To solve the above dilemma, we propose a Mutually Semantic Distillation Network (MSDN), which progressively distills the intrinsic semantic representations between visual and attribute features for ZSL. MSDN incorporates an attribute->visual attention sub-net that learns attribute-based visual features, and a visual->attribute attention sub-net that learns visual-based attribute features. By further introducing a semantic distillation loss, the two mutual attention sub-nets are capable of learning collaboratively and teaching each other throughout the training process. The proposed MSDN yields significant improvements over the strong baselines, leading to new state-of-the-art performances on three popular challenging benchmarks. Our source codes, pre-trained models, and more results have been available at the anonymous project website: https://anonymous.4open.science/r/MSDN. | https://openaccess.thecvf.com/content/CVPR2022/papers/Chen_MSDN_Mutually_Semantic_Distillation_Network_for_Zero-Shot_Learning_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2203.03137 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_MSDN_Mutually_Semantic_Distillation_Network_for_Zero-Shot_Learning_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_MSDN_Mutually_Semantic_Distillation_Network_for_Zero-Shot_Learning_CVPR_2022_paper.html | CVPR 2022 | null |
Oriented RepPoints for Aerial Object Detection | Wentong Li, Yijie Chen, Kaixuan Hu, Jianke Zhu | In contrast to the generic object, aerial targets are often non-axis aligned with arbitrary orientations having the cluttered surroundings. Unlike the mainstreamed approaches regressing the bounding box orientations, this paper proposes an effective adaptive points learning approach to aerial object detection by taking advantage of the adaptive points representation, which is able to capture the geometric information of the arbitrary-oriented instances. To this end, three oriented conversion functions are presented to facilitate the classification and localization with accurate orientation. Moreover, we propose an effective quality assessment and sample assignment scheme for adaptive points learning toward choosing the representative oriented reppoints samples during training, which is able to capture the non-axis aligned features from adjacent objects or background noises. A spatial constraint is introduced to penalize the outlier points for roust adaptive learning. Experimental results on four challenging aerial datasets including DOTA, HRSC2016, UCAS-AOD and DIOR-R, demonstrate the efficacy of our proposed approach. The source code is availabel at: https://github.com/LiWentomng/OrientedRepPoints. | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_Oriented_RepPoints_for_Aerial_Object_Detection_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2105.11111 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Oriented_RepPoints_for_Aerial_Object_Detection_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Oriented_RepPoints_for_Aerial_Object_Detection_CVPR_2022_paper.html | CVPR 2022 | null |
OccAM's Laser: Occlusion-Based Attribution Maps for 3D Object Detectors on LiDAR Data | David Schinagl, Georg Krispel, Horst Possegger, Peter M. Roth, Horst Bischof | While 3D object detection in LiDAR point clouds is well-established in academia and industry, the explainability of these models is a largely unexplored field. In this paper, we propose a method to generate attribution maps for the detected objects in order to better understand the behavior of such models. These maps indicate the importance of each 3D point in predicting the specific objects. Our method works with black-box models: We do not require any prior knowledge of the architecture nor access to the model's internals, like parameters, activations or gradients. Our efficient perturbation-based approach empirically estimates the importance of each point by testing the model with randomly generated subsets of the input point cloud. Our sub-sampling strategy takes into account the special characteristics of LiDAR data, such as the depth-dependent point density. We show a detailed evaluation of the attribution maps and demonstrate that they are interpretable and highly informative. Furthermore, we compare the attribution maps of recent 3D object detection architectures to provide insights into their decision-making processes. | https://openaccess.thecvf.com/content/CVPR2022/papers/Schinagl_OccAMs_Laser_Occlusion-Based_Attribution_Maps_for_3D_Object_Detectors_on_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Schinagl_OccAMs_Laser_Occlusion-Based_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Schinagl_OccAMs_Laser_Occlusion-Based_Attribution_Maps_for_3D_Object_Detectors_on_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Schinagl_OccAMs_Laser_Occlusion-Based_Attribution_Maps_for_3D_Object_Detectors_on_CVPR_2022_paper.html | CVPR 2022 | null |
BigDatasetGAN: Synthesizing ImageNet With Pixel-Wise Annotations | Daiqing Li, Huan Ling, Seung Wook Kim, Karsten Kreis, Sanja Fidler, Antonio Torralba | Annotating images with pixel-wise labels is a time-consuming and costly process. Recently, DatasetGAN showcased a promising alternative - to synthesize a large labeled dataset via a generative adversarial network (GAN) by exploiting a small set of manually labeled, GAN-generated images. Here, we scale DatasetGAN to ImageNet scale of class diversity. We take image samples from the class-conditional generative model BigGAN trained on ImageNet, and manually annotate only 5 images per class, for all 1k classes. By training an effective feature segmentation architecture on top of BigGAN, we turn BigGAN into a labeled dataset generator. We further show that VQGAN can similarly serve as a dataset generator, leveraging the already annotated data. We create a new ImageNet benchmark by labeling an additional set of real images and evaluate segmentation performance in a variety of settings. Through an extensive ablation study we show big gains in leveraging a large generated dataset to train different supervised and self-supervised backbone models on pixel-wise tasks. Furthermore, we demonstrate that using our synthesized datasets for pre-training leads to improvements over standard ImageNet pre-training on several downstream datasets, such as PASCAL-VOC, MS-COCO, Cityscapes and chest X-ray, as well as tasks (detection, segmentation). Our benchmark will be made public and maintain a leaderboard for this challenging task. | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_BigDatasetGAN_Synthesizing_ImageNet_With_Pixel-Wise_Annotations_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Li_BigDatasetGAN_Synthesizing_ImageNet_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2201.04684 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Li_BigDatasetGAN_Synthesizing_ImageNet_With_Pixel-Wise_Annotations_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Li_BigDatasetGAN_Synthesizing_ImageNet_With_Pixel-Wise_Annotations_CVPR_2022_paper.html | CVPR 2022 | null |
Align Representations With Base: A New Approach to Self-Supervised Learning | Shaofeng Zhang, Lyn Qiu, Feng Zhu, Junchi Yan, Hengrui Zhang, Rui Zhao, Hongyang Li, Xiaokang Yang | Existing symmetric contrastive learning methods suffer from collapses (complete and dimensional) or quadratic complexity of objectives. Departure from these methods which maximize mutual information of two generated views, along either instance or feature dimension, the proposed paradigm introduces intermediate variables at the feature level, and maximizes the consistency between variables and representations of each view. Specifically, the proposed intermediate variables are the nearest group of base vectors to representations. Hence, we call the proposed method ARB (Align Representations with Base). Compared with other symmetric approaches, ARB 1) does not require negative pairs, which leads the complexity of the overall objective function is in linear order, 2) reduces feature redundancy, increasing the information density of training samples, 3) is more robust to output dimension size, which outperforms previous feature-wise arts over 28% Top-1 accuracy on ImageNet-100 under low-dimension settings. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhang_Align_Representations_With_Base_A_New_Approach_to_Self-Supervised_Learning_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Align_Representations_With_Base_A_New_Approach_to_Self-Supervised_Learning_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Align_Representations_With_Base_A_New_Approach_to_Self-Supervised_Learning_CVPR_2022_paper.html | CVPR 2022 | null |
Exploring Denoised Cross-Video Contrast for Weakly-Supervised Temporal Action Localization | Jingjing Li, Tianyu Yang, Wei Ji, Jue Wang, Li Cheng | Weakly-supervised temporal action localization aims to localize actions in untrimmed videos with only video-level labels. Most existing methods address this problem with a "localization-by-classification" pipeline that localizes action regions based on snippet-wise classification sequences. Snippet-wise classifications are unfortunately error prone due to the sparsity of video-level labels. Inspired by recent success in unsupervised contrastive representation learning, we propose a novel denoised cross-video contrastive algorithm, aiming to enhance the feature discrimination ability of video snippets for accurate temporal action localization in the weakly-supervised setting. This is enabled by three key designs: 1) an effective pseudo-label denoising module to alleviate the side effects caused by noisy contrastive features, 2) an efficient region-level feature contrast strategy with a region-level memory bank to capture "global" contrast across the entire dataset, and 3) a diverse contrastive learning strategy to enable action-background separation as well as intra-class compactness & inter-class separability. Extensive experiments on THUMOS14 and ActivityNet v1.3 demonstrate the superior performance of our approach. | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_Exploring_Denoised_Cross-Video_Contrast_for_Weakly-Supervised_Temporal_Action_Localization_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Exploring_Denoised_Cross-Video_Contrast_for_Weakly-Supervised_Temporal_Action_Localization_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Exploring_Denoised_Cross-Video_Contrast_for_Weakly-Supervised_Temporal_Action_Localization_CVPR_2022_paper.html | CVPR 2022 | null |
Pre-Train, Self-Train, Distill: A Simple Recipe for Supersizing 3D Reconstruction | Kalyan Vasudev Alwala, Abhinav Gupta, Shubham Tulsiani | Our work learns a unified model for single-view 3D reconstruction of objects from hundreds of semantic categories. As a scalable alternative to direct 3D supervision, our work relies on segmented image collections for learning 3D of generic categories. Unlike prior works that use similar supervision but learn independent category-specific models from scratch, our approach of learning a unified model simplifies the training process while also allowing the model to benefit from the common structure across categories. Using image collections from standard recognition datasets, we show that our approach allows learning 3D inference for over 150 object categories. We evaluate using two datasets and qualitatively and quantitatively show that our unified reconstruction approach improves over prior category-specific reconstruction baselines. Our final 3D reconstruction model is also capable of zero-shot inference on images from unseen object categories and we empirically show that increasing the number of training categories improves the reconstruction quality. | https://openaccess.thecvf.com/content/CVPR2022/papers/Alwala_Pre-Train_Self-Train_Distill_A_Simple_Recipe_for_Supersizing_3D_Reconstruction_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Alwala_Pre-Train_Self-Train_Distill_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.03642 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Alwala_Pre-Train_Self-Train_Distill_A_Simple_Recipe_for_Supersizing_3D_Reconstruction_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Alwala_Pre-Train_Self-Train_Distill_A_Simple_Recipe_for_Supersizing_3D_Reconstruction_CVPR_2022_paper.html | CVPR 2022 | null |
Meta Distribution Alignment for Generalizable Person Re-Identification | Hao Ni, Jingkuan Song, Xiaopeng Luo, Feng Zheng, Wen Li, Heng Tao Shen | Domain Generalizable (DG) person ReID is a challenging task which trains a model on source domains yet generalizes well on target domains. Existing methods use source domains to learn domain-invariant features, and assume those features are also irrelevant with target domains. However, they do not consider the target domain information which is unavailable in the training phrase of DG. To address this issue, we propose a novel Meta Distribution Alignment (MDA) method to enable them to share similar distribution in a test-time-training fashion. Specifically, since high-dimensional features are difficult to constrain with a known simple distribution, we first introduce an intermediate latent space constrained to a known prior distribution. The source domain data is mapped to this latent space and then reconstructed back. A meta-learning strategy is introduced to facilitate generalization and support fast adaption. To reduce their discrepancy, we further propose a test-time adaptive updating strategy based on the latent space which efficiently adapts model to unseen domains with a few samples. Extensive experimental results show that our model outperforms the state-of-the-art methods by up to 5.1% R-1 on average on the large-scale and 4.7% R-1 on the single-source domain generalization ReID benchmark. | https://openaccess.thecvf.com/content/CVPR2022/papers/Ni_Meta_Distribution_Alignment_for_Generalizable_Person_Re-Identification_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Ni_Meta_Distribution_Alignment_for_Generalizable_Person_Re-Identification_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Ni_Meta_Distribution_Alignment_for_Generalizable_Person_Re-Identification_CVPR_2022_paper.html | CVPR 2022 | null |
TeachAugment: Data Augmentation Optimization Using Teacher Knowledge | Teppei Suzuki | Optimization of image transformation functions for the purpose of data augmentation has been intensively studied. In particular, adversarial data augmentation strategies, which search augmentation maximizing task loss, show significant improvement in the model generalization for many tasks. However, the existing methods require careful parameter tuning to avoid excessively strong deformations that take away image features critical for acquiring generalization. In this paper, we propose a data augmentation optimization method based on the adversarial strategy called TeachAugment, which can produce informative transformed images to the model without requiring careful tuning by leveraging a teacher model. Specifically, the augmentation is searched so that augmented images are adversarial for the target model and recognizable for the teacher model. We also propose data augmentation using neural networks, which simplifies the search space design and allows for updating of the data augmentation using the gradient method. We show that TeachAugment outperforms existing methods in experiments of image classification, semantic segmentation, and unsupervised representation learning tasks. | https://openaccess.thecvf.com/content/CVPR2022/papers/Suzuki_TeachAugment_Data_Augmentation_Optimization_Using_Teacher_Knowledge_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Suzuki_TeachAugment_Data_Augmentation_CVPR_2022_supplemental.zip | http://arxiv.org/abs/2202.12513 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Suzuki_TeachAugment_Data_Augmentation_Optimization_Using_Teacher_Knowledge_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Suzuki_TeachAugment_Data_Augmentation_Optimization_Using_Teacher_Knowledge_CVPR_2022_paper.html | CVPR 2022 | null |
SVIP: Sequence VerIfication for Procedures in Videos | Yicheng Qian, Weixin Luo, Dongze Lian, Xu Tang, Peilin Zhao, Shenghua Gao | In this paper, we propose a novel sequence verification task that aims to distinguish positive video pairs performing the same action sequence from negative ones with step-level transformations but still conducting the same task. Such a challenging task resides in an open-set setting without prior action detection or segmentation that requires event-level or even frame-level annotations. To that end, we carefully reorganize two publicly available action-related datasets with step-procedure-task structure. To fully investigate the effectiveness of any method, we collect a scripted video dataset enumerating all kinds of step-level transformations in chemical experiments. Besides, a novel evaluation metric Weighted Distance Ratio is introduced to ensure equivalence for different step-level transformations during evaluation. In the end, a simple but effective baseline based on the transformer encoder with a novel sequence alignment loss is introduced to better characterize long-term dependency between steps, which outperforms other action recognition methods. Codes and data will be released. | https://openaccess.thecvf.com/content/CVPR2022/papers/Qian_SVIP_Sequence_VerIfication_for_Procedures_in_Videos_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Qian_SVIP_Sequence_VerIfication_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.06447 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Qian_SVIP_Sequence_VerIfication_for_Procedures_in_Videos_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Qian_SVIP_Sequence_VerIfication_for_Procedures_in_Videos_CVPR_2022_paper.html | CVPR 2022 | null |
Weakly Supervised Temporal Sentence Grounding With Gaussian-Based Contrastive Proposal Learning | Minghang Zheng, Yanjie Huang, Qingchao Chen, Yuxin Peng, Yang Liu | Temporal sentence grounding aims to detect the most salient moment corresponding to the natural language query from untrimmed videos. As labeling the temporal boundaries is labor-intensive and subjective, the weakly-supervised methods have recently received increasing attention. Most of the existing weakly-supervised methods generate the proposals by sliding windows, which are content-independent and of low quality. Moreover, they train their model to distinguish positive visual-language pairs from negative ones randomly collected from other videos, ignoring the highly confusing video segments within the same video. In this paper, we propose Contrastive Proposal Learning (CPL) to overcome the above limitations. Specifically, we use multiple learnable Gaussian functions to generate both positive and negative proposals within the same video that can characterize the multiple events in a long video. Then, we propose a controllable easy to hard negative proposal mining strategy to collect negative samples within the same video, which can ease the model optimization and enables CPL to distinguish highly confusing scenes. The experiments show that our method achieves state-of-the-art performance on Charades-STA and ActivityNet Captions datasets. The code and models are available at https://github.com/minghangz/cpl. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zheng_Weakly_Supervised_Temporal_Sentence_Grounding_With_Gaussian-Based_Contrastive_Proposal_Learning_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zheng_Weakly_Supervised_Temporal_Sentence_Grounding_With_Gaussian-Based_Contrastive_Proposal_Learning_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zheng_Weakly_Supervised_Temporal_Sentence_Grounding_With_Gaussian-Based_Contrastive_Proposal_Learning_CVPR_2022_paper.html | CVPR 2022 | null |
Low-Resource Adaptation for Personalized Co-Speech Gesture Generation | Chaitanya Ahuja, Dong Won Lee, Louis-Philippe Morency | Personalizing an avatar for co-speech gesture generation from spoken language requires learning the idiosyncrasies of a person's gesture style from a small amount of data. Previous methods in gesture generation require large amounts of data for each speaker, which is often infeasible. We propose an approach, named DiffGAN, that efficiently personalizes co-speech gesture generation models of a high-resource source speaker to target speaker with just 2 minutes of target training data. A unique characteristic of DiffGAN is its ability to account for the crossmodal grounding shift, while also addressing the distribution shift in the output domain. We substantiate the effectiveness of our approach a large scale publicly available dataset through quantitative, qualitative and user studies, which show that our proposed methodology significantly outperforms prior approaches for low-resource adaptation of gesture generation. Code and videos can be found at https://chahuja.com/diffgan | https://openaccess.thecvf.com/content/CVPR2022/papers/Ahuja_Low-Resource_Adaptation_for_Personalized_Co-Speech_Gesture_Generation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Ahuja_Low-Resource_Adaptation_for_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Ahuja_Low-Resource_Adaptation_for_Personalized_Co-Speech_Gesture_Generation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Ahuja_Low-Resource_Adaptation_for_Personalized_Co-Speech_Gesture_Generation_CVPR_2022_paper.html | CVPR 2022 | null |
BoosterNet: Improving Domain Generalization of Deep Neural Nets Using Culpability-Ranked Features | Nourhan Bayasi, Ghassan Hamarneh, Rafeef Garbi | Deep learning (DL) models trained to minimize empirical risk on a single domain often fail to generalize when applied to other domains. Model failures due to poor generalizability are quite common in practice and may prove quite perilous in mission-critical applications, e.g., diagnostic imaging where real-world data often exhibits pronounced variability. Such limitations have led to increased interest in domain generalization (DG) approaches that improve the ability of models learned from a single or multiple source domains to generalize to out-of-distribution (OOD) test domains. In this work, we propose BoosterNet, a lean add-on network that can be simply appended to any arbitrary core network to improve its generalization capability without requiring any changes in its architecture or training procedure. Specifically, using a novel measure of feature culpability, BoosterNet is trained episodically on the most and least culpable data features extracted from critical units in the core network based on their contribution towards class-specific prediction errors, which have shown to improve generalization. At inference time, corresponding test image features are extracted from the closest class-specific units, determined by smart gating via a Siamese network, and fed to BoosterNet for improved generalization. We evaluate the performance of BoosterNet within two very different classification problems, digits and skin lesions, and demonstrate a marked improvement in model generalization to OOD test domains compared to SOTA. | https://openaccess.thecvf.com/content/CVPR2022/papers/Bayasi_BoosterNet_Improving_Domain_Generalization_of_Deep_Neural_Nets_Using_Culpability-Ranked_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Bayasi_BoosterNet_Improving_Domain_Generalization_of_Deep_Neural_Nets_Using_Culpability-Ranked_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Bayasi_BoosterNet_Improving_Domain_Generalization_of_Deep_Neural_Nets_Using_Culpability-Ranked_CVPR_2022_paper.html | CVPR 2022 | null |
Task-Specific Inconsistency Alignment for Domain Adaptive Object Detection | Liang Zhao, Limin Wang | Detectors trained with massive labeled data often exhibit dramatic performance degradation in some particular scenarios with data distribution gap. To alleviate this problem of domain shift, conventional wisdom typically concentrates solely on reducing the discrepancy between the source and target domains via attached domain classifiers, yet ignoring the difficulty of such transferable features in coping with both classification and localization subtasks in object detection. To address this issue, in this paper, we propose Task-specific Inconsistency Alignment (TIA), by developing a new alignment mechanism in separate task spaces, improving the performance of the detector on both subtasks. Specifically, we add a set of auxiliary predictors for both classification and localization branches, and exploit their behavioral inconsistencies as finer-grained domain-specific measures. Then, we devise task-specific losses to align such cross-domain disagreement of both subtasks. By optimizing them individually, we are able to well approximate the category- and boundary-wise discrepancies in each task space, and therefore narrow them in a decoupled manner. TIA demonstrates superior results on various scenarios to the previous state-of-the-art methods. It is also observed that both the classification and localization capabilities of the detector are sufficiently strengthened, further demonstrating the effectiveness of our TIA method. Code and trained models are publicly available at https://github.com/MCG-NJU/TIA. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhao_Task-Specific_Inconsistency_Alignment_for_Domain_Adaptive_Object_Detection_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhao_Task-Specific_Inconsistency_Alignment_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.15345 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhao_Task-Specific_Inconsistency_Alignment_for_Domain_Adaptive_Object_Detection_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhao_Task-Specific_Inconsistency_Alignment_for_Domain_Adaptive_Object_Detection_CVPR_2022_paper.html | CVPR 2022 | null |
HDR-NeRF: High Dynamic Range Neural Radiance Fields | Xin Huang, Qi Zhang, Ying Feng, Hongdong Li, Xuan Wang, Qing Wang | We present High Dynamic Range Neural Radiance Fields (HDR-NeRF) to recover an HDR radiance field from a set of low dynamic range (LDR) views with different exposures. Using the HDR-NeRF, we are able to generate both novel HDR views and novel LDR views under different exposures. The key to our method is to model the physical imaging process, which dictates that the radiance of a scene point transforms to a pixel value in the LDR image with two implicit functions: a radiance field and a tone mapper. The radiance field encodes the scene radiance (values vary from 0 to +infty), which outputs the density and radiance of a ray by giving corresponding ray origin and ray direction. The tone mapper models the mapping process that a ray hitting on the camera sensor becomes a pixel value. The color of the ray is predicted by feeding the radiance and the corresponding exposure time into the tone mapper. We use the classic volume rendering technique to project the output radiance, colors, and densities into HDR and LDR images, while only the input LDR images are used as the supervision. We collect a new forward-facing HDR dataset to evaluate the proposed method. Experimental results on synthetic and real-world scenes validate that our method can not only accurately control the exposures of synthesized views but also render views with a high dynamic range. | https://openaccess.thecvf.com/content/CVPR2022/papers/Huang_HDR-NeRF_High_Dynamic_Range_Neural_Radiance_Fields_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Huang_HDR-NeRF_High_Dynamic_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Huang_HDR-NeRF_High_Dynamic_Range_Neural_Radiance_Fields_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Huang_HDR-NeRF_High_Dynamic_Range_Neural_Radiance_Fields_CVPR_2022_paper.html | CVPR 2022 | null |
MS2DG-Net: Progressive Correspondence Learning via Multiple Sparse Semantics Dynamic Graph | Luanyuan Dai, Yizhang Liu, Jiayi Ma, Lifang Wei, Taotao Lai, Changcai Yang, Riqing Chen | Establishing superior-quality correspondences in an image pair is pivotal to many subsequent computer vision tasks. Using Euclidean distance between correspondences to find neighbors and extract local information is a common strategy in previous works. However, most such works ignore similar sparse semantics information between two given images and cannot capture local topology among correspondences well. Therefore, to deal with the above problems, Multiple Sparse Semantics Dynamic Graph Network (MS^ 2 DG-Net) is proposed, in this paper, to predict probabilities of correspondences as inliers and recover camera poses. MS^ 2 DG-Net dynamically builds sparse semantics graphs based on sparse semantics similarity between two given images, to capture local topology among correspondences, while maintaining permutation-equivariant. Extensive experiments prove that MS^ 2 DG-Net outperforms state-of-the-art methods in outlier removal and camera pose estimation tasks on the public datasets with heavy outliers. Source code:https://github.com/changcaiyang/MS2DG-Net | https://openaccess.thecvf.com/content/CVPR2022/papers/Dai_MS2DG-Net_Progressive_Correspondence_Learning_via_Multiple_Sparse_Semantics_Dynamic_Graph_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Dai_MS2DG-Net_Progressive_Correspondence_Learning_via_Multiple_Sparse_Semantics_Dynamic_Graph_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Dai_MS2DG-Net_Progressive_Correspondence_Learning_via_Multiple_Sparse_Semantics_Dynamic_Graph_CVPR_2022_paper.html | CVPR 2022 | null |
Neural Emotion Director: Speech-Preserving Semantic Control of Facial Expressions in "In-the-Wild" Videos | Foivos Paraperas Papantoniou, Panagiotis P. Filntisis, Petros Maragos, Anastasios Roussos | In this paper, we introduce a novel deep learning method for photo-realistic manipulation of the emotional state of actors in "in-the-wild" videos. The proposed method is based on a parametric 3D face representation of the actor in the input scene that offers a reliable disentanglement of the facial identity from the head pose and facial expressions. It then uses a novel deep domain translation framework that alters the facial expressions in a consistent and plausible manner, taking into account their dynamics. Finally, the altered facial expressions are used to photo-realistically manipulate the facial region in the input scene based on an especially-designed neural face renderer. To the best of our knowledge, our method is the first to be capable of controlling the actor's facial expressions by even using as a sole input the semantic labels of the manipulated emotions, while at the same time preserving the speech-related lip movements. We conduct extensive qualitative and quantitative evaluations and comparisons, which demonstrate the effectiveness of our approach and the especially promising results that we obtain. Our method opens a plethora of new possibilities for useful applications of neural rendering technologies, ranging from movie post-production and video games to photo-realistic affective avatars. | https://openaccess.thecvf.com/content/CVPR2022/papers/Papantoniou_Neural_Emotion_Director_Speech-Preserving_Semantic_Control_of_Facial_Expressions_in_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Papantoniou_Neural_Emotion_Director_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.00585 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Papantoniou_Neural_Emotion_Director_Speech-Preserving_Semantic_Control_of_Facial_Expressions_in_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Papantoniou_Neural_Emotion_Director_Speech-Preserving_Semantic_Control_of_Facial_Expressions_in_CVPR_2022_paper.html | CVPR 2022 | null |
Learning To Listen: Modeling Non-Deterministic Dyadic Facial Motion | Evonne Ng, Hanbyul Joo, Liwen Hu, Hao Li, Trevor Darrell, Angjoo Kanazawa, Shiry Ginosar | We present a framework for modeling interactional communication in dyadic conversations: given multimodal inputs of a speaker, we autoregressively output multiple possibilities of corresponding listener motion. We combine the motion and speech audio of the speaker using a motion-audio cross attention transformer. Furthermore, we enable non-deterministic prediction by learning a discrete latent representation of realistic listener motion with a novel motion-encoding VQ-VAE. Our method organically captures the multimodal and non-deterministic nature of nonverbal dyadic interactions. Moreover, it produces realistic 3D listener facial motion synchronous with the speaker (see video). We demonstrate that our method outperforms baselines qualitatively and quantitatively via a rich suite of experiments. To facilitate this line of research, we introduce a novel and large in-the-wild dataset of dyadic conversations. Code, data, and videos available at http://people.eecs.berkeley.edu/ evonne_ng/projects/learning2listen/. | https://openaccess.thecvf.com/content/CVPR2022/papers/Ng_Learning_To_Listen_Modeling_Non-Deterministic_Dyadic_Facial_Motion_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2204.08451 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Ng_Learning_To_Listen_Modeling_Non-Deterministic_Dyadic_Facial_Motion_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Ng_Learning_To_Listen_Modeling_Non-Deterministic_Dyadic_Facial_Motion_CVPR_2022_paper.html | CVPR 2022 | null |
3PSDF: Three-Pole Signed Distance Function for Learning Surfaces With Arbitrary Topologies | Weikai Chen, Cheng Lin, Weiyang Li, Bo Yang | Recent advances in learning 3D shapes using neural implicit functions have achieved impressive results by breaking the previous barrier of resolution and diversity for varying topologies. However, most of such approaches are limited to closed surfaces as they require the space to be divided into inside and outside. More recent works based on unsigned distance function have been proposed to handle complex geometry containing both the open and closed surfaces. Nonetheless, as their direct outputs are point clouds, robustly obtaining high-quality meshing results from discrete points remains an open question. We present a novel learnable implicit representation, called the three-pole signed distance function (3PSDF), that can represent non-watertight 3D shapes with arbitrary topologies while supporting easy field-to-mesh conversion using the classic Marching Cubes algorithm. The key to our method is the introduction of a new sign, the NULL sign, in addition to the conventional in and out labels. The existence of the null sign could stop the formation of a closed isosurface derived from the bisector of the in/out regions. Further, we propose a dedicated learning framework to effectively learn 3PSDF without worrying about the vanishing gradient due to the null labels. Experimental results show that our approach outperforms the previous state-of-the-art methods in a wide range of benchmarks both quantitatively and qualitatively. | https://openaccess.thecvf.com/content/CVPR2022/papers/Chen_3PSDF_Three-Pole_Signed_Distance_Function_for_Learning_Surfaces_With_Arbitrary_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Chen_3PSDF_Three-Pole_Signed_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2205.15572 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_3PSDF_Three-Pole_Signed_Distance_Function_for_Learning_Surfaces_With_Arbitrary_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_3PSDF_Three-Pole_Signed_Distance_Function_for_Learning_Surfaces_With_Arbitrary_CVPR_2022_paper.html | CVPR 2022 | null |
Capturing Humans in Motion: Temporal-Attentive 3D Human Pose and Shape Estimation From Monocular Video | Wen-Li Wei, Jen-Chun Lin, Tyng-Luh Liu, Hong-Yuan Mark Liao | Learning to capture human motion is essential to 3D human pose and shape estimation from monocular video. However, the existing methods mainly rely on recurrent or convolutional operation to model such temporal information, which limits the ability to capture non-local context relations of human motion. To address this problem, we propose a motion pose and shape network (MPS-Net) to effectively capture humans in motion to estimate accurate and temporally coherent 3D human pose and shape from a video. Specifically, we first propose a motion continuity attention (MoCA) module that leverages visual cues observed from human motion to adaptively recalibrate the range that needs attention in the sequence to better capture the motion continuity dependencies. Then, we develop a hierarchical attentive feature integration (HAFI) module to effectively combine adjacent past and future feature representations to strengthen temporal correlation and refine the feature representation of the current frame. By coupling the MoCA and HAFI modules, the proposed MPS-Net excels in estimating 3D human pose and shape in the video. Though conceptually simple, our MPS-Net not only outperforms the state-of-the-art methods on the 3DPW, MPI-INF-3DHP, and Human3.6M benchmark datasets, but also uses fewer network parameters. The video demos can be found at https://mps-net.github.io/MPS-Net/. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wei_Capturing_Humans_in_Motion_Temporal-Attentive_3D_Human_Pose_and_Shape_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wei_Capturing_Humans_in_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.08534 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wei_Capturing_Humans_in_Motion_Temporal-Attentive_3D_Human_Pose_and_Shape_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wei_Capturing_Humans_in_Motion_Temporal-Attentive_3D_Human_Pose_and_Shape_CVPR_2022_paper.html | CVPR 2022 | null |
MixFormer: End-to-End Tracking With Iterative Mixed Attention | Yutao Cui, Cheng Jiang, Limin Wang, Gangshan Wu | Tracking often uses a multi-stage pipeline of feature extraction, target information integration, and bounding box estimation. To simplify this pipeline and unify the process of feature extraction and target information integration, we present a compact tracking framework, termed as MixFormer, built upon transformers. Our core design is to utilize the flexibility of attention operations, and propose a Mixed Attention Module (MAM) for simultaneous feature extraction and target information integration. This synchronous modeling scheme allows to extract target-specific discriminative features and perform extensive communication between target and search area. Based on MAM, we build our MixFormer tracking framework simply by stacking multiple MAMs with progressive patch embedding and placing a localization head on top. In addition, to handle multiple target templates during online tracking, we devise an asymmetric attention scheme in MAM to reduce computational cost, and propose an effective score prediction module to select high-quality templates. Our MixFormer sets a new state-of-the-art performance on five tracking benchmarks, including LaSOT, TrackingNet, VOT2020, GOT-10k, and UAV123. In particular, our MixFormer-L achieves NP score of 79.9% on LaSOT, 88.9% on TrackingNet and EAO of 0.555 on VOT2020. We also perform in-depth ablation studies to demonstrate the effectiveness of simultaneous feature extraction and information integration. Code and trained models are publicly available at https://github.com/MCG-NJU/MixFormer. | https://openaccess.thecvf.com/content/CVPR2022/papers/Cui_MixFormer_End-to-End_Tracking_With_Iterative_Mixed_Attention_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Cui_MixFormer_End-to-End_Tracking_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.11082 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Cui_MixFormer_End-to-End_Tracking_With_Iterative_Mixed_Attention_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Cui_MixFormer_End-to-End_Tracking_With_Iterative_Mixed_Attention_CVPR_2022_paper.html | CVPR 2022 | null |
Sparse Fuse Dense: Towards High Quality 3D Detection With Depth Completion | Xiaopei Wu, Liang Peng, Honghui Yang, Liang Xie, Chenxi Huang, Chengqi Deng, Haifeng Liu, Deng Cai | Current LiDAR-only 3D detection methods inevitably suffer from the sparsity of point clouds. Many multi-modal methods are proposed to alleviate this issue, while different representations of images and point clouds make it difficult to fuse them, resulting in suboptimal performance. In this paper, we present a novel multi-modal framework SFD (Sparse Fuse Dense), which utilizes pseudo point clouds generated from depth completion to tackle the issues mentioned above. Different from prior works, we propose a new RoI fusion strategy 3D-GAF (3D Grid-wise Attentive Fusion) to make fuller use of information from different types of point clouds. Specifically, 3D-GAF fuses 3D RoI features from the pair of point clouds in a grid-wise attentive way, which is more fine-grained and more precise. In addition, we propose a SynAugment (Synchronized Augmentation) to enable our multi-modal framework to utilize all data augmentation approaches tailored to LiDAR-only methods. Lastly, we customize an effective and efficient feature extractor CPConv (Color Point Convolution) for pseudo point clouds. It can explore 2D image features and 3D geometric features of pseudo point clouds simultaneously. Our method holds the highest entry on the KITTI car 3D object detection leaderboard, demonstrating the effectiveness of our SFD. Code will be made publicly available. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wu_Sparse_Fuse_Dense_Towards_High_Quality_3D_Detection_With_Depth_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wu_Sparse_Fuse_Dense_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.09780 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wu_Sparse_Fuse_Dense_Towards_High_Quality_3D_Detection_With_Depth_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wu_Sparse_Fuse_Dense_Towards_High_Quality_3D_Detection_With_Depth_CVPR_2022_paper.html | CVPR 2022 | null |
GIRAFFE HD: A High-Resolution 3D-Aware Generative Model | Yang Xue, Yuheng Li, Krishna Kumar Singh, Yong Jae Lee | 3D-aware generative models have shown that the introduction of 3D information can lead to more controllable image generation. In particular, the current state-of-the-art model GIRAFFE can control each object's rotation, translation, scale, and scene camera pose without corresponding supervision. However, GIRAFFE only operates well when the image resolution is low. We propose GIRAFFE HD, a high-resolution 3D-aware generative model that inherits all of GIRAFFE's controllable features while generating high-quality, high-resolution images (512^2 resolution and above). The key idea is to leverage a style-based neural renderer, and to independently generate the foreground and background to force their disentanglement while imposing consistency constraints to stitch them together to composite a coherent final image. We demonstrate state-of-the-art 3D controllable high-resolution image generation on multiple natural image datasets. | https://openaccess.thecvf.com/content/CVPR2022/papers/Xue_GIRAFFE_HD_A_High-Resolution_3D-Aware_Generative_Model_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Xue_GIRAFFE_HD_A_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.14954 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Xue_GIRAFFE_HD_A_High-Resolution_3D-Aware_Generative_Model_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Xue_GIRAFFE_HD_A_High-Resolution_3D-Aware_Generative_Model_CVPR_2022_paper.html | CVPR 2022 | null |
InOut: Diverse Image Outpainting via GAN Inversion | Yen-Chi Cheng, Chieh Hubert Lin, Hsin-Ying Lee, Jian Ren, Sergey Tulyakov, Ming-Hsuan Yang | Image outpainting seeks for a semantically consistent extension of the input image beyond its available content. Compared to inpainting --- filling in missing pixels in a way coherent with the neighboring pixels --- outpainting can be achieved in more diverse ways since the problem is less constrained by the surrounding pixels. Existing image outpainting methods pose the problem as a conditional image-to-image translation task, often generating repetitive structures and textures by replicating the content available in the input image. In this work, we formulate the problem from the perspective of inverting generative adversarial networks. Our generator renders micro-patches conditioned on their joint latent code as well as their individual positions in the image. To outpaint an image, we seek for multiple latent codes not only recovering available patches but also synthesizing diverse outpainting by patch-based generation. This leads to richer structure and content in the outpainted regions. Furthermore, our formulation allows for outpainting conditioned on the categorical input, thereby enabling flexible user controls. Extensive experimental results demonstrate the proposed method performs favorably against existing in- and outpainting methods, featuring higher visual quality and diversity. | https://openaccess.thecvf.com/content/CVPR2022/papers/Cheng_InOut_Diverse_Image_Outpainting_via_GAN_Inversion_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Cheng_InOut_Diverse_Image_Outpainting_via_GAN_Inversion_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Cheng_InOut_Diverse_Image_Outpainting_via_GAN_Inversion_CVPR_2022_paper.html | CVPR 2022 | null |
PNP: Robust Learning From Noisy Labels by Probabilistic Noise Prediction | Zeren Sun, Fumin Shen, Dan Huang, Qiong Wang, Xiangbo Shu, Yazhou Yao, Jinhui Tang | Label noise has been a practical challenge in deep learning due to the strong capability of deep neural networks in fitting all training data. Prior literature primarily resorts to sample selection methods for combating noisy labels. However, these approaches focus on dividing samples by order sorting or threshold selection, inevitably introducing hyper-parameters (e.g., selection ratio / threshold) that are hard-to-tune and dataset-dependent. To this end, we propose a simple yet effective approach named PNP (Probabilistic Noise Prediction) to explicitly model label noise. Specifically, we simultaneously train two networks, in which one predicts the category label and the other predicts the noise type. By predicting label noise probabilistically, we identify noisy samples and adopt dedicated optimization objectives accordingly. Finally, we establish a joint loss for network update by unifying the classification loss, the auxiliary constraint loss, and the in-distribution consistency loss. Comprehensive experimental results on synthetic and real-world datasets demonstrate the superiority of our proposed method. | https://openaccess.thecvf.com/content/CVPR2022/papers/Sun_PNP_Robust_Learning_From_Noisy_Labels_by_Probabilistic_Noise_Prediction_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Sun_PNP_Robust_Learning_From_Noisy_Labels_by_Probabilistic_Noise_Prediction_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Sun_PNP_Robust_Learning_From_Noisy_Labels_by_Probabilistic_Noise_Prediction_CVPR_2022_paper.html | CVPR 2022 | null |
Estimating Structural Disparities for Face Models | Shervin Ardeshir, Cristina Segalin, Nathan Kallus | In machine learning, disparity metrics are often defined by measuring the difference in the performance or outcome of a model, across different sub-populations (groups) of datapoints. Thus, the inputs to disparity quantification consist of a model's predictions y', the ground-truth labels for the predictions y, and group labels g for the data points. Performance of the model for each group is calculated by comparing y' and y for the datapoints within a specific group, and as a result, disparity of performance across the different groups can be calculated. In many real world scenarios however, group labels (g) may not be available at scale during training and validation time, or collecting them might not be feasible or desirable as they could often be sensitive information. As a result, evaluating disparity metrics across categorical groups would not be feasible. On the other hand, in many scenarios noisy groupings may be obtainable using some form of a proxy, which would allow measuring disparity metrics across sub-populations. Here we explore performing such analysis on computer vision models trained on human faces, and on tasks such as face attribute prediction and affect estimation. Our experiments indicate that embeddings resulting from an off-the-shelf face recognition model, could meaningfully serve as a proxy for such estimation. | https://openaccess.thecvf.com/content/CVPR2022/papers/Ardeshir_Estimating_Structural_Disparities_for_Face_Models_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2204.06562 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Ardeshir_Estimating_Structural_Disparities_for_Face_Models_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Ardeshir_Estimating_Structural_Disparities_for_Face_Models_CVPR_2022_paper.html | CVPR 2022 | null |
Revisiting the Transferability of Supervised Pretraining: An MLP Perspective | Yizhou Wang, Shixiang Tang, Feng Zhu, Lei Bai, Rui Zhao, Donglian Qi, Wanli Ouyang | The pretrain-finetune paradigm is a classical pipeline in visual learning. Recent progress on unsupervised pretraining methods shows superior transfer performance to their supervised counterparts. This paper revisits this phenomenon and sheds new light on understanding the transferability gap between unsupervised and supervised pretraining from a multilayer perceptron (MLP) perspective. While previous works focus on the effectiveness of MLP on unsupervised image classification where pretraining and evaluation are conducted on the same dataset, we reveal that the MLP projector is also the key factor to better transferability of unsupervised pretraining methods than supervised pretraining methods. Based on this observation, we attempt to close the transferability gap between supervised and unsupervised pretraining by adding an MLP projector before the classifier in supervised pretraining. Our analysis indicates that the MLP projector can help retain intra-class variation of visual features, decrease the feature distribution distance between pretraining and evaluation datasets, and reduce feature redundancy. Extensive experiments on public benchmarks demonstrate that the added MLP projector significantly boosts the transferability of supervised pretraining, e.g. +7.2% top-1 accuracy on the concept generalization task, +5.8% top-1 accuracy for linear evaluation on 12-domain classification tasks, and +0.8% AP on COCO object detection task, making supervised pretraining comparable or even better than unsupervised pretraining. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_Revisiting_the_Transferability_of_Supervised_Pretraining_An_MLP_Perspective_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wang_Revisiting_the_Transferability_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.00496 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Revisiting_the_Transferability_of_Supervised_Pretraining_An_MLP_Perspective_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Revisiting_the_Transferability_of_Supervised_Pretraining_An_MLP_Perspective_CVPR_2022_paper.html | CVPR 2022 | null |
Plenoxels: Radiance Fields Without Neural Networks | Sara Fridovich-Keil, Alex Yu, Matthew Tancik, Qinhong Chen, Benjamin Recht, Angjoo Kanazawa | We introduce Plenoxels (plenoptic voxels), a system for photorealistic view synthesis. Plenoxels represent a scene as a sparse 3D grid with spherical harmonics. This representation can be optimized from calibrated images via gradient methods and regularization without any neural components. On standard, benchmark tasks, Plenoxels are optimized two orders of magnitude faster than Neural Radiance Fields with no loss in visual quality. For video and code, please see https://alexyu.net/plenoxels. | https://openaccess.thecvf.com/content/CVPR2022/papers/Fridovich-Keil_Plenoxels_Radiance_Fields_Without_Neural_Networks_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Fridovich-Keil_Plenoxels_Radiance_Fields_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Fridovich-Keil_Plenoxels_Radiance_Fields_Without_Neural_Networks_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Fridovich-Keil_Plenoxels_Radiance_Fields_Without_Neural_Networks_CVPR_2022_paper.html | CVPR 2022 | null |
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