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Counterfactual Samples Synthesizing for Robust Visual Question Answering | Long Chen, Xin Yan, Jun Xiao, Hanwang Zhang, Shiliang Pu, Yueting Zhuang | Despite Visual Question Answering (VQA) has realized impressive progress over the last few years, today's VQA models tend to capture superficial linguistic correlations in the train set and fail to generalize to the test set with different QA distributions. To reduce the language biases, several recent works introduce an auxiliary question-only model to regularize the training of targeted VQA model, and achieve dominating performance on VQA-CP. However, since the complexity of design, current methods are unable to equip the ensemble-based models with two indispensable characteristics of an ideal VQA model: 1) visual-explainable: the model should rely on the right visual regions when making decisions. 2) question-sensitive: the model should be sensitive to the linguistic variations in question. To this end, we propose a model-agnostic Counterfactual Samples Synthesizing (CSS) training scheme. The CSS generates numerous counterfactual training samples by masking critical objects in images or words in questions, and assigning different ground-truth answers. After training with the complementary samples (ie, the original and generated samples), the VQA models are forced to focus on all critical objects and words, which significantly improves both visual-explainable and question-sensitive abilities. In return, the performance of these models is further boosted. Extensive ablations have shown the effectiveness of CSS. Particularly, by building on top of the model LMH, we achieve a record-breaking performance of 58.95% on VQA-CP v2, with 6.5% gains. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Chen_Counterfactual_Samples_Synthesizing_for_Robust_Visual_Question_Answering_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.06576 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Chen_Counterfactual_Samples_Synthesizing_for_Robust_Visual_Question_Answering_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Chen_Counterfactual_Samples_Synthesizing_for_Robust_Visual_Question_Answering_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Inter-Region Affinity Distillation for Road Marking Segmentation | Yuenan Hou, Zheng Ma, Chunxiao Liu, Tak-Wai Hui, Chen Change Loy | We study the problem of distilling knowledge from a large deep teacher network to a much smaller student network for the task of road marking segmentation. In this work, we explore a novel knowledge distillation (KD) approach that can transfer 'knowledge' on scene structure more effectively from a teacher to a student model. Our method is known as Inter-Region Affinity KD (IntRA-KD). It decomposes a given road scene image into different regions and represents each region as a node in a graph. An inter-region affinity graph is then formed by establishing pairwise relationships between nodes based on their similarity in feature distribution. To learn structural knowledge from the teacher network, the student is required to match the graph generated by the teacher. The proposed method shows promising results on three large-scale road marking segmentation benchmarks, i.e., ApolloScape, CULane and LLAMAS, by taking various lightweight models as students and ResNet-101 as the teacher. IntRA-KD consistently brings higher performance gains on all lightweight models, compared to previous distillation methods. Our code is available at https://github.com/ cardwing/Codes-for-IntRA-KD. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Hou_Inter-Region_Affinity_Distillation_for_Road_Marking_Segmentation_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.05304 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Hou_Inter-Region_Affinity_Distillation_for_Road_Marking_Segmentation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Hou_Inter-Region_Affinity_Distillation_for_Road_Marking_Segmentation_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Hou_Inter-Region_Affinity_Distillation_CVPR_2020_supplemental.pdf | null | null |
Deformation-Aware Unpaired Image Translation for Pose Estimation on Laboratory Animals | Siyuan Li, Semih Gunel, Mirela Ostrek, Pavan Ramdya, Pascal Fua, Helge Rhodin | Our goal is to capture the pose of real animals using synthetic training examples, without using any manual supervision. Our focus is on neuroscience model organisms, to be able to study how neural circuits orchestrate behaviour. Human pose estimation attains remarkable accuracy when trained on real or simulated datasets consisting of millions of frames. However, for many applications simulated models are unrealistic and real training datasets with comprehensive annotations do not exist. We address this problem with a new sim2real domain transfer method. Our key contribution is the explicit and independent modeling of appearance, shape and pose in an unpaired image translation framework. Our model lets us train a pose estimator on the target domain by transferring readily available body keypoint locations from the source domain to generated target images. We compare our approach with existing domain transfer methods and demonstrate improved pose estimation accuracy on Drosophila melanogaster (fruit fly), Caenorhabditis elegans (worm) and Danio rerio (zebrafish), without requiring any manual annotation on the target domain and despite using simplistic off-the-shelf animal characters for simulation, or simple geometric shapes as models. Our new datasets, code and trained models will be published to support future computer vision and neuroscientific studies. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Li_Deformation-Aware_Unpaired_Image_Translation_for_Pose_Estimation_on_Laboratory_Animals_CVPR_2020_paper.pdf | http://arxiv.org/abs/2001.08601 | https://www.youtube.com/watch?v=be1S4GJgyzY | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Deformation-Aware_Unpaired_Image_Translation_for_Pose_Estimation_on_Laboratory_Animals_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Deformation-Aware_Unpaired_Image_Translation_for_Pose_Estimation_on_Laboratory_Animals_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Li_Deformation-Aware_Unpaired_Image_CVPR_2020_supplemental.pdf | null | null |
Few-Shot Pill Recognition | Suiyi Ling, Andreas Pastor, Jing Li, Zhaohui Che, Junle Wang, Jieun Kim, Patrick Le Callet | Pill image recognition is vital for many personal/public health-care applications and should be robust to diverse unconstrained real-world conditions. Most existing pill recognition models are limited in tackling this challenging few-shot learning problem due to the insufficient instances per category. With limited training data, neural network-based models have limitations in discovering most discriminating features, or going deeper. Especially, existing models fail to handle the hard samples taken under less controlled imaging conditions. In this study, a new pill image database, namely CURE, is first developed with more varied imaging conditions and instances for each pill category. Secondly, a W2-net is proposed for better pill segmentation. Thirdly, a Multi-Stream (MS) deep network that captures task-related features along with a novel two-stage training methodology are proposed. Within the proposed framework, a Batch All strategy that considers all the samples is first employed for the sub-streams, and then a Batch Hard strategy that considers only the hard samples mined in the first stage is utilized for the fusion network. By doing so, complex samples that could not be represented by one type of feature could be focused and the model could be forced to exploit other domain-related information more effectively. Experiment results show that the proposed model outperforms state-of-the-art models on both the National Institute of Health (NIH) and our CURE database. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Ling_Few-Shot_Pill_Recognition_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Ling_Few-Shot_Pill_Recognition_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Ling_Few-Shot_Pill_Recognition_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Ling_Few-Shot_Pill_Recognition_CVPR_2020_supplemental.pdf | null | null |
Learn to Augment: Joint Data Augmentation and Network Optimization for Text Recognition | Canjie Luo, Yuanzhi Zhu, Lianwen Jin, Yongpan Wang | Handwritten text and scene text suffer from various shapes and distorted patterns. Thus training a robust recognition model requires a large amount of data to cover diversity as much as possible. In contrast to data collection and annotation, data augmentation is a low cost way. In this paper, we propose a new method for text image augmentation. Different from traditional augmentation methods such as rotation, scaling and perspective transformation, our proposed augmentation method is designed to learn proper and efficient data augmentation which is more effective and specific for training a robust recognizer. By using a set of custom fiducial points, the proposed augmentation method is flexible and controllable. Furthermore, we bridge the gap between the isolated processes of data augmentation and network optimization by joint learning. An agent network learns from the output of the recognition network and controls the fiducial points to generate more proper training samples for the recognition network. Extensive experiments on various benchmarks, including regular scene text, irregular scene text and handwritten text, show that the proposed augmentation and the joint learning methods significantly boost the performance of the recognition networks. A general toolkit for geometric augmentation is available. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Luo_Learn_to_Augment_Joint_Data_Augmentation_and_Network_Optimization_for_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.06606 | https://www.youtube.com/watch?v=w_gN1NpSYOY | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Luo_Learn_to_Augment_Joint_Data_Augmentation_and_Network_Optimization_for_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Luo_Learn_to_Augment_Joint_Data_Augmentation_and_Network_Optimization_for_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
PointGMM: A Neural GMM Network for Point Clouds | Amir Hertz, Rana Hanocka, Raja Giryes, Daniel Cohen-Or | Point clouds are a popular representation for 3D shapes. However, they encode a particular sampling without accounting for shape priors or non-local information. We advocate for the use of a hierarchical Gaussian mixture model (hGMM), which is a compact, adaptive and lightweight representation that probabilistically defines the underlying 3D surface. We present PointGMM, a neural network that learns to generate hGMMs which are characteristic of the shape class, and also coincide with the input point cloud. PointGMM is trained over a collection of shapes to learn a class-specific prior. The hierarchical representation has two main advantages: (i) coarse-to-fine learning, which avoids converging to poor local-minima; and (ii) (an unsupervised) consistent partitioning of the input shape. We show that as a generative model, PointGMM learns a meaningful latent space which enables generating consistent interpolations between existing shapes, as well as synthesizing novel shapes. We also present a novel framework for rigid registration using PointGMM, that learns to disentangle orientation from structure of an input shape. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Hertz_PointGMM_A_Neural_GMM_Network_for_Point_Clouds_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.13326 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Hertz_PointGMM_A_Neural_GMM_Network_for_Point_Clouds_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Hertz_PointGMM_A_Neural_GMM_Network_for_Point_Clouds_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Hertz_PointGMM_A_Neural_CVPR_2020_supplemental.pdf | null | null |
Weakly Supervised Semantic Point Cloud Segmentation: Towards 10x Fewer Labels | Xun Xu, Gim Hee Lee | Point cloud analysis has received much attention recently; and segmentation is one of the most important tasks. The success of existing approaches is attributed to deep network design and large amount of labelled training data, where the latter is assumed to be always available. However, obtaining 3d point cloud segmentation labels is often very costly in practice. In this work, we propose a weakly supervised point cloud segmentation approach which requires only a tiny fraction of points to be labelled in the training stage. This is made possible by learning gradient approximation and exploitation of additional spatial and color smoothness constraints. Experiments are done on three public datasets with different degrees of weak supervision. In particular, our proposed method can produce results that are close to and sometimes even better than its fully supervised counterpart with 10X fewer labels. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Xu_Weakly_Supervised_Semantic_Point_Cloud_Segmentation_Towards_10x_Fewer_Labels_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.04091 | https://www.youtube.com/watch?v=oK1mn3GQiGc | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Xu_Weakly_Supervised_Semantic_Point_Cloud_Segmentation_Towards_10x_Fewer_Labels_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Xu_Weakly_Supervised_Semantic_Point_Cloud_Segmentation_Towards_10x_Fewer_Labels_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Xu_Weakly_Supervised_Semantic_CVPR_2020_supplemental.pdf | null | null |
CoverNet: Multimodal Behavior Prediction Using Trajectory Sets | Tung Phan-Minh, Elena Corina Grigore, Freddy A. Boulton, Oscar Beijbom, Eric M. Wolff | We present CoverNet, a new method for multimodal, probabilistic trajectory prediction for urban driving. Previous work has employed a variety of methods, including multimodal regression, occupancy maps, and 1-step stochastic policies. We instead frame the trajectory prediction problem as classification over a diverse set of trajectories. The size of this set remains manageable due to the limited number of distinct actions that can be taken over a reasonable prediction horizon. We structure the trajectory set to a) ensure a desired level of coverage of the state space, and b) eliminate physically impossible trajectories. By dynamically generating trajectory sets based on the agent's current state, we can further improve our method's efficiency. We demonstrate our approach on public, real world self-driving datasets, and show that it outperforms state-of-the-art methods. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Phan-Minh_CoverNet_Multimodal_Behavior_Prediction_Using_Trajectory_Sets_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Phan-Minh_CoverNet_Multimodal_Behavior_Prediction_Using_Trajectory_Sets_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Phan-Minh_CoverNet_Multimodal_Behavior_Prediction_Using_Trajectory_Sets_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Phan-Minh_CoverNet_Multimodal_Behavior_CVPR_2020_supplemental.zip | null | null |
Screencast Tutorial Video Understanding | Kunpeng Li, Chen Fang, Zhaowen Wang, Seokhwan Kim, Hailin Jin, Yun Fu | Screencast tutorials are videos created by people to teach how to use software applications or demonstrate procedures for accomplishing tasks. It is very popular for both novice and experienced users to learn new skills, compared to other tutorial media such as text, because of the visual guidance and the ease of understanding. In this paper, we propose visual understanding of screencast tutorials as a new research problem to the computer vision community. We collect a new dataset of Adobe Photoshop video tutorials and annotate it with both low-level and high-level semantic labels. We introduce a bottom-up pipeline to understand Photoshop video tutorials. We leverage state-of-the-art object detection algorithms with domain specific visual cues to detect important events in a video tutorial and segment it into clips according to the detected events. We propose a visual cue reasoning algorithm for two high-level tasks: video retrieval and video captioning. We conduct extensive evaluations of the proposed pipeline. Experimental results show that it is effective in terms of understanding video tutorials. We believe our work will serves as a starting point for future research on this important application domain of video understanding. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Li_Screencast_Tutorial_Video_Understanding_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Screencast_Tutorial_Video_Understanding_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Screencast_Tutorial_Video_Understanding_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Gated Channel Transformation for Visual Recognition | Zongxin Yang, Linchao Zhu, Yu Wu, Yi Yang | In this work, we propose a generally applicable transformation unit for visual recognition with deep convolutional neural networks. This transformation explicitly models channel relationships with explainable control variables. These variables determine the neuron behaviors of competition or cooperation, and they are jointly optimized with the convolutional weight towards more accurate recognition. In Squeeze-and-Excitation (SE) Networks, the channel relationships are implicitly learned by fully connected layers, and the SE block is integrated at the block-level. We instead introduce a channel normalization layer to reduce the number of parameters and computational complexity. This lightweight layer incorporates a simple l2 normalization, enabling our transformation unit applicable to operator-level without much increase of additional parameters. Extensive experiments demonstrate the effectiveness of our unit with clear margins on many vision tasks, i.e., image classification on ImageNet, object detection and instance segmentation on COCO, video classification on Kinetics. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Yang_Gated_Channel_Transformation_for_Visual_Recognition_CVPR_2020_paper.pdf | http://arxiv.org/abs/1909.11519 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Yang_Gated_Channel_Transformation_for_Visual_Recognition_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Yang_Gated_Channel_Transformation_for_Visual_Recognition_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Learning to Measure the Static Friction Coefficient in Cloth Contact | Abdullah Haroon Rasheed, Victor Romero, Florence Bertails-Descoubes, Stefanie Wuhrer, Jean-Sebastien Franco, Arnaud Lazarus | Measuring friction coefficients between cloth and an external body is a longstanding issue in mechanical engineering, never yet addressed with a pure vision-based system. The latter offers the prospect of simpler, less invasive friction measurement protocols compared to traditional ones, and can vastly benefit from recent deep learning advances. Such a novel measurement strategy however proves challenging, as no large labelled dataset for cloth contact exists, and creating one would require thousands of physics workbench measurements with broad coverage of cloth-material pairs. Using synthetic data instead is only possible assuming the availability of a soft-body mechanical simulator with true-to-life friction physics accuracy, yet to be verified. We propose a first vision-based measurement network for friction between cloth and a substrate, using a simple and repeatable video acquisition protocol. We train our network on purely synthetic data generated by a state-of-the-art frictional contact simulator, which we carefully calibrate and validate against real experiments under controlled conditions. We show promising results on a large set of contact pairs between real cloth samples and various kinds of substrates, with 93.6% of all measurements predicted within 0.1 range of standard physics bench measurements. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Rasheed_Learning_to_Measure_the_Static_Friction_Coefficient_in_Cloth_Contact_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Rasheed_Learning_to_Measure_the_Static_Friction_Coefficient_in_Cloth_Contact_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Rasheed_Learning_to_Measure_the_Static_Friction_Coefficient_in_Cloth_Contact_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Rasheed_Learning_to_Measure_CVPR_2020_supplemental.zip | null | null |
Can Deep Learning Recognize Subtle Human Activities? | Vincent Jacquot, Zhuofan Ying, Gabriel Kreiman | Deep Learning has driven recent and exciting progress in computer vision, instilling the belief that these algorithms could solve any visual task. Yet, datasets commonly used to train and test computer vision algorithms have pervasive confounding factors. Such biases make it difficult to truly estimate the performance of those algorithms and how well computer vision models can extrapolate outside the distribution in which they were trained. In this work, we propose a new action classification challenge that is performed well by humans, but poorly by state-of-the-art Deep Learning models. As a proof-of-principle, we consider three exemplary tasks: drinking, reading, and sitting. The best accuracies reached using state-of-the-art computer vision models were 61.7%, 62.8%, and 76.8%, respectively, while human participants scored above 90% accuracy on the three tasks. We propose a rigorous method to reduce confounds when creating datasets, and when comparing human versus computer vision performance. Source code and datasets are publicly available. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Jacquot_Can_Deep_Learning_Recognize_Subtle_Human_Activities_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.13852 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Jacquot_Can_Deep_Learning_Recognize_Subtle_Human_Activities_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Jacquot_Can_Deep_Learning_Recognize_Subtle_Human_Activities_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Jacquot_Can_Deep_Learning_CVPR_2020_supplemental.pdf | https://cove.thecvf.com/datasets/364 | null |
Combating Noisy Labels by Agreement: A Joint Training Method with Co-Regularization | Hongxin Wei, Lei Feng, Xiangyu Chen, Bo An | Deep Learning with noisy labels is a practically challenging problem in weakly-supervised learning. The state-of-the-art approaches "Decoupling" and "Co-teaching+" claim that the "disagreement" strategy is crucial for alleviating the problem of learning with noisy labels. In this paper, we start from a different perspective and propose a robust learning paradigm called JoCoR, which aims to reduce the diversity of two networks during training. Specifically, we first use two networks to make predictions on the same mini-batch data and calculate a joint loss with Co-Regularization for each training example. Then we select small-loss examples to update the parameters of both two networks simultaneously. Trained by the joint loss, these two networks would be more and more similar due to the effect of Co-Regularization. Extensive experimental results on corrupted data from benchmark datasets including MNIST, CIFAR-10, CIFAR-100 and Clothing1M demonstrate that JoCoR is superior to many state-of-the-art approaches for learning with noisy labels. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wei_Combating_Noisy_Labels_by_Agreement_A_Joint_Training_Method_with_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.02752 | https://www.youtube.com/watch?v=Yi8WSdOnBkI | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Wei_Combating_Noisy_Labels_by_Agreement_A_Joint_Training_Method_with_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Wei_Combating_Noisy_Labels_by_Agreement_A_Joint_Training_Method_with_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Wei_Combating_Noisy_Labels_CVPR_2020_supplemental.pdf | null | null |
Superpixel Segmentation With Fully Convolutional Networks | Fengting Yang, Qian Sun, Hailin Jin, Zihan Zhou | In computer vision, superpixels have been widely used as an effective way to reduce the number of image primitives for subsequent processing. But only a few attempts have been made to incorporate them into deep neural networks. One main reason is that the standard convolution operation is defined on regular grids and becomes inefficient when applied to superpixels. Inspired by an initialization strategy commonly adopted by traditional superpixel algorithms, we present a novel method that employs a simple fully convolutional network to predict superpixels on a regular image grid. Experimental results on benchmark datasets show that our method achieves state-of-the-art superpixel segmentation performance while running at about 50fps. Based on the predicted superpixels, we further develop a downsampling/upsampling scheme for deep networks with the goal of generating high-resolution outputs for dense prediction tasks. Specifically, we modify a popular network architecture for stereo matching to simultaneously predict superpixels and disparities. We show that improved disparity estimation accuracy can be obtained on public datasets. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Yang_Superpixel_Segmentation_With_Fully_Convolutional_Networks_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.12929 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Yang_Superpixel_Segmentation_With_Fully_Convolutional_Networks_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Yang_Superpixel_Segmentation_With_Fully_Convolutional_Networks_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Yang_Superpixel_Segmentation_With_CVPR_2020_supplemental.pdf | null | null |
ContourNet: Taking a Further Step Toward Accurate Arbitrary-Shaped Scene Text Detection | Yuxin Wang, Hongtao Xie, Zheng-Jun Zha, Mengting Xing, Zilong Fu, Yongdong Zhang | Scene text detection has witnessed rapid development in recent years. However, there still exists two main challenges: 1) many methods suffer from false positives in their text representations; 2) the large scale variance of scene texts makes it hard for network to learn samples. In this paper, we propose the ContourNet, which effectively handles these two problems taking a further step toward accurate arbitrary-shaped text detection. At first, a scale-insensitive Adaptive Region Proposal Network (Adaptive-RPN) is proposed to generate text proposals by only focusing on the Intersection over Union (IoU) values between predicted and ground-truth bounding boxes. Then a novel Local Orthogonal Texture-aware Module (LOTM) models the local texture information of proposal features in two orthogonal directions and represents text region with a set of contour points. Considering that the strong unidirectional or weakly orthogonal activation is usually caused by the monotonous texture characteristic of false-positive patterns (e.g. streaks.), our method effectively suppresses these false positives by only outputting predictions with high response value in both orthogonal directions. This gives more accurate description of text regions. Extensive experiments on three challenging datasets (Total-Text, CTW1500 and ICDAR2015) verify that our method achieves the state-of-the-art performance. Code is available at https://github.com/wangyuxin87/ContourNet. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wang_ContourNet_Taking_a_Further_Step_Toward_Accurate_Arbitrary-Shaped_Scene_Text_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.04940 | https://www.youtube.com/watch?v=8XZeNOOzAFQ | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_ContourNet_Taking_a_Further_Step_Toward_Accurate_Arbitrary-Shaped_Scene_Text_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_ContourNet_Taking_a_Further_Step_Toward_Accurate_Arbitrary-Shaped_Scene_Text_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Wang_ContourNet_Taking_a_CVPR_2020_supplemental.pdf | null | null |
Optimal least-squares solution to the hand-eye calibration problem | Amit Dekel, Linus Harenstam-Nielsen, Sergio Caccamo | We propose a least-squares formulation to the noisy hand-eye calibration problem using dual-quaternions, and introduce efficient algorithms to find the exact optimal solution, based on analytic properties of the problem, avoiding non-linear optimization. We further present simple analytic approximate solutions which provide remarkably good estimations compared to the exact solution. In addition, we show how to generalize our solution to account for a given extrinsic prior in the cost function. To the best of our knowledge our algorithm is the most efficient approach to optimally solve the hand-eye calibration problem. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Dekel_Optimal_least-squares_solution_to_the_hand-eye_calibration_problem_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Dekel_Optimal_least-squares_solution_to_the_hand-eye_calibration_problem_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Dekel_Optimal_least-squares_solution_to_the_hand-eye_calibration_problem_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Dekel_Optimal_least-squares_solution_CVPR_2020_supplemental.pdf | null | null |
Uncertainty-Aware CNNs for Depth Completion: Uncertainty from Beginning to End | Abdelrahman Eldesokey, Michael Felsberg, Karl Holmquist, Michael Persson | The focus in deep learning research has been mostly to push the limits of prediction accuracy. However, this was often achieved at the cost of increased complexity, raising concerns about the interpretability and the reliability of deep networks. Recently, an increasing attention has been given to untangling the complexity of deep networks and quantifying their uncertainty for different computer vision tasks. Differently, the task of depth completion has not received enough attention despite the inherent noisy nature of depth sensors. In this work, we thus focus on modeling the uncertainty of depth data in depth completion starting from the sparse noisy input all the way to the final prediction. We propose a novel approach to identify disturbed measurements in the input by learning an input confidence estimator in a self-supervised manner based on the normalized convolutional neural networks (NCNNs). Further, we propose a probabilistic version of NCNNs that produces a statistically meaningful uncertainty measure for the final prediction. When we evaluate our approach on the KITTI dataset for depth completion, we outperform all the existing Bayesian Deep Learning approaches in terms of prediction accuracy, quality of the uncertainty measure, and the computational efficiency. Moreover, our small network with 670k parameters performs on-par with conventional approaches with millions of parameters. These results give strong evidence that separating the network into parallel uncertainty and prediction streams leads to state-of-the-art performance with accurate uncertainty estimates. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Eldesokey_Uncertainty-Aware_CNNs_for_Depth_Completion_Uncertainty_from_Beginning_to_End_CVPR_2020_paper.pdf | http://arxiv.org/abs/2006.03349 | https://www.youtube.com/watch?v=nN4f_omztwY | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Eldesokey_Uncertainty-Aware_CNNs_for_Depth_Completion_Uncertainty_from_Beginning_to_End_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Eldesokey_Uncertainty-Aware_CNNs_for_Depth_Completion_Uncertainty_from_Beginning_to_End_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Eldesokey_Uncertainty-Aware_CNNs_for_CVPR_2020_supplemental.pdf | null | null |
Learning From Web Data With Self-Organizing Memory Module | Yi Tu, Li Niu, Junjie Chen, Dawei Cheng, Liqing Zhang | Learning from web data has attracted lots of research interest in recent years. However, crawled web images usually have two types of noises, label noise and background noise, which induce extra difficulties in utilizing them effectively. Most existing methods either rely on human supervision or ignore the background noise. In this paper, we propose a novel method, which is capable of handling these two types of noises together, without the supervision of clean images in the training stage. Particularly, we formulate our method under the framework of multi-instance learning by grouping ROIs (i.e., images and their region proposals) from the same category into bags. ROIs in each bag are assigned with different weights based on the representative/discriminative scores of their nearest clusters, in which the clusters and their scores are obtained via our designed memory module. Our memory module could be naturally integrated with the classification module, leading to an end-to-end trainable system. Extensive experiments on four benchmark datasets demonstrate the effectiveness of our method. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Tu_Learning_From_Web_Data_With_Self-Organizing_Memory_Module_CVPR_2020_paper.pdf | http://arxiv.org/abs/1906.12028 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Tu_Learning_From_Web_Data_With_Self-Organizing_Memory_Module_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Tu_Learning_From_Web_Data_With_Self-Organizing_Memory_Module_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Tu_Learning_From_Web_CVPR_2020_supplemental.pdf | null | null |
Overcoming Classifier Imbalance for Long-Tail Object Detection With Balanced Group Softmax | Yu Li, Tao Wang, Bingyi Kang, Sheng Tang, Chunfeng Wang, Jintao Li, Jiashi Feng | Solving long-tail large vocabulary object detection with deep learning based models is a challenging and demanding task, which is however under-explored. In this work, we provide the first systematic analysis on the underperformance of state-of-the-art models in front of long-tail distribution. We find existing detection methods are unable to model few-shot classes when the dataset is extremely skewed, which can result in classifier imbalance in terms of parameter magnitude. Directly adapting long-tail classification models to detection frameworks can not solve this problem due to the intrinsic difference between detection and classification. In this work, we propose a novel balanced group softmax (BAGS) module for balancing the classifiers within the detection frameworks through group-wise training. It implicitly modulates the training process for the head and tail classes and ensures they are both sufficiently trained, without requiring any extra sampling for the instances from the tail classes. Extensive experiments on the very recent long-tail large vocabulary object recognition benchmark LVIS show that our proposed BAGS significantly improves the performance of detectors with various backbones and frameworks on both object detection and instance segmentation. It beats all state-of-the-art methods transferred from long-tail image classification and establishes new state-of-the-art. Code is available at https://github.com/FishYuLi/BalancedGroupSoftmax. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Li_Overcoming_Classifier_Imbalance_for_Long-Tail_Object_Detection_With_Balanced_Group_CVPR_2020_paper.pdf | http://arxiv.org/abs/2006.10408 | https://www.youtube.com/watch?v=Lp72nHceTZQ | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Overcoming_Classifier_Imbalance_for_Long-Tail_Object_Detection_With_Balanced_Group_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Overcoming_Classifier_Imbalance_for_Long-Tail_Object_Detection_With_Balanced_Group_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Li_Overcoming_Classifier_Imbalance_CVPR_2020_supplemental.pdf | null | null |
Hierarchical Scene Coordinate Classification and Regression for Visual Localization | Xiaotian Li, Shuzhe Wang, Yi Zhao, Jakob Verbeek, Juho Kannala | Visual localization is critical to many applications in computer vision and robotics. To address single-image RGB localization, state-of-the-art feature-based methods match local descriptors between a query image and a pre-built 3D model. Recently, deep neural networks have been exploited to regress the mapping between raw pixels and 3D coordinates in the scene, and thus the matching is implicitly performed by the forward pass through the network. However, in a large and ambiguous environment, learning such a regression task directly can be difficult for a single network. In this work, we present a new hierarchical scene coordinate network to predict pixel scene coordinates in a coarse-to-fine manner from a single RGB image. The network consists of a series of output layers, each of them conditioned on the previous ones. The final output layer predicts the 3D coordinates and the others produce progressively finer discrete location labels. The proposed method outperforms the baseline regression-only network and allows us to train compact models which scale robustly to large environments. It sets a new state-of-the-art for single-image RGB localization performance on the 7-Scenes, 12-Scenes, Cambridge Landmarks datasets, and three combined scenes. Moreover, for large-scale outdoor localization on the Aachen Day-Night dataset, we present a hybrid approach which outperforms existing scene coordinate regression methods, and reduces significantly the performance gap w.r.t. explicit feature matching methods. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Li_Hierarchical_Scene_Coordinate_Classification_and_Regression_for_Visual_Localization_CVPR_2020_paper.pdf | http://arxiv.org/abs/1909.06216 | https://www.youtube.com/watch?v=bbJRag3wMfE | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Hierarchical_Scene_Coordinate_Classification_and_Regression_for_Visual_Localization_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Hierarchical_Scene_Coordinate_Classification_and_Regression_for_Visual_Localization_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Li_Hierarchical_Scene_Coordinate_CVPR_2020_supplemental.pdf | null | null |
Symmetry and Group in Attribute-Object Compositions | Yong-Lu Li, Yue Xu, Xiaohan Mao, Cewu Lu | Attributes and objects can compose diverse compositions. To model the compositional nature of these general concepts, it is a good choice to learn them through transformations, such as coupling and decoupling. However, complex transformations need to satisfy specific principles to guarantee the rationality. In this paper, we first propose a previously ignored principle of attribute-object transformation: Symmetry. For example, coupling peeled-apple with attribute peeled should result in peeled-apple, and decoupling peeled from apple should still output apple. Incorporating the symmetry principle, a transformation framework inspired by group theory is built, i.e. SymNet. SymNet consists of two modules, Coupling Network and Decoupling Network. With the group axioms and symmetry property as objectives, we adopt Deep Neural Networks to implement SymNet and train it in an end-to-end paradigm. Moreover, we propose a Relative Moving Distance (RMD) based recognition method to utilize the attribute change instead of the attribute pattern itself to classify attributes. Our symmetry learning can be utilized for the Compositional Zero-Shot Learning task and outperforms the state-of-the-art on widely-used benchmarks. Code is available at https://github.com/DirtyHarryLYL/SymNet. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Li_Symmetry_and_Group_in_Attribute-Object_Compositions_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.00587 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Symmetry_and_Group_in_Attribute-Object_Compositions_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Symmetry_and_Group_in_Attribute-Object_Compositions_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
SurfelGAN: Synthesizing Realistic Sensor Data for Autonomous Driving | Zhenpei Yang, Yuning Chai, Dragomir Anguelov, Yin Zhou, Pei Sun, Dumitru Erhan, Sean Rafferty, Henrik Kretzschmar | Autonomous driving system development is critically dependent on the ability to replay complex and diverse traffic scenarios in simulation. In such scenarios, the ability to accurately simulate the vehicle sensors such as cameras, lidar or radar is hugely helpful. However, current sensor simulators leverage gaming engines such as Unreal or Unity, requiring manual creation of environments, objects, and material properties. Such approaches have limited scalability and fail to produce realistic approximations of camera, lidar, and radar data without significant additional work. In this paper, we present a simple yet effective approach to generate realistic scenario sensor data, based only on a limited amount of lidar and camera data collected by an autonomous vehicle. Our approach uses texture-mapped surfels to efficiently reconstruct the scene from an initial vehicle pass or set of passes, preserving rich information about object 3D geometry and appearance, as well as the scene conditions. We then leverage a SurfelGAN network to reconstruct realistic camera images for novel positions and orientations of the self-driving vehicle and moving objects in the scene. We demonstrate our approach on the Waymo Open Dataset and show that it can synthesize realistic camera data for simulated scenarios. We also create a novel dataset that contains cases in which two self-driving vehicles observe the same scene at the same time. We use this dataset to provide additional evaluation and demonstrate the usefulness of our SurfelGAN model. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Yang_SurfelGAN_Synthesizing_Realistic_Sensor_Data_for_Autonomous_Driving_CVPR_2020_paper.pdf | http://arxiv.org/abs/2005.03844 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Yang_SurfelGAN_Synthesizing_Realistic_Sensor_Data_for_Autonomous_Driving_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Yang_SurfelGAN_Synthesizing_Realistic_Sensor_Data_for_Autonomous_Driving_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Yang_SurfelGAN_Synthesizing_Realistic_CVPR_2020_supplemental.pdf | null | null |
What Machines See Is Not What They Get: Fooling Scene Text Recognition Models With Adversarial Text Images | Xing Xu, Jiefu Chen, Jinhui Xiao, Lianli Gao, Fumin Shen, Heng Tao Shen | The research on scene text recognition (STR) has made remarkable progress in recent years with the development of deep neural networks (DNNs). Recent studies on adversarial attack have verified that a DNN model designed for non-sequential tasks (e.g., classification, segmentation and retrieval) can be easily fooled by adversarial examples. Actually, STR is an application highly related to security issues. However, there are few studies considering the safety and reliability of STR models that make sequential prediction. In this paper, we make the first attempt in attacking the state-of-the-art DNN-based STR models. Specifically, we propose a novel and efficient optimization-based method that can be naturally integrated to different sequential prediction schemes, i.e., connectionist temporal classification (CTC) and attention mechanism. We apply our proposed method to five state-of-the-art STR models with both targeted and untargeted attack modes, the comprehensive results on 7 real-world datasets and 2 synthetic datasets consistently show the vulnerability of these STR models with a significant performance drop. Finally, we also test our attack method on a real-world STR engine of Baidu OCR, which demonstrates the practical potentials of our method. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Xu_What_Machines_See_Is_Not_What_They_Get_Fooling_Scene_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=yRp59Zi-XX4 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Xu_What_Machines_See_Is_Not_What_They_Get_Fooling_Scene_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Xu_What_Machines_See_Is_Not_What_They_Get_Fooling_Scene_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Learning to Learn Single Domain Generalization | Fengchun Qiao, Long Zhao, Xi Peng | We are concerned with a worst-case scenario in model generalization, in the sense that a model aims to perform well on many unseen domains while there is only one single domain available for training. We propose a new method named adversarial domain augmentation to solve this Out-of-Distribution (OOD) generalization problem. The key idea is to leverage adversarial training to create "fictitious" yet "challenging" populations, from which a model can learn to generalize with theoretical guarantees. To facilitate fast and desirable domain augmentation, we cast the model training in a meta-learning scheme and use a Wasserstein Auto-Encoder (WAE) to relax the widely used worst-case constraint. Detailed theoretical analysis is provided to testify our formulation, while extensive experiments on multiple benchmark datasets indicate its superior performance in tackling single domain generalization. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Qiao_Learning_to_Learn_Single_Domain_Generalization_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.13216 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Qiao_Learning_to_Learn_Single_Domain_Generalization_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Qiao_Learning_to_Learn_Single_Domain_Generalization_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Qiao_Learning_to_Learn_CVPR_2020_supplemental.pdf | null | null |
Warp to the Future: Joint Forecasting of Features and Feature Motion | Josip Saric, Marin Orsic, Tonci Antunovic, Sacha Vrazic, Sinisa Segvic | We address anticipation of scene development by forecasting semantic segmentation of future frames. Several previous works approach this problem by F2F (feature-to-feature) forecasting where future features are regressed from observed features. Different from previous work, we consider a novel F2M (feature-to-motion) formulation, which performs the forecast by warping observed features according to regressed feature flow. This formulation models a causal relationship between the past and the future, and regularizes inference by reducing dimensionality of the forecasting target. However, emergence of future scenery which was not visible in observed frames can not be explained by warping. We propose to address this issue by complementing F2M forecasting with the classic F2F approach. We realize this idea as a multi-head F2MF model built atop shared features. Experiments show that the F2M head prevails in static parts of the scene while the F2F head kicks-in to fill-in the novel regions. The proposed F2MF model operates in synergy with correlation features and outperforms all previous approaches both in short-term and mid-term forecast on the Cityscapes dataset. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Saric_Warp_to_the_Future_Joint_Forecasting_of_Features_and_Feature_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Saric_Warp_to_the_Future_Joint_Forecasting_of_Features_and_Feature_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Saric_Warp_to_the_Future_Joint_Forecasting_of_Features_and_Feature_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Saric_Warp_to_the_CVPR_2020_supplemental.pdf | null | null |
Action Genome: Actions As Compositions of Spatio-Temporal Scene Graphs | Jingwei Ji, Ranjay Krishna, Li Fei-Fei, Juan Carlos Niebles | Action recognition has typically treated actions and activities as monolithic events that occur in videos. However, there is evidence from Cognitive Science and Neuroscience that people actively encode activities into consistent hierarchical part structures. However, in Computer Vision, few explorations on representations that encode event partonomies have been made. Inspired by evidence that the prototypical unit of an event is an action-object interaction, we introduce Action Genome, a representation that decomposes actions into spatio-temporal scene graphs. Action Genome captures changes between objects and their pairwise relationships while an action occurs. It contains 10K videos with 0.4M objects and 1.7M visual relationships annotated. With Action Genome, we extend an existing action recognition model by incorporating scene graphs as spatio-temporal feature banks to achieve better performance on the Charades dataset. Next, by decomposing and learning the temporal changes in visual relationships that result in an action, we demonstrate the utility of a hierarchical event decomposition by enabling few-shot action recognition, achieving 42.7% mAP using as few as 10 examples. Finally, we benchmark existing scene graph models on the new task of spatio-temporal scene graph prediction. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Ji_Action_Genome_Actions_As_Compositions_of_Spatio-Temporal_Scene_Graphs_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Ji_Action_Genome_Actions_As_Compositions_of_Spatio-Temporal_Scene_Graphs_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Ji_Action_Genome_Actions_As_Compositions_of_Spatio-Temporal_Scene_Graphs_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Speech2Action: Cross-Modal Supervision for Action Recognition | Arsha Nagrani, Chen Sun, David Ross, Rahul Sukthankar, Cordelia Schmid, Andrew Zisserman | Is it possible to guess human action from dialogue alone? In this work we investigate the link between spoken words and actions in movies. We note that movie screenplays describe actions, as well as contain the speech of characters and hence can be used to learn this correlation with no additional supervision. We train a BERT-based Speech2Action classifier on over a thousand movie screenplays, to predict action labels from transcribed speech segments. We then apply this model to the speech segments of a large unlabelled movie corpus (188M speech segments from 288K movies). Using the predictions of this model, we obtain weak action labels for over 800K video clips. By training on these video clips, we demonstrate superior action recognition performance on standard action recognition benchmarks, without using a single manually labelled action example. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Nagrani_Speech2Action_Cross-Modal_Supervision_for_Action_Recognition_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.13594 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Nagrani_Speech2Action_Cross-Modal_Supervision_for_Action_Recognition_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Nagrani_Speech2Action_Cross-Modal_Supervision_for_Action_Recognition_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Nagrani_Speech2Action_Cross-Modal_Supervision_CVPR_2020_supplemental.pdf | null | null |
Learning to Cluster Faces via Confidence and Connectivity Estimation | Lei Yang, Dapeng Chen, Xiaohang Zhan, Rui Zhao, Chen Change Loy, Dahua Lin | Face clustering is an essential tool for exploiting the unlabeled face data, and has a wide range of applications including face annotation and retrieval. Recent works show that supervised clustering can result in noticeable performance gain. However, they usually involve heuristic steps and require numerous overlapped subgraphs, severely restricting their accuracy and efficiency. In this paper, we propose a fully learnable clustering framework without requiring a large number of overlapped subgraphs. Instead, we transform the clustering problem into two sub-problems. Specifically, two graph convolutional networks, named GCN-V and GCN-E, are designed to estimate the confidence of vertices and the connectivity of edges, respectively. With the vertex confidence and edge connectivity, we can naturally organize more relevant vertices on the affinity graph and group them into clusters. Experiments on two large-scale benchmarks show that our method significantly improves clustering accuracy and thus performance of the recognition models trained on top, yet it is an order of magnitude more efficient than existing supervised methods. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Yang_Learning_to_Cluster_Faces_via_Confidence_and_Connectivity_Estimation_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.00445 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Yang_Learning_to_Cluster_Faces_via_Confidence_and_Connectivity_Estimation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Yang_Learning_to_Cluster_Faces_via_Confidence_and_Connectivity_Estimation_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Yang_Learning_to_Cluster_CVPR_2020_supplemental.pdf | null | null |
Rethinking Performance Estimation in Neural Architecture Search | Xiawu Zheng, Rongrong Ji, Qiang Wang, Qixiang Ye, Zhenguo Li, Yonghong Tian, Qi Tian | Neural architecture search (NAS) remains a challenging problem, which is attributed to the indispensable and time-consuming component of performance estimation (PE). In this paper, we provide a novel yet systematic rethinking of PE in a resource constrained regime, termed budgeted PE (BPE), which precisely and effectively estimates the performance of an architecture sampled from an architecture space. Since searching an optimal BPE is extremely time-consuming as it requires to train a large number of networks for evaluation, we propose a Minimum Importance Pruning (MIP) approach. Given a dataset and a BPE search space, MIP estimates the importance of hyper-parameters using random forest and subsequently prunes the minimum one from the next iteration. In this way, MIP effectively prunes less important hyper-parameters to allocate more computational resource on more important ones, thus achieving an effective exploration. By combining BPE with various search algorithms including reinforcement learning, evolution algorithm, random search, and differentiable architecture search, we achieve 1, 000x of NAS speed up with a negligible performance drop comparing to the SOTA. All the NAS search codes are available at: https: //github.com/zhengxiawu/rethinking_performance_ estimation_in_NAS | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zheng_Rethinking_Performance_Estimation_in_Neural_Architecture_Search_CVPR_2020_paper.pdf | http://arxiv.org/abs/2005.09917 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zheng_Rethinking_Performance_Estimation_in_Neural_Architecture_Search_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zheng_Rethinking_Performance_Estimation_in_Neural_Architecture_Search_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Revisiting the Sibling Head in Object Detector | Guanglu Song, Yu Liu, Xiaogang Wang | The "shared head for classification and localization" (sibling head), firstly denominated in Fast RCNN, has been leading the fashion of the object detection community in the past five years. This paper provides the observation that the spatial misalignment between the two object functions in the sibling head can considerably hurt the training process, but this misalignment can be resolved by a very simple operator called task-aware spatial disentanglement (TSD). Considering the classification and regression, TSD decouples them from the spatial dimension by generating two disentangled proposals for them, which are estimated by the shared proposal. This is inspired by the natural insight that for one instance, the features in some salient area may have rich information for classification while these around the boundary may be good at bounding box regression. Surprisingly, this simple design can boost all backbones and models on both MS COCO and Google OpenImage consistently by 3% mAP. Further, we propose a progressive constraint to enlarge the performance margin between the disentangled and the shared proposals, and gain 1% more mAP. We show the TSD breaks through the upper bound of nowadays single-model detector by a large margin (mAP 49.4 with ResNet-101, 51.2 with SENet154), and is the core model of our 1st place solution on the Google OpenImage Challenge 2019. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Song_Revisiting_the_Sibling_Head_in_Object_Detector_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.07540 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Song_Revisiting_the_Sibling_Head_in_Object_Detector_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Song_Revisiting_the_Sibling_Head_in_Object_Detector_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
EcoNAS: Finding Proxies for Economical Neural Architecture Search | Dongzhan Zhou, Xinchi Zhou, Wenwei Zhang, Chen Change Loy, Shuai Yi, Xuesen Zhang, Wanli Ouyang | Neural Architecture Search (NAS) achieves significant progress in many computer vision tasks. While many methods are proposed to improve the efficiency of NAS, the search progress is still laborious because training and evaluating plausible architectures over large search space is time-consuming. Assessing network candidates under a proxy (i.e., computationally reduced setting) thus becomes inevitable. In this paper, we observe that most existing proxies exhibit different behaviors in maintaining the rank consistency among network candidates. In particular, some proxies can be more reliable - the rank of candidates does not differ much comparing their reduced setting performance and final performance. In this paper, we systematically investigate some widely adopted reduction factors and report our observations. Inspired by these observations, we present a reliable proxy and further formulate a hierarchical proxy strategy that spends more computations on candidate networks that are potentially more accurate, while discards unpromising ones in early stage with a fast proxy. This leads to an economical evolutionary-based NAS (EcoNAS), which achieves an impressive 400xsearch time reduction in comparison to the evolutionary-based state of the art [19] (8 v.s. 3150 GPU days). Some new proxies led by our observations can also be applied to accelerate other NAS methods while still able to discover good candidate networks with performance matching those found by previous proxy strategies. Codes and models will be released to facilitate future research. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zhou_EcoNAS_Finding_Proxies_for_Economical_Neural_Architecture_Search_CVPR_2020_paper.pdf | http://arxiv.org/abs/2001.01233 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhou_EcoNAS_Finding_Proxies_for_Economical_Neural_Architecture_Search_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhou_EcoNAS_Finding_Proxies_for_Economical_Neural_Architecture_Search_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Zhou_EcoNAS_Finding_Proxies_CVPR_2020_supplemental.pdf | null | null |
Norm-Aware Embedding for Efficient Person Search | Di Chen, Shanshan Zhang, Jian Yang, Bernt Schiele | Person Search is a practically relevant task that aims to jointly solve Person Detection and Person Re-identification (re-ID). Specifically, it requires to find and locate all instances with the same identity as the query person in a set of panoramic gallery images. One major challenge comes from the contradictory goals of the two sub-tasks, i.e., person detection focuses on finding the commonness of all persons while person re-ID handles the differences among multiple identities. Therefore, it is crucial to reconcile the relationship between the two sub-tasks in a joint person search model. To this end, We present a novel approach called Norm-Aware Embedding to disentangle the person embedding into norm and angle for detection and re-ID respectively, allowing for both effective and efficient multi-task training. We further extend the proposal-level person embedding to pixel-level, whose discrimination ability is less affected by mis-alignment. We outperform other one-step methods by a large margin and achieve comparable performance to two-step methods on both CUHK-SYSU and PRW. Also, Our method is easy to train and resource-friendly, running at 12 fps on a single GPU. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Chen_Norm-Aware_Embedding_for_Efficient_Person_Search_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Chen_Norm-Aware_Embedding_for_Efficient_Person_Search_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Chen_Norm-Aware_Embedding_for_Efficient_Person_Search_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Chen_Norm-Aware_Embedding_for_CVPR_2020_supplemental.zip | null | null |
Syntax-Aware Action Targeting for Video Captioning | Qi Zheng, Chaoyue Wang, Dacheng Tao | Existing methods on video captioning have made great efforts to identify objects/instances in videos, but few of them emphasize the prediction of action. As a result, the learned models are likely to depend heavily on the prior of training data, such as the co-occurrence of objects, which may cause an enormous divergence between the generated descriptions and the video content. In this paper, we explicitly emphasize the importance of action by predicting visually-related syntax components including subject, object and predicate. Specifically, we propose a Syntax-Aware Action Targeting (SAAT) module that firstly builds a self-attended scene representation to draw global dependence among multiple objects within a scene, and then decodes the visually-related syntax components by setting different queries. After targeting the action, indicated by predicate, our captioner learns an attention distribution over the predicate and the previously predicted words to guide the generation of the next word. Comprehensive experiments on MSVD and MSR-VTT datasets demonstrate the efficacy of the proposed model. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zheng_Syntax-Aware_Action_Targeting_for_Video_Captioning_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zheng_Syntax-Aware_Action_Targeting_for_Video_Captioning_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zheng_Syntax-Aware_Action_Targeting_for_Video_Captioning_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Zheng_Syntax-Aware_Action_Targeting_CVPR_2020_supplemental.pdf | null | null |
On Vocabulary Reliance in Scene Text Recognition | Zhaoyi Wan, Jielei Zhang, Liang Zhang, Jiebo Luo, Cong Yao | The pursuit of high performance on public benchmarks has been the driving force for research in scene text recognition, and notable progresses have been achieved. However, a close investigation reveals a startling fact that the state-of-the-art methods perform well on images with words within vocabulary but generalize poorly to images with words outside vocabulary. We call this phenomenon "vocabulary reliance". In this paper, we establish an analytical framework, in which different datasets, metrics and module combinations for quantitative comparisons are devised, to conduct an in-depth study on the problem of vocabulary reliance in scene text recognition. Key findings include: (1) Vocabulary reliance is ubiquitous, i.e., all existing algorithms more or less exhibit such characteristic; (2) Attention-based decoders prove weak in generalizing to words outside vocabulary and segmentation-based decoders perform well in utilizing visual features; (3) Context modeling is highly coupled with the prediction layers. These findings provide new insights and can benefit future research in scene text recognition. Furthermore, we propose a simple yet effective mutual learning strategy to allow models of two families (attention-based and segmentation-based) to learn collaboratively. This remedy alleviates the problem of vocabulary reliance and significantly improves the overall scene text recognition performance. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wan_On_Vocabulary_Reliance_in_Scene_Text_Recognition_CVPR_2020_paper.pdf | http://arxiv.org/abs/2005.03959 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Wan_On_Vocabulary_Reliance_in_Scene_Text_Recognition_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Wan_On_Vocabulary_Reliance_in_Scene_Text_Recognition_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Imitative Non-Autoregressive Modeling for Trajectory Forecasting and Imputation | Mengshi Qi, Jie Qin, Yu Wu, Yi Yang | Trajectory forecasting and imputation are pivotal steps towards understanding the movement of human and objects, which are quite challenging since the future trajectories and missing values in a temporal sequence are full of uncertainties, and the spatial-temporally contextual correlation is hard to model. Yet, the relevance between sequence prediction and imputation is disregarded by existing approaches. To this end, we propose a novel imitative non-autoregressive modeling method to simultaneously handle the trajectory prediction task and the missing value imputation task. Specifically, our framework adopts an imitation learning paradigm, which contains a recurrent conditional variational autoencoder (RC-VAE) as a demonstrator, and a non-autoregressive transformation model (NART) as a learner. By jointly optimizing the two models, RC-VAE can predict the future trajectory and capture the temporal relationship in the sequence to supervise the NART learner. As a result, NART learns from the demonstrator and imputes the missing value in a non autoregressive strategy. We conduct extensive experiments on three popular datasets, and the results show that our model achieves state-of-the-art performance across all the datasets. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Qi_Imitative_Non-Autoregressive_Modeling_for_Trajectory_Forecasting_and_Imputation_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=0eiXmWGDJNs | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Qi_Imitative_Non-Autoregressive_Modeling_for_Trajectory_Forecasting_and_Imputation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Qi_Imitative_Non-Autoregressive_Modeling_for_Trajectory_Forecasting_and_Imputation_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Hi-CMD: Hierarchical Cross-Modality Disentanglement for Visible-Infrared Person Re-Identification | Seokeon Choi, Sumin Lee, Youngeun Kim, Taekyung Kim, Changick Kim | Visible-infrared person re-identification (VI-ReID) is an important task in night-time surveillance applications, since visible cameras are difficult to capture valid appearance information under poor illumination conditions. Compared to traditional person re-identification that handles only the intra-modality discrepancy, VI-ReID suffers from additional cross-modality discrepancy caused by different types of imaging systems. To reduce both intra- and cross-modality discrepancies, we propose a Hierarchical Cross-Modality Disentanglement (Hi-CMD) method, which automatically disentangles ID-discriminative factors and ID-excluded factors from visible-thermal images. We only use ID-discriminative factors for robust cross-modality matching without ID-excluded factors such as pose or illumination. To implement our approach, we introduce an ID-preserving person image generation network and a hierarchical feature learning module. Our generation network learns the disentangled representation by generating a new cross-modality image with different poses and illuminations while preserving a person's identity. At the same time, the feature learning module enables our model to explicitly extract the common ID-discriminative characteristic between visible-infrared images. Extensive experimental results demonstrate that our method outperforms the state-of-the-art methods on two VI-ReID datasets. The source code is available at: https://github.com/bismex/HiCMD. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Choi_Hi-CMD_Hierarchical_Cross-Modality_Disentanglement_for_Visible-Infrared_Person_Re-Identification_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=mZ5Yty8krCI | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Choi_Hi-CMD_Hierarchical_Cross-Modality_Disentanglement_for_Visible-Infrared_Person_Re-Identification_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Choi_Hi-CMD_Hierarchical_Cross-Modality_Disentanglement_for_Visible-Infrared_Person_Re-Identification_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Choi_Hi-CMD_Hierarchical_Cross-Modality_CVPR_2020_supplemental.pdf | null | null |
Say As You Wish: Fine-Grained Control of Image Caption Generation With Abstract Scene Graphs | Shizhe Chen, Qin Jin, Peng Wang, Qi Wu | Humans are able to describe image contents with coarse to fine details as they wish. However, most image captioning models are intention-agnostic which cannot generate diverse descriptions according to different user intentions initiatively. In this work, we propose the Abstract Scene Graph (ASG) structure to represent user intention in fine-grained level and control what and how detailed the generated description should be. The ASG is a directed graph consisting of three types of abstract nodes (object, attribute, relationship) grounded in the image without any concrete semantic labels. Thus it is easy to obtain either manually or automatically. From the ASG, we propose a novel ASG2Caption model, which is able to recognise user intentions and semantics in the graph, and therefore generate desired captions following the graph structure. Our model achieves better controllability conditioning on ASGs than carefully designed baselines on both VisualGenome and MSCOCO datasets. It also significantly improves the caption diversity via automatically sampling diverse ASGs as control signals. Code will be released at https://github.com/cshizhe/asg2cap. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Chen_Say_As_You_Wish_Fine-Grained_Control_of_Image_Caption_Generation_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.00387 | https://www.youtube.com/watch?v=CnFmQ8OZ-Ys | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Chen_Say_As_You_Wish_Fine-Grained_Control_of_Image_Caption_Generation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Chen_Say_As_You_Wish_Fine-Grained_Control_of_Image_Caption_Generation_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Chen_Say_As_You_CVPR_2020_supplemental.pdf | null | null |
TESA: Tensor Element Self-Attention via Matricization | Francesca Babiloni, Ioannis Marras, Gregory Slabaugh, Stefanos Zafeiriou | Representation learning is a fundamental part of modern computer vision, where abstract representations of data are encoded as tensors optimized to solve problems like image segmentation and inpainting. Recently, self-attention in the form of Non-Local Block has emerged as a powerful technique to enrich features, by capturing complex interdependencies in feature tensors. However, standard self-attention approaches leverage only spatial relationships, drawing similarities between vectors and overlooking correlations between channels. In this paper, we introduce a new method, called Tensor Element Self-Attention (TESA) that generalizes such work to capture interdependencies along all dimensions of the tensor using matricization. An order R tensor produces R results, one for each dimension. The results are then fused to produce an enriched output which encapsulates similarity among tensor elements. Additionally, we analyze self-attention mathematically, providing new perspectives on how it adjusts the singular values of the input feature tensor. With these new insights, we present experimental results demonstrating how TESA can benefit diverse problems including classification and instance segmentation. By simply adding a TESA module to existing networks, we substantially improve competitive baselines and set new state-of-the-art results for image inpainting on Celeb and low light raw-to-rgb image translation on SID. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Babiloni_TESA_Tensor_Element_Self-Attention_via_Matricization_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Babiloni_TESA_Tensor_Element_Self-Attention_via_Matricization_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Babiloni_TESA_Tensor_Element_Self-Attention_via_Matricization_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Clean-Label Backdoor Attacks on Video Recognition Models | Shihao Zhao, Xingjun Ma, Xiang Zheng, James Bailey, Jingjing Chen, Yu-Gang Jiang | Deep neural networks (DNNs) are vulnerable to backdoor attacks which can hide backdoor triggers in DNNs by poisoning training data. A backdoored model behaves normally on clean test images, yet consistently predicts a particular target class for any test examples that contain the trigger pattern. As such, backdoor attacks are hard to detect, and have raised severe security concerns in real-world applications. Thus far, backdoor research has mostly been conducted in the image domain with image classification models. In this paper, we show that existing image backdoor attacks are far less effective on videos, and outline 4 strict conditions where existing attacks are likely to fail: 1) scenarios with more input dimensions (eg. videos), 2) scenarios with high resolution, 3) scenarios with a large number of classes and few examples per class (a "sparse dataset"), and 4) attacks with access to correct labels (eg. clean-label attacks). We propose the use of a universal adversarial trigger as the backdoor trigger to attack video recognition models, a situation where backdoor attacks are likely to be challenged by the above 4 strict conditions. We show on benchmark video datasets that our proposed backdoor attack can manipulate state-of-the-art video models with high success rates by poisoning only a small proportion of training data (without changing the labels). We also show that our proposed backdoor attack is resistant to state-of-the-art backdoor defense/detection methods, and can even be applied to improve image backdoor attacks. Our proposed video backdoor attack not only serves as a strong baseline for improving the robustness of video models, but also provides a new perspective for more understanding more powerful backdoor attacks. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zhao_Clean-Label_Backdoor_Attacks_on_Video_Recognition_Models_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.03030 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhao_Clean-Label_Backdoor_Attacks_on_Video_Recognition_Models_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhao_Clean-Label_Backdoor_Attacks_on_Video_Recognition_Models_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
RPM-Net: Robust Point Matching Using Learned Features | Zi Jian Yew, Gim Hee Lee | Iterative Closest Point (ICP) solves the rigid point cloud registration problem iteratively in two steps: (1) make hard assignments of spatially closest point correspondences, and then (2) find the least-squares rigid transformation. The hard assignments of closest point correspondences based on spatial distances are sensitive to the initial rigid transformation and noisy/outlier points, which often cause ICP to converge to wrong local minima. In this paper, we propose the RPM-Net -- a less sensitive to initialization and more robust deep learning-based approach for rigid point cloud registration. To this end, our network uses the differentiable Sinkhorn layer and annealing to get soft assignments of point correspondences from hybrid features learned from both spatial coordinates and local geometry. To further improve registration performance, we introduce a secondary network to predict optimal annealing parameters. Unlike some existing methods, our RPM-Net handles missing correspondences and point clouds with partial visibility. Experimental results show that our RPM-Net achieves state-of-the-art performance compared to existing non-deep learning and recent deep learning methods. Our source code is available at the project website (https://github.com/yewzijian/RPMNet). | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Yew_RPM-Net_Robust_Point_Matching_Using_Learned_Features_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Yew_RPM-Net_Robust_Point_Matching_Using_Learned_Features_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Yew_RPM-Net_Robust_Point_Matching_Using_Learned_Features_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Yew_RPM-Net_Robust_Point_CVPR_2020_supplemental.zip | null | null |
Improving One-Shot NAS by Suppressing the Posterior Fading | Xiang Li, Chen Lin, Chuming Li, Ming Sun, Wei Wu, Junjie Yan, Wanli Ouyang | Neural architecture search (NAS) has demonstrated much success in automatically designing effective neural network architectures. To improve the efficiency of NAS, previous approaches adopt weight sharing method to force all models share the same set of weights. However, it has been observed that a model performing better with shared weights does not necessarily perform better when trained alone. In this paper, we analyse existing weight sharing one-shot NAS approaches from a Bayesian point of view and identify the Posterior Fading problem, which compromises the effectiveness of shared weights. To alleviate this problem, we present a novel approach to guide the parameter posterior towards its true distribution. Moreover, a hard latency constraint is introduced during the search so that the desired latency can be achieved. The resulted method, namely Posterior Convergent NAS (PC-NAS), achieves state-of-the-art performance under standard GPU latency constraint on ImageNet. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Li_Improving_One-Shot_NAS_by_Suppressing_the_Posterior_Fading_CVPR_2020_paper.pdf | http://arxiv.org/abs/1910.02543 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Improving_One-Shot_NAS_by_Suppressing_the_Posterior_Fading_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Improving_One-Shot_NAS_by_Suppressing_the_Posterior_Fading_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Understanding Human Hands in Contact at Internet Scale | Dandan Shan, Jiaqi Geng, Michelle Shu, David F. Fouhey | Hands are the central means by which humans manipulate their world and being able to reliably extract hand state information from Internet videos of humans engaged in their hands has the potential to pave the way to systems that can learn from petabytes of video data. This paper proposes steps towards this by inferring a rich representation of hands engaged in interaction method that includes: hand location, side, contact state, and a box around the object in contact. To support this effort, we gather a large-scale dataset of hands in contact with objects consisting of 131 days of footage as well as a 100K annotated hand-contact video frame dataset. The learned model on this dataset can serve as a foundation for hand-contact understanding in videos. We quantitatively evaluate it both on its own and in service of predicting and learning from 3D meshes of human hands. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Shan_Understanding_Human_Hands_in_Contact_at_Internet_Scale_CVPR_2020_paper.pdf | http://arxiv.org/abs/2006.06669 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Shan_Understanding_Human_Hands_in_Contact_at_Internet_Scale_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Shan_Understanding_Human_Hands_in_Contact_at_Internet_Scale_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Self-Supervised Equivariant Attention Mechanism for Weakly Supervised Semantic Segmentation | Yude Wang, Jie Zhang, Meina Kan, Shiguang Shan, Xilin Chen | Image-level weakly supervised semantic segmentation is a challenging problem that has been deeply studied in recent years. Most of advanced solutions exploit class activation map (CAM). However, CAMs can hardly serve as the object mask due to the gap between full and weak supervisions. In this paper, we propose a self-supervised equivariant attention mechanism (SEAM) to discover additional supervision and narrow the gap. Our method is based on the observation that equivariance is an implicit constraint in fully supervised semantic segmentation, whose pixel-level labels take the same spatial transformation as the input images during data augmentation. However, this constraint is lost on the CAMs trained by image-level supervision. Therefore, we propose consistency regularization on predicted CAMs from various transformed images to provide self-supervision for network learning. Moreover, we propose a pixel correlation module (PCM), which exploits context appearance information and refines the prediction of current pixel by its similar neighbors, leading to further improvement on CAMs consistency. Extensive experiments on PASCAL VOC 2012 dataset demonstrate our method outperforms state-of-the-art methods using the same level of supervision. The code is released online. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wang_Self-Supervised_Equivariant_Attention_Mechanism_for_Weakly_Supervised_Semantic_Segmentation_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.04581 | https://www.youtube.com/watch?v=TBQYh9SrBqM | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Self-Supervised_Equivariant_Attention_Mechanism_for_Weakly_Supervised_Semantic_Segmentation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Self-Supervised_Equivariant_Attention_Mechanism_for_Weakly_Supervised_Semantic_Segmentation_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
TBT: Targeted Neural Network Attack With Bit Trojan | Adnan Siraj Rakin, Zhezhi He, Deliang Fan | Security of modern Deep Neural Networks (DNNs) is under severe scrutiny as the deployment of these models become widespread in many intelligence-based applications. Most recently, DNNs are attacked through Trojan which can effectively infect the model during the training phase and get activated only through specific input patterns (i.e, trigger) during inference. In this work, for the first time, we propose a novel Targeted Bit Trojan(TBT) method, which can insert a targeted neural Trojan into a DNN through bit-flip attack. Our algorithm efficiently generates a trigger specifically designed to locate certain vulnerable bits of DNN weights stored in main memory (i.e., DRAM). The objective is that once the attacker flips these vulnerable bits, the network still operates with normal inference accuracy with benign input. However, when the attacker activates the trigger by embedding it with any input, the network is forced to classify all inputs to a certain target class. We demonstrate that flipping only several vulnerable bits identified by our method, using available bit-flip techniques (i.e, row-hammer), can transform a fully functional DNN model into a Trojan-infected model. We perform extensive experiments of CIFAR-10, SVHN and ImageNet datasets on both VGG-16 and Resnet-18 architectures. Our proposed TBT could classify 92 of test images to a target class with as little as 84 bit-flips out of 88 million weight bits on Resnet-18 for CIFAR10 dataset. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Rakin_TBT_Targeted_Neural_Network_Attack_With_Bit_Trojan_CVPR_2020_paper.pdf | http://arxiv.org/abs/1909.05193 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Rakin_TBT_Targeted_Neural_Network_Attack_With_Bit_Trojan_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Rakin_TBT_Targeted_Neural_Network_Attack_With_Bit_Trojan_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
End-to-End Learning of Visual Representations From Uncurated Instructional Videos | Antoine Miech, Jean-Baptiste Alayrac, Lucas Smaira, Ivan Laptev, Josef Sivic, Andrew Zisserman | Annotating videos is cumbersome, expensive and not scalable. Yet, many strong video models still rely on manually annotated data. With the recent introduction of the HowTo100M dataset, narrated videos now offer the possibility of learning video representations without manual supervision. In this work we propose a new learning approach, MIL-NCE, capable of addressing mis- alignments inherent in narrated videos. With this approach we are able to learn strong video representations from scratch, without the need for any manual annotation. We evaluate our representations on a wide range of four downstream tasks over eight datasets: action recognition (HMDB-51, UCF-101, Kinetics-700), text-to- video retrieval (YouCook2, MSR-VTT), action localization (YouTube-8M Segments, CrossTask) and action segmentation (COIN). Our method outperforms all published self-supervised approaches for these tasks as well as several fully supervised baselines. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Miech_End-to-End_Learning_of_Visual_Representations_From_Uncurated_Instructional_Videos_CVPR_2020_paper.pdf | http://arxiv.org/abs/1912.06430 | https://www.youtube.com/watch?v=e6t-95DauuM | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Miech_End-to-End_Learning_of_Visual_Representations_From_Uncurated_Instructional_Videos_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Miech_End-to-End_Learning_of_Visual_Representations_From_Uncurated_Instructional_Videos_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
OrigamiNet: Weakly-Supervised, Segmentation-Free, One-Step, Full Page Text Recognition by learning to unfold | Mohamed Yousef, Tom E. Bishop | Text recognition is a major computer vision task with a big set of associated challenges. One of those traditional challenges is the coupled nature of text recognition and segmentation. This problem has been progressively solved over the past decades, going from segmentation based recognition to segmentation free approaches, which proved more accurate and much cheaper to annotate data for. We take a step from segmentation-free single line recognition towards segmentation-free multi-line / full page recognition. We propose a novel and simple neural network module, termed OrigamiNet, that can augment any CTC-trained, fully convolutional single line text recognizer, to convert it into a multi-line version by providing the model with enough spatial capacity to be able to properly collapse a 2D input signal into 1D without losing information. Such modified networks can be trained using exactly their same simple original procedure, and using only unsegmented image and text pairs. We carry out a set of interpretability experiments that show that our trained models learn an accurate implicit line segmentation. We achieve state-of-the-art character error rate on both IAM & ICDAR 2017 HTR benchmarks for handwriting recognition, surpassing all other methods in the literature. On IAM we even surpass single line methods that use accurate localization information during training. Our code is available online at https://github.com/IntuitionMachines/OrigamiNet . | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Yousef_OrigamiNet_Weakly-Supervised_Segmentation-Free_One-Step_Full_Page_Text_Recognition_by_learning_CVPR_2020_paper.pdf | http://arxiv.org/abs/2006.07491 | https://www.youtube.com/watch?v=CXxsiS838mQ | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Yousef_OrigamiNet_Weakly-Supervised_Segmentation-Free_One-Step_Full_Page_Text_Recognition_by_learning_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Yousef_OrigamiNet_Weakly-Supervised_Segmentation-Free_One-Step_Full_Page_Text_Recognition_by_learning_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Hierarchical Graph Attention Network for Visual Relationship Detection | Li Mi, Zhenzhong Chen | Visual Relationship Detection (VRD) aims to describe the relationship between two objects by providing a structural triplet shown as . Existing graph-based methods mainly represent the relationships by an object-level graph, which ignores to model the triplet-level dependencies. In this work, a Hierarchical Graph Attention Network (HGAT) is proposed to capture the dependencies on both object-level and triplet-level. Object-level graph aims to capture the interactions between objects, while the triplet-level graph models the dependencies among relation triplets. In addition, prior knowledge and attention mechanism are introduced to fix the redundant or missing edges on graphs that are constructed according to spatial correlation. With these approaches, nodes are allowed to attend over their spatial and semantic neighborhoods' features based on the visual or semantic feature correlation. Experimental results on the well-known VG and VRD datasets demonstrate that our model significantly outperforms the state-of-the-art methods. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Mi_Hierarchical_Graph_Attention_Network_for_Visual_Relationship_Detection_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Mi_Hierarchical_Graph_Attention_Network_for_Visual_Relationship_Detection_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Mi_Hierarchical_Graph_Attention_Network_for_Visual_Relationship_Detection_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Mi_Hierarchical_Graph_Attention_CVPR_2020_supplemental.pdf | null | null |
Neural Implicit Embedding for Point Cloud Analysis | Kent Fujiwara, Taiichi Hashimoto | We present a novel representation for point clouds that encapsulates the local characteristics of the underlying structure. The key idea is to embed an implicit representation of the point cloud, namely the distance field, into neural networks. One neural network is used to embed a portion of the distance field around a point. The resulting network weights are concatenated to be used as a representation of the corresponding point cloud instance. To enable comparison among the weights, Extreme Learning Machine (ELM) is employed as the embedding network. Invariance to scale and coordinate change can be achieved by introducing a scale commutative activation layer to the ELM, and aligning the distance field into a canonical pose. Experimental results using our representation demonstrate that our proposal is capable of similar or better classification and segmentation performance compared to the state-of-the-art point-based methods, while requiring less time for training. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Fujiwara_Neural_Implicit_Embedding_for_Point_Cloud_Analysis_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Fujiwara_Neural_Implicit_Embedding_for_Point_Cloud_Analysis_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Fujiwara_Neural_Implicit_Embedding_for_Point_Cloud_Analysis_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Fujiwara_Neural_Implicit_Embedding_CVPR_2020_supplemental.zip | null | null |
Better Captioning With Sequence-Level Exploration | Jia Chen, Qin Jin | Sequence-level learning objective has been widely used in captioning tasks to achieve the state-of-the-art performance for many models. In this objective, the model is trained by the reward on the quality of its generated captions (sequence-level). In this work, we show the limitation of the current sequence-level learning objective for captioning tasks from both theory and empirical result. In theory, we show that the current objective is equivalent to only optimizing the precision side of the caption set generated by the model and therefore overlooks the recall side. Empirical result shows that the model trained by this objective tends to get lower score on the recall side. We propose to add a sequence-level exploration term to the current objective to boost recall. It guides the model to explore more plausible captions in the training. In this way, the proposed objective takes both the precision and recall sides of generated captions into account. Experiments show the effectiveness of the proposed method on both video and image captioning datasets. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Chen_Better_Captioning_With_Sequence-Level_Exploration_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.03749 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Chen_Better_Captioning_With_Sequence-Level_Exploration_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Chen_Better_Captioning_With_Sequence-Level_Exploration_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Moving in the Right Direction: A Regularization for Deep Metric Learning | Deen Dayal Mohan, Nishant Sankaran, Dennis Fedorishin, Srirangaraj Setlur, Venu Govindaraju | Deep metric learning leverages carefully designed sampling strategies and loss functions that aid in optimizing the generation of a discriminable embedding space. While effective sampling of pairs is critical for shaping the metric space during training, the relative interactions between pairs, and consequently the forces exerted on these pairs that direct their displacement in the embedding space can significantly impact the formation of well separated clusters. In this work, we identify a shortcoming of existing loss formulations which fail to consider more optimal directions of pair displacements as another criterion for optimization. We propose a novel direction regularization to explicitly account for the layout of sampled pairs and attempt to introduce orthogonality in the representations. The proposed regularization is easily integrated into existing loss functions providing considerable performance improvements. We experimentally validate our hypothesis on the Cars-196, CUB-200 and InShop datasets and outperform existing methods to yield state-of-the-art results on these datasets. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Mohan_Moving_in_the_Right_Direction_A_Regularization_for_Deep_Metric_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Mohan_Moving_in_the_Right_Direction_A_Regularization_for_Deep_Metric_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Mohan_Moving_in_the_Right_Direction_A_Regularization_for_Deep_Metric_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Mohan_Moving_in_the_CVPR_2020_supplemental.pdf | null | null |
Improved Few-Shot Visual Classification | Peyman Bateni, Raghav Goyal, Vaden Masrani, Frank Wood, Leonid Sigal | Few-shot learning is a fundamental task in computer vision that carries the promise of alleviating the need for exhaustively labeled data. Most few-shot learning approaches to date have focused on progressively more complex neural feature extractors and classifier adaptation strategies, and the refinement of the task definition itself. In this paper, we explore the hypothesis that a simple class-covariance-based distance metric, namely the Mahalanobis distance, adopted into a state of the art few-shot learning approach (CNAPS) can, in and of itself, lead to a significant performance improvement. We also discover that it is possible to learn adaptive feature extractors that allow useful estimation of the high dimensional feature covariances required by this metric from surprisingly few samples. The result of our work is a new "Simple CNAPS" architecture which has up to 9.2% fewer trainable parameters than CNAPS and performs up to 6.1% better than state of the art on the standard few-shot image classification benchmark dataset. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Bateni_Improved_Few-Shot_Visual_Classification_CVPR_2020_paper.pdf | http://arxiv.org/abs/1912.03432 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Bateni_Improved_Few-Shot_Visual_Classification_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Bateni_Improved_Few-Shot_Visual_Classification_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Bateni_Improved_Few-Shot_Visual_CVPR_2020_supplemental.pdf | null | null |
Visual Chirality | Zhiqiu Lin, Jin Sun, Abe Davis, Noah Snavely | How can we tell whether an image has been mirrored? While we understand the geometry of mirror reflections very well, less has been said about how it affects distributions of imagery at scale, despite widespread use for data augmentation in computer vision. In this paper, we investigate how the statistics of visual data are changed by reflection. We refer to these changes as "visual chirality," after the concept of geometric chirality---the notion of objects that are distinct from their mirror image. Our analysis of visual chirality reveals surprising results, including low-level chiral signals pervading imagery stemming from image processing in cameras, to the ability to discover visual chirality in images of people and faces. Our work has implications for data augmentation, self-supervised learning, and image forensics. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Lin_Visual_Chirality_CVPR_2020_paper.pdf | http://arxiv.org/abs/2006.09512 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Lin_Visual_Chirality_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Lin_Visual_Chirality_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Lin_Visual_Chirality_CVPR_2020_supplemental.pdf | null | null |
Neural Architecture Search for Lightweight Non-Local Networks | Yingwei Li, Xiaojie Jin, Jieru Mei, Xiaochen Lian, Linjie Yang, Cihang Xie, Qihang Yu, Yuyin Zhou, Song Bai, Alan L. Yuille | Non-Local (NL) blocks have been widely studied in various vision tasks. However, it has been rarely explored to embed the NL blocks in mobile neural networks, mainly due to the following challenges: 1) NL blocks generally have heavy computation cost which makes it difficult to be applied in applications where computational resources are limited, and 2) it is an open problem to discover an optimal configuration to embed NL blocks into mobile neural networks. We propose AutoNL to overcome the above two obstacles. Firstly, we propose a Lightweight Non-Local (LightNL) block by squeezing the transformation operations and incorporating compact features. With the novel design choices, the proposed LightNL block is 400 times computationally cheaper than its conventional counterpart without sacrificing the performance. Secondly, by relaxing the structure of the LightNL block to be differentiable during training, we propose an efficient neural architecture search algorithm to learn an optimal configuration of LightNL blocks in an end-to-end manner. Notably, using only 32 GPU hours, the searched AutoNL model achieves 77.7% top-1 accuracy on ImageNet under a typical mobile setting (350M FLOPs), significantly outperforming previous mobile models including MobileNetV2 (+5.7%), FBNet (+2.8%) and MnasNet (+2.1%). Code and models are available at https://github.com/LiYingwei/AutoNL. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Li_Neural_Architecture_Search_for_Lightweight_Non-Local_Networks_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.01961 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Neural_Architecture_Search_for_Lightweight_Non-Local_Networks_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Neural_Architecture_Search_for_Lightweight_Non-Local_Networks_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Private-kNN: Practical Differential Privacy for Computer Vision | Yuqing Zhu, Xiang Yu, Manmohan Chandraker, Yu-Xiang Wang | With increasing ethical and legal concerns on privacy for deep models in visual recognition, differential privacy has emerged as a mechanism to disguise membership of sensitive data in training datasets. Recent methods like Private Aggregation of Teacher Ensembles (PATE) leverage a large ensemble of teacher models trained on disjoint subsets of private data, to transfer knowledge to a student model with privacy guarantees. However, labeled vision data is often expensive and datasets, when split into many disjoint training sets, lead to significantly sub-optimal accuracy and thus hardly sustain good privacy bounds. We propose a practically data-efficient scheme based on private release of k-nearest neighbor (kNN) queries, which altogether avoids splitting the training dataset. Our approach allows the use of privacy-amplification by subsampling and iterative refinement of the kNN feature embedding. We rigorously analyze the theoretical properties of our method and demonstrate strong experimental performance on practical computer vision datasets for face attribute recognition and person reidentification. In particular, we achieve comparable or better accuracy than PATE while reducing more than 90% of the privacy loss, thereby providing the "most practical method to-date" for private deep learning in computer vision. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zhu_Private-kNN_Practical_Differential_Privacy_for_Computer_Vision_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhu_Private-kNN_Practical_Differential_Privacy_for_Computer_Vision_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhu_Private-kNN_Practical_Differential_Privacy_for_Computer_Vision_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Zhu_Private-kNN_Practical_Differential_CVPR_2020_supplemental.pdf | null | null |
Old Is Gold: Redefining the Adversarially Learned One-Class Classifier Training Paradigm | Muhammad Zaigham Zaheer, Jin-Ha Lee, Marcella Astrid, Seung-Ik Lee | A popular method for anomaly detection is to use the generator of an adversarial network to formulate anomaly score over reconstruction loss of input. Due to the rare occurrence of anomalies, optimizing such networks can be a cumbersome task. Another possible approach is to use both generator and discriminator for anomaly detection. However, attributed to the involvement of adversarial training, this model is often unstable in a way that the performance fluctuates drastically with each training step. In this study, we propose a framework that effectively generates stable results across a wide range of training steps and allows us to use both the generator and the discriminator of an adversarial model for efficient and robust anomaly detection. Our approach transforms the fundamental role of a discriminator from identifying real and fake data to distinguishing between good and bad quality reconstructions. To this end, we prepare training examples for the good quality reconstruction by employing the current generator, whereas poor quality examples are obtained by utilizing an old state of the same generator. This way, the discriminator learns to detect subtle distortions that often appear in reconstructions of the anomaly inputs. Extensive experiments performed on Caltech-256 and MNIST image datasets for novelty detection show superior results. Furthermore, on UCSD Ped2 video dataset for anomaly detection, our model achieves a frame-level AUC of 98.1%, surpassing recent state-of-the-art methods | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zaheer_Old_Is_Gold_Redefining_the_Adversarially_Learned_One-Class_Classifier_Training_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.07657 | https://www.youtube.com/watch?v=TQNRR3dvOt0 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zaheer_Old_Is_Gold_Redefining_the_Adversarially_Learned_One-Class_Classifier_Training_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zaheer_Old_Is_Gold_Redefining_the_Adversarially_Learned_One-Class_Classifier_Training_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Zaheer_Old_Is_Gold_CVPR_2020_supplemental.zip | null | null |
Cops-Ref: A New Dataset and Task on Compositional Referring Expression Comprehension | Zhenfang Chen, Peng Wang, Lin Ma, Kwan-Yee K. Wong, Qi Wu | Referring expression comprehension (REF) aims at identifying a particular object in a scene by a natural language expression. It requires joint reasoning over the textual and visual domains to solve the problem. Some popular referring expression datasets, however, fail to provide an ideal test bed for evaluating the reasoning ability of the models, mainly because 1) their expressions typically describe only some simple distinctive properties of the object and 2) their images contain limited distracting information. To bridge the gap, we propose a new dataset for visual reasoning in context of referring expression comprehension with two main features. First, we design a novel expression engine rendering various reasoning logics that can be flexibly combined with rich visual properties to generate expressions with varying compositionality. Second, to better exploit the full reasoning chain embodied in an expression, we propose a new test setting by adding additional distracting images containing objects sharing similar properties with the referent, thus minimising the success rate of reasoning-free cross-domain alignment. We evaluate several state-of-the-art REF models, but find none of them can achieve promising performance. A proposed modular hard mining strategy performs the best but still leaves substantial room for improvement. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Chen_Cops-Ref_A_New_Dataset_and_Task_on_Compositional_Referring_Expression_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=K2L1_VQz8a8 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Chen_Cops-Ref_A_New_Dataset_and_Task_on_Compositional_Referring_Expression_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Chen_Cops-Ref_A_New_Dataset_and_Task_on_Compositional_Referring_Expression_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Chen_Cops-Ref_A_New_CVPR_2020_supplemental.pdf | null | null |
Learning Longterm Representations for Person Re-Identification Using Radio Signals | Lijie Fan, Tianhong Li, Rongyao Fang, Rumen Hristov, Yuan Yuan, Dina Katabi | Person Re-Identification (ReID) aims to recognize a person-of-interest across different places and times. Existing ReID methods rely on images or videos collected using RGB cameras. They extract appearance features like clothes, shoes, hair, etc. Such features, however, can change drastically from one day to the next, leading to inability to identify people over extended time periods. In this paper, we introduce RF-ReID, a novel approach that harnesses radio frequency (RF) signals for longterm person ReID. RF signals traverse clothes and reflect off the human body; thus they can be used to extract more persistent human-identifying features like body size and shape. We evaluate the performance of RF-ReID on longitudinal datasets that span days and weeks, where the person may wear different clothes across days. Our experiments demonstrate that RF-ReID outperforms state-of-the-art RGB-based ReID approaches for long term person ReID. Our results also reveal two interesting features: First since RF signals work in the presence of occlusions and poor lighting, RF-ReID allows for person ReID in such scenarios. Second, unlike photos and videos which reveal personal and private information, RF signals are more privacy-preserving, and hence can help extend person ReID to privacy-concerned domains, like healthcare. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Fan_Learning_Longterm_Representations_for_Person_Re-Identification_Using_Radio_Signals_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.01091 | https://www.youtube.com/watch?v=zvCzgx9JDn8 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Fan_Learning_Longterm_Representations_for_Person_Re-Identification_Using_Radio_Signals_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Fan_Learning_Longterm_Representations_for_Person_Re-Identification_Using_Radio_Signals_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
DSNAS: Direct Neural Architecture Search Without Parameter Retraining | Shoukang Hu, Sirui Xie, Hehui Zheng, Chunxiao Liu, Jianping Shi, Xunying Liu, Dahua Lin | If NAS methods are solutions, what is the problem? Most existing NAS methods require two-stage parameter optimization. However, performance of the same architecture in the two stages correlates poorly. In this work, we propose a new problem definition for NAS, task-specific end-to-end, based on this observation. We argue that given a computer vision task for which a NAS method is expected, this definition can reduce the vaguely-defined NAS evaluation to i) accuracy of this task and ii) the total computation consumed to finally obtain a model with satisfying accuracy. Seeing that most existing methods do not solve this problem directly, we propose DSNAS, an efficient differentiable NAS framework that simultaneously optimizes architecture and parameters with a low-biased Monte Carlo estimate. Child networks derived from DSNAS can be deployed directly without parameter retraining. Comparing with two-stage methods, DSNAS successfully discovers networks with comparable accuracy (74.4%) on ImageNet in 420 GPU hours, reducing the total time by more than 34%. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Hu_DSNAS_Direct_Neural_Architecture_Search_Without_Parameter_Retraining_CVPR_2020_paper.pdf | http://arxiv.org/abs/2002.09128 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Hu_DSNAS_Direct_Neural_Architecture_Search_Without_Parameter_Retraining_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Hu_DSNAS_Direct_Neural_Architecture_Search_Without_Parameter_Retraining_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Hu_DSNAS_Direct_Neural_CVPR_2020_supplemental.pdf | null | null |
SESS: Self-Ensembling Semi-Supervised 3D Object Detection | Na Zhao, Tat-Seng Chua, Gim Hee Lee | The performance of existing point cloud-based 3D object detection methods heavily relies on large-scale high-quality 3D annotations. However, such annotations are often tedious and expensive to collect. Semi-supervised learning is a good alternative to mitigate the data annotation issue, but has remained largely unexplored in 3D object detection. Inspired by the recent success of self-ensembling technique in semi-supervised image classification task, we propose SESS, a self-ensembling semi-supervised 3D object detection framework. Specifically, we design a thorough perturbation scheme to enhance generalization of the network on unlabeled and new unseen data. Furthermore, we propose three consistency losses to enforce the consistency between two sets of predicted 3D object proposals, to facilitate the learning of structure and semantic invariances of objects. Extensive experiments conducted on SUN RGB-D and ScanNet datasets demonstrate the effectiveness of SESS in both inductive and transductive semi-supervised 3D object detection. Our SESS achieves competitive performance compared to the state-of-the-art fully-supervised method by using only 50% labeled data. Our code is available at https://github.com/Na-Z/sess. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zhao_SESS_Self-Ensembling_Semi-Supervised_3D_Object_Detection_CVPR_2020_paper.pdf | http://arxiv.org/abs/1912.11803 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhao_SESS_Self-Ensembling_Semi-Supervised_3D_Object_Detection_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhao_SESS_Self-Ensembling_Semi-Supervised_3D_Object_Detection_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Zhao_SESS_Self-Ensembling_Semi-Supervised_CVPR_2020_supplemental.pdf | null | null |
Panoptic-DeepLab: A Simple, Strong, and Fast Baseline for Bottom-Up Panoptic Segmentation | Bowen Cheng, Maxwell D. Collins, Yukun Zhu, Ting Liu, Thomas S. Huang, Hartwig Adam, Liang-Chieh Chen | In this work, we introduce Panoptic-DeepLab, a simple, strong, and fast system for panoptic segmentation, aiming to establish a solid baseline for bottom-up methods that can achieve comparable performance of two-stage methods while yielding fast inference speed. In particular, Panoptic-DeepLab adopts the dual-ASPP and dual-decoder structures specific to semantic, and instance segmentation, respectively. The semantic segmentation branch is the same as the typical design of any semantic segmentation model (e.g., DeepLab), while the instance segmentation branch is class-agnostic, involving a simple instance center regression. As a result, our single Panoptic-DeepLab simultaneously ranks first at all three Cityscapes benchmarks, setting the new state-of-art of 84.2% mIoU, 39.0% AP, and 65.5% PQ on test set. Additionally, equipped with MobileNetV3, Panoptic-DeepLab runs nearly in real-time with a single 1025x2049 image (15.8 frames per second), while achieving a competitive performance on Cityscapes (54.1 PQ% on test set). On Mapillary Vistas test set, our ensemble of six models attains 42.7% PQ, outperforming the challenge winner in 2018 by a healthy margin of 1.5%. Finally, our Panoptic-DeepLab also performs on par with several top-down approaches on the challenging COCO dataset. For the first time, we demonstrate a bottom-up approach could deliver state-of-the-art results on panoptic segmentation. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Cheng_Panoptic-DeepLab_A_Simple_Strong_and_Fast_Baseline_for_Bottom-Up_Panoptic_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=EAPgRg_YPIk | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Cheng_Panoptic-DeepLab_A_Simple_Strong_and_Fast_Baseline_for_Bottom-Up_Panoptic_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Cheng_Panoptic-DeepLab_A_Simple_Strong_and_Fast_Baseline_for_Bottom-Up_Panoptic_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Cheng_Panoptic-DeepLab_A_Simple_CVPR_2020_supplemental.pdf | null | null |
Spatio-Temporal Graph for Video Captioning With Knowledge Distillation | Boxiao Pan, Haoye Cai, De-An Huang, Kuan-Hui Lee, Adrien Gaidon, Ehsan Adeli, Juan Carlos Niebles | Video captioning is a challenging task that requires a deep understanding of visual scenes. State-of-the-art methods generate captions using either scene-level or object-level information but without explicitly modeling object interactions. Thus, they often fail to make visually grounded predictions, and are sensitive to spurious correlations. In this paper, we propose a novel spatio-temporal graph model for video captioning that exploits object interactions in space and time. Our model builds interpretable links and is able to provide explicit visual grounding. To avoid unstable performance caused by the variable number of objects, we further propose an object-aware knowledge distillation mechanism, in which local object information is used to regularize global scene features. We demonstrate the efficacy of our approach through extensive experiments on two benchmarks, showing our approach yields competitive performance with interpretable predictions. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Pan_Spatio-Temporal_Graph_for_Video_Captioning_With_Knowledge_Distillation_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.13942 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Pan_Spatio-Temporal_Graph_for_Video_Captioning_With_Knowledge_Distillation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Pan_Spatio-Temporal_Graph_for_Video_Captioning_With_Knowledge_Distillation_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
ACNe: Attentive Context Normalization for Robust Permutation-Equivariant Learning | Weiwei Sun, Wei Jiang, Eduard Trulls, Andrea Tagliasacchi, Kwang Moo Yi | Many problems in computer vision require dealing with sparse, unordered data in the form of point clouds. Permutation-equivariant networks have become a popular solution - they operate on individual data points with simple perceptrons and extract contextual information with global pooling. This can be achieved with a simple normalization of the feature maps, a global operation that is unaffected by the order. In this paper, we propose Attentive Context Normalization (ACN), a simple yet effective technique to build permutation-equivariant networks robust to outliers. Specifically, we show how to normalize the feature maps with weights that are estimated within the network, excluding outliers from this normalization. We use this mechanism to leverage two types of attention: local and global - by combining them, our method is able to find the essential data points in high-dimensional space in order to solve a given task. We demonstrate through extensive experiments that our approach, which we call Attentive Context Networks (ACNe), provides a significant leap in performance compared to the state-of-the-art on camera pose estimation, robust fitting, and point cloud classification under noise and outliers. Source code: https://github.com/vcg-uvic/acne. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Sun_ACNe_Attentive_Context_Normalization_for_Robust_Permutation-Equivariant_Learning_CVPR_2020_paper.pdf | http://arxiv.org/abs/1907.02545 | https://www.youtube.com/watch?v=sBxguUF3XAQ | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Sun_ACNe_Attentive_Context_Normalization_for_Robust_Permutation-Equivariant_Learning_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Sun_ACNe_Attentive_Context_Normalization_for_Robust_Permutation-Equivariant_Learning_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Sun_ACNe_Attentive_Context_CVPR_2020_supplemental.pdf | null | https://openaccess.thecvf.com |
ViBE: Dressing for Diverse Body Shapes | Wei-Lin Hsiao, Kristen Grauman | Body shape plays an important role in determining what garments will best suit a given person, yet today's clothing recommendation methods take a "one shape fits all" approach. These body-agnostic vision methods and datasets are a barrier to inclusion, ill-equipped to provide good suggestions for diverse body shapes. We introduce ViBE, a VIsual Body-aware Embedding that captures clothing's affinity with different body shapes. Given an image of a person, the proposed embedding identifies garments that will flatter her specific body shape. We show how to learn the embedding from an online catalog displaying fashion models of various shapes and sizes wearing the products, and we devise a method to explain the algorithm's suggestions for well-fitting garments. We apply our approach to a dataset of diverse subjects, and demonstrate its strong advantages over status quo body-agnostic recommendation, both according to automated metrics and human opinion. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Hsiao_ViBE_Dressing_for_Diverse_Body_Shapes_CVPR_2020_paper.pdf | http://arxiv.org/abs/1912.06697 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Hsiao_ViBE_Dressing_for_Diverse_Body_Shapes_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Hsiao_ViBE_Dressing_for_Diverse_Body_Shapes_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Hsiao_ViBE_Dressing_for_CVPR_2020_supplemental.pdf | null | null |
Density-Based Clustering for 3D Object Detection in Point Clouds | Syeda Mariam Ahmed, Chee Meng Chew | Current 3D detection networks either rely on 2D object proposals or try to directly predict bounding box parameters from each point in a scene. While former methods are dependent on performance of 2D detectors, latter approaches are challenging due to the sparsity and occlusion in point clouds, making it difficult to regress accurate parameters. In this work, we introduce a novel approach for 3D object detection that is significant in two main aspects: a) cascaded modular approach that focuses the receptive field of each module on specific points in the point cloud, for improved feature learning and b) a class agnostic instance segmentation module that is initiated using unsupervised clustering. The objective of a cascaded approach is to sequentially minimize the number of points running through the network. While three different modules perform the tasks of background-foreground segmentation, class agnostic instance segmentation and object detection, through individually trained point based networks. We also evaluate bayesian uncertainty in modules, demonstrating the over all level of confidence in our prediction results. Performance of the network is evaluated on the SUN RGB-D benchmark dataset, that demonstrates an improvement as compared to state-of-the-art methods. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Ahmed_Density-Based_Clustering_for_3D_Object_Detection_in_Point_Clouds_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Ahmed_Density-Based_Clustering_for_3D_Object_Detection_in_Point_Clouds_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Ahmed_Density-Based_Clustering_for_3D_Object_Detection_in_Point_Clouds_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Diverse Image Generation via Self-Conditioned GANs | Steven Liu, Tongzhou Wang, David Bau, Jun-Yan Zhu, Antonio Torralba | We introduce a simple but effective unsupervised method for generating diverse images. We train a class-conditional GAN model without using manually annotated class labels. Instead, our model is conditional on labels automatically derived from clustering in the discriminator's feature space. Our clustering step automatically discovers diverse modes, and explicitly requires the generator to cover them. Experiments on standard mode collapse benchmarks show that our method outperforms several competing methods when addressing mode collapse. Our method also performs well on large-scale datasets such as ImageNet and Places365, improving both diversity and standard metrics (e.g., Frechet Inception Distance), compared to previous methods. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Liu_Diverse_Image_Generation_via_Self-Conditioned_GANs_CVPR_2020_paper.pdf | http://arxiv.org/abs/2006.10728 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Liu_Diverse_Image_Generation_via_Self-Conditioned_GANs_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Liu_Diverse_Image_Generation_via_Self-Conditioned_GANs_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
A Certifiably Globally Optimal Solution to Generalized Essential Matrix Estimation | Ji Zhao, Wanting Xu, Laurent Kneip | We present a convex optimization approach for generalized essential matrix (GEM) estimation. The six-point minimal solver for the GEM has poor numerical stability and applies only for a minimal number of points. Existing non-minimal solvers for GEM estimation rely on either local optimization or relinearization techniques, which impedes high accuracy in common scenarios. Our proposed non-minimal solver minimizes the sum of squared residuals by reformulating the problem as a quadratically constrained quadratic program. The globally optimal solution is thus obtained by a semidefinite relaxation. The algorithm retrieves certifiably globally optimal solutions to the original non-convex problem in polynomial time. We also provide the necessary and sufficient conditions to recover the optimal GEM from the relaxed problems. The improved performance is demonstrated over experiments on both synthetic and real multi-camera systems. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zhao_A_Certifiably_Globally_Optimal_Solution_to_Generalized_Essential_Matrix_Estimation_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=ewiee2vKLn8 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhao_A_Certifiably_Globally_Optimal_Solution_to_Generalized_Essential_Matrix_Estimation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhao_A_Certifiably_Globally_Optimal_Solution_to_Generalized_Essential_Matrix_Estimation_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Video Panoptic Segmentation | Dahun Kim, Sanghyun Woo, Joon-Young Lee, In So Kweon | Panoptic segmentation has become a new standard of visual recognition task by unifying previous semantic segmentation and instance segmentation tasks in concert. In this paper, we propose and explore a new video extension of this task, called video panoptic segmentation. The task requires generating consistent panoptic segmentation as well as an association of instance ids across video frames. To invigorate research on this new task, we present two types of video panoptic datasets. The first is a re-organization of the synthetic VIPER dataset into the video panoptic format to exploit its large-scale pixel annotations. The second is a temporal extension on the Cityscapes val. set, by providing new video panoptic annotations (Cityscapes-VPS). Moreover, we propose a novel video panoptic segmentation network (VPSNet) which jointly predicts object classes, bounding boxes, masks, instance id tracking, and semantic segmentation in video frames. To provide appropriate metrics for this task, we propose a video panoptic quality (VPQ) metric and evaluate our method and several other baselines. Experimental results demonstrate the effectiveness of the presented two datasets. We achieve state-of-the-art results in image PQ on Cityscapes and also in VPQ on Cityscapes-VPS and VIPER datasets. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Kim_Video_Panoptic_Segmentation_CVPR_2020_paper.pdf | http://arxiv.org/abs/2006.11339 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Kim_Video_Panoptic_Segmentation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Kim_Video_Panoptic_Segmentation_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Structured Multi-Hashing for Model Compression | Elad Eban, Yair Movshovitz-Attias, Hao Wu, Mark Sandler, Andrew Poon, Yerlan Idelbayev, Miguel A. Carreira-Perpinan | Despite the success of deep neural networks (DNNs), state-of-the-art models are too large to deploy on low-resource devices or common server configurations in which multiple models are held in memory. Model compression methods address this limitation by reducing the memory footprint, latency, or energy consumption of a model with minimal impact on accuracy. We focus on the task of reducing the number of learnable variables in the model. In this work we combine ideas from weight hashing and dimensionality reductions resulting in a simple and powerful structured multi-hashing method based on matrix products that allows direct control of model size of any deep network and is trained end-to-end. We demonstrate the strength of our approach by compressing models from the ResNet, EfficientNet, and MobileNet architecture families. Our method allows us to drastically decrease the number of variables while maintaining high accuracy. For instance, by applying our approach to EfficentNet-B4 (16M parameters) we reduce it to the size of B0 (5M parameters), while gaining over 3% in accuracy over B0 baseline. On the commonly used benchmark CIFAR10 we reduce the ResNet32 model by 75% with no loss in quality, and are able to do a 10x compression while still achieving above 90% accuracy. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Eban_Structured_Multi-Hashing_for_Model_Compression_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Eban_Structured_Multi-Hashing_for_Model_Compression_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Eban_Structured_Multi-Hashing_for_Model_Compression_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Maintaining Discrimination and Fairness in Class Incremental Learning | Bowen Zhao, Xi Xiao, Guojun Gan, Bin Zhang, Shu-Tao Xia | Deep neural networks (DNNs) have been applied in class incremental learning, which aims to solve common real-world problems of learning new classes continually. One drawback of standard DNNs is that they are prone to catastrophic forgetting. Knowledge distillation (KD) is a commonly used technique to alleviate this problem. In this paper, we demonstrate it can indeed help the model to output more discriminative results within old classes. However, it cannot alleviate the problem that the model tends to classify objects into new classes, causing the positive effect of KD to be hidden and limited. We observed that an important factor causing catastrophic forgetting is that the weights in the last fully connected (FC) layer are highly biased in class incremental learning. In this paper, we propose a simple and effective solution motivated by the aforementioned observations to address catastrophic forgetting. Firstly, we utilize KD to maintain the discrimination within old classes. Then, to further maintain the fairness between old classes and new classes, we propose Weight Aligning (WA) that corrects the biased weights in the FC layer after normal training process. Unlike previous work, WA does not require any extra parameters or a validation set in advance, as it utilizes the information provided by the biased weights themselves. The proposed method is evaluated on ImageNet-1000, ImageNet-100, and CIFAR-100 under various settings. Experimental results show that the proposed method can effectively alleviate catastrophic forgetting and significantly outperform state-of-the-art methods. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zhao_Maintaining_Discrimination_and_Fairness_in_Class_Incremental_Learning_CVPR_2020_paper.pdf | http://arxiv.org/abs/1911.07053 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhao_Maintaining_Discrimination_and_Fairness_in_Class_Incremental_Learning_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhao_Maintaining_Discrimination_and_Fairness_in_Class_Incremental_Learning_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Zhao_Maintaining_Discrimination_and_CVPR_2020_supplemental.pdf | null | null |
ZeroQ: A Novel Zero Shot Quantization Framework | Yaohui Cai, Zhewei Yao, Zhen Dong, Amir Gholami, Michael W. Mahoney, Kurt Keutzer | Quantization is a promising approach for reducing the inference time and memory footprint of neural networks. However, most existing quantization methods require access to the original training dataset for retraining during quantization. This is often not possible for applications with sensitive or proprietary data, e.g., due to privacy and security concerns. Existing zero-shot quantization methods use different heuristics to address this, but they result in poor performance, especially when quantizing to ultra-low precision. Here, we propose \OURS, a novel zero-shot quantization framework to address this. \OURS enables mixed-precision quantization without any access to the training or validation data. This is achieved by optimizing for a Distilled Dataset, which is engineered to match the statistics of batch normalization across different layers of the network. \OURS supports both uniform and mixed-precision quantization. For the latter, we introduce a novel Pareto frontier based method to automatically determine the mixed-precision bit setting for all layers, with no manual search involved. We extensively test our proposed method on a diverse set of models, including ResNet18/50/152, MobileNetV2, ShuffleNet, SqueezeNext, and InceptionV3 on ImageNet, as well as RetinaNet-ResNet50 on the Microsoft COCO dataset. In particular, we show that \OURS can achieve 1.71% higher accuracy on MobileNetV2, as compared to the recently proposed DFQ [??] method. Importantly, \OURS has a very low computational overhead, and it can finish the entire quantization process in less than 30s (0.5% of one epoch training time of ResNet50 on ImageNet). We have open-sourced the \OURS framework(https://github.com/amirgholami/ZeroQ). | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Cai_ZeroQ_A_Novel_Zero_Shot_Quantization_Framework_CVPR_2020_paper.pdf | http://arxiv.org/abs/2001.00281 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Cai_ZeroQ_A_Novel_Zero_Shot_Quantization_Framework_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Cai_ZeroQ_A_Novel_Zero_Shot_Quantization_Framework_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Cai_ZeroQ_A_Novel_CVPR_2020_supplemental.pdf | null | null |
Learning Visual Motion Segmentation Using Event Surfaces | Anton Mitrokhin, Zhiyuan Hua, Cornelia Fermuller, Yiannis Aloimonos | Event-based cameras have been designed for scene motion perception - their high temporal resolution and spatial data sparsity converts the scene into a volume of boundary trajectories and allows to track and analyze the evolution of the scene in time. Analyzing this data is computationally expensive, and there is substantial lack of theory on dense-in-time object motion to guide the development of new algorithms; hence, many works resort to a simple solution of discretizing the event stream and converting it to classical pixel maps, which allows for application of conventional image processing methods. In this work we present a Graph Convolutional neural network for the task of scene motion segmentation by a moving camera. We convert the event stream into a 3D graph in (x,y,t) space and keep per-event temporal information. The difficulty of the task stems from the fact that unlike in metric space, the shape of an object in (x,y,t) space depends on its motion and is not the same across the dataset. We discuss properties of of the event data with respect to this 3D recognition problem, and show that our Graph Convolutional architecture is superior to PointNet++. We evaluate our method on the state of the art event-based motion segmentation dataset - EV-IMO and perform comparisons to a frame-based method proposed by its authors. Our ablation studies show that increasing the event slice width improves the accuracy, and how subsampling and edge configurations affect the network performance. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Mitrokhin_Learning_Visual_Motion_Segmentation_Using_Event_Surfaces_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Mitrokhin_Learning_Visual_Motion_Segmentation_Using_Event_Surfaces_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Mitrokhin_Learning_Visual_Motion_Segmentation_Using_Event_Surfaces_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Mitrokhin_Learning_Visual_Motion_CVPR_2020_supplemental.pdf | null | null |
Orthogonal Convolutional Neural Networks | Jiayun Wang, Yubei Chen, Rudrasis Chakraborty, Stella X. Yu | Deep convolutional neural networks are hindered by training instability and feature redundancy towards further performance improvement. A promising solution is to impose orthogonality on convolutional filters. We develop an efficient approach to impose filter orthogonality on a convolutional layer based on the doubly block-Toeplitz matrix representation of the convolutional kernel, instead of the common kernel orthogonality approach, which we show is only necessary but not sufficient for ensuring orthogonal convolutions. Our proposed orthogonal convolution requires no additional parameters and little computational overhead. It consistently outperforms the kernel orthogonality alternative on a wide range of tasks such as image classification and inpainting under supervised, semi-supervised and unsupervised settings. It learns more diverse and expressive features with better training stability, robustness, and generalization. Our code is publicly available. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wang_Orthogonal_Convolutional_Neural_Networks_CVPR_2020_paper.pdf | http://arxiv.org/abs/1911.12207 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Orthogonal_Convolutional_Neural_Networks_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Orthogonal_Convolutional_Neural_Networks_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Wang_Orthogonal_Convolutional_Neural_CVPR_2020_supplemental.pdf | null | null |
Just Go With the Flow: Self-Supervised Scene Flow Estimation | Himangi Mittal, Brian Okorn, David Held | When interacting with highly dynamic environments, scene flow allows autonomous systems to reason about the non-rigid motion of multiple independent objects. This is of particular interest in the field of autonomous driving, in which many cars, people, bicycles, and other objects need to be accurately tracked. Current state-of-the-art methods require annotated scene flow data from autonomous driving scenes to train scene flow networks with supervised learning. As an alternative, we present a method of training scene flow that uses two self-supervised losses, based on nearest neighbors and cycle consistency. These self-supervised losses allow us to train our method on large unlabeled autonomous driving datasets; the resulting method matches current state-of-the-art supervised performance using no real world annotations and exceeds state-of-the-art performance when combining our self-supervised approach with supervised learning on a smaller labeled dataset. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Mittal_Just_Go_With_the_Flow_Self-Supervised_Scene_Flow_Estimation_CVPR_2020_paper.pdf | http://arxiv.org/abs/1912.00497 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Mittal_Just_Go_With_the_Flow_Self-Supervised_Scene_Flow_Estimation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Mittal_Just_Go_With_the_Flow_Self-Supervised_Scene_Flow_Estimation_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Mittal_Just_Go_With_CVPR_2020_supplemental.zip | null | null |
Set-Constrained Viterbi for Set-Supervised Action Segmentation | Jun Li, Sinisa Todorovic | This paper is about weakly supervised action segmentation, where the ground truth specifies only a set of actions present in a training video, but not their true temporal ordering. Prior work typically uses a classifier that independently labels video frames for generating the pseudo ground truth, and multiple instance learning for training the classifier. We extend this framework by specifying an HMM, which accounts for co-occurrences of action classes and their temporal lengths, and by explicitly training the HMM on a Viterbi-based loss. Our first contribution is the formulation of a new set-constrained Viterbi algorithm (SCV). Given a video, the SCV generates the MAP action segmentation that satisfies the ground truth. This prediction is used as a framewise pseudo ground truth in our HMM training. Our second contribution in training is a new regularization of feature affinities between training videos that share the same action classes. Evaluation on action segmentation and alignment on the Breakfast, MPII Cooking2, Hollywood Extended datasets demonstrates our significant performance improvement for the two tasks over prior work. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Li_Set-Constrained_Viterbi_for_Set-Supervised_Action_Segmentation_CVPR_2020_paper.pdf | http://arxiv.org/abs/2002.11925 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Set-Constrained_Viterbi_for_Set-Supervised_Action_Segmentation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Set-Constrained_Viterbi_for_Set-Supervised_Action_Segmentation_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Fast Sparse ConvNets | Erich Elsen, Marat Dukhan, Trevor Gale, Karen Simonyan | Historically, the pursuit of efficient inference has been one of the driving forces behind the research into new deep learning architectures and building blocks. Some of the recent examples include: the squeeze-and-excitation module, depthwise separable convolutions in Xception, and the inverted bottleneck in MobileNet v2. Notably, in all of these cases, the resulting building blocks enabled not only higher efficiency, but also higher accuracy, and found wide adoption in the field. In this work, we further expand the arsenal of efficient building blocks for neural network architectures; but instead of combining standard primitives (such as convolution), we advocate for the replacement of these dense primitives with their sparse counterparts. While the idea of using sparsity to decrease the parameter count is not new, the conventional wisdom is that this reduction in theoretical FLOPs does not translate into real-world efficiency gains. We aim to correct this misconception by introducing a family of efficient sparse kernels for several hardware platforms, which we plan to open source for the benefit of the community. Equipped with our efficient implementation of sparse primitives, we show that sparse versions of MobileNet v1 and MobileNet v2 architectures substantially outperform strong dense baselines on the efficiency-accuracy curve. On Snapdragon 835 our sparse networks outperform their dense equivalents by 1.3 - 2.4x - equivalent to approximately one entire generation of improvement. We hope that our findings will facilitate wider adoption of sparsity as a tool for creating efficient and accurate deep learning architectures. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Elsen_Fast_Sparse_ConvNets_CVPR_2020_paper.pdf | http://arxiv.org/abs/1911.09723 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Elsen_Fast_Sparse_ConvNets_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Elsen_Fast_Sparse_ConvNets_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Learning a Weakly-Supervised Video Actor-Action Segmentation Model With a Wise Selection | Jie Chen, Zhiheng Li, Jiebo Luo, Chenliang Xu | We address weakly-supervised video actor-action segmentation (VAAS), which extends general video object segmentation (VOS) to additionally consider action labels of the actors. The most successful methods on VOS synthesize a pool of pseudo-annotations (PAs) and then refine them iteratively. However, they face challenges as to how to select from a massive amount of PAs high-quality ones, how to set an appropriate stop condition for weakly-supervised training, and how to initialize PAs pertaining to VAAS. To overcome these challenges, we propose a general Weakly-Supervised framework with a Wise Selection of training samples and model evaluation criterion (WS^2). Instead of blindly trusting quality-inconsistent PAs, WS^2 employs a learning-based selection to select effective PAs and a novel region integrity criterion as a stopping condition for weakly-supervised training. In addition, a 3D-Conv GCAM is devised to adapt to the VAAS task. Extensive experiments show that WS^2 achieves state-of-the-art performance on both weakly-supervised VOS and VAAS tasks and is on par with the best fully-supervised method on VAAS. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Chen_Learning_a_Weakly-Supervised_Video_Actor-Action_Segmentation_Model_With_a_Wise_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.13141 | https://www.youtube.com/watch?v=LngdGKDKR3Q | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Chen_Learning_a_Weakly-Supervised_Video_Actor-Action_Segmentation_Model_With_a_Wise_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Chen_Learning_a_Weakly-Supervised_Video_Actor-Action_Segmentation_Model_With_a_Wise_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Chen_Learning_a_Weakly-Supervised_CVPR_2020_supplemental.pdf | null | null |
Gradually Vanishing Bridge for Adversarial Domain Adaptation | Shuhao Cui, Shuhui Wang, Junbao Zhuo, Chi Su, Qingming Huang, Qi Tian | In unsupervised domain adaptation, rich domain-specific characteristics bring great challenge to learn domain-invariant representations. However, domain discrepancy is considered to be directly minimized in existing solutions, which is difficult to achieve in practice. Some methods alleviate the difficulty by explicitly modeling domain-invariant and domain-specific parts in the representations, but the adverse influence of the explicit construction lies in the residual domain-specific characteristics in the constructed domain-invariant representations. In this paper, we equip adversarial domain adaptation with Gradually Vanishing Bridge (GVB) mechanism on both generator and discriminator. On the generator, GVB could not only reduce the overall transfer difficulty, but also reduce the influence of the residual domain-specific characteristics in domain-invariant representations. On the discriminator, GVB contributes to enhance the discriminating ability, and balance the adversarial training process. Experiments on three challenging datasets show that our GVB methods outperform strong competitors, and cooperate well with other adversarial methods. The code is available at https://github.com/cuishuhao/GVB. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Cui_Gradually_Vanishing_Bridge_for_Adversarial_Domain_Adaptation_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.13183 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Cui_Gradually_Vanishing_Bridge_for_Adversarial_Domain_Adaptation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Cui_Gradually_Vanishing_Bridge_for_Adversarial_Domain_Adaptation_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Deep Degradation Prior for Low-Quality Image Classification | Yang Wang, Yang Cao, Zheng-Jun Zha, Jing Zhang, Zhiwei Xiong | State-of-the-art image classification algorithms building upon convolutional neural networks (CNNs) are commonly trained on large annotated datasets of high-quality images. When applied to low-quality images, they will suffer a significant degradation in performance, since the structural and statistical properties of pixels in the neighborhood are obstructed by image degradation. To address this problem, this paper proposes a novel deep degradation prior for low-quality image classification. It is based on statistical observations that, in the deep representation space, image patches with structural similarity have uniform distribution even if they come from different images, and the distributions of corresponding patches in low- and high-quality images have uniform margins under the same degradation condition. Therefore, we propose a feature de-drifting module (FDM) to learn the mapping relationship between deep representations of low- and high- quality images, and leverage it as a deep degradation prior (DDP) for low-quality image classification. Since the statistical properties are independent to image content, deep degradation prior can be learned on a training set of limited images without supervision of semantic labels and served in a form of "plugging-in" module of the existing classification networks to improve their performance on degraded images. Evaluations on the benchmark dataset ImageNet-C demonstrate that our proposed DDP can improve the accuracy of the pre-trained network model by more than 20% under various degradation conditions. Even under the extreme setting that only 10 images from CUB-C dataset are used for the training of DDP, our method improves the accuracy of VGG16 on ImageNet-C from 37% to 55%. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wang_Deep_Degradation_Prior_for_Low-Quality_Image_Classification_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Deep_Degradation_Prior_for_Low-Quality_Image_Classification_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Deep_Degradation_Prior_for_Low-Quality_Image_Classification_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Visual-Textual Capsule Routing for Text-Based Video Segmentation | Bruce McIntosh, Kevin Duarte, Yogesh S Rawat, Mubarak Shah | Joint understanding of vision and natural language is a challenging problem with a wide range of applications in artificial intelligence. In this work, we focus on integration of video and text for the task of actor and action video segmentation from a sentence. We propose a capsule-based approach which performs pixel-level localization based on a natural language query describing the actor of interest. We encode both the video and textual input in the form of capsules, which provide a more effective representation in comparison with standard convolution based features. Our novel visual-textual routing mechanism allows for the fusion of video and text capsules to successfully localize the actor and action. The existing works on actor-action localization are mainly focused on localization in a single frame instead of the full video. Different from existing works, we propose to perform the localization on all frames of the video. To validate the potential of the proposed network for actor and action video localization, we extend an existing actor-action dataset (A2D) with annotations for all the frames. The experimental evaluation demonstrates the effectiveness of our capsule network for text selective actor and action localization in videos. The proposed method also improves upon the performance of the existing state-of-the art works on single frame-based localization. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/McIntosh_Visual-Textual_Capsule_Routing_for_Text-Based_Video_Segmentation_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/McIntosh_Visual-Textual_Capsule_Routing_for_Text-Based_Video_Segmentation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/McIntosh_Visual-Textual_Capsule_Routing_for_Text-Based_Video_Segmentation_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/McIntosh_Visual-Textual_Capsule_Routing_CVPR_2020_supplemental.zip | null | null |
Towards Inheritable Models for Open-Set Domain Adaptation | Jogendra Nath Kundu, Naveen Venkat, Ambareesh Revanur, Rahul M V, R. Venkatesh Babu | There has been a tremendous progress in Domain Adaptation (DA) for visual recognition tasks. Particularly, open-set DA has gained considerable attention wherein the target domain contains additional unseen categories. Existing open-set DA approaches demand access to a labeled source dataset along with unlabeled target instances. However, this reliance on co-existing source and target data is highly impractical in scenarios where data-sharing is restricted due to its proprietary nature or privacy concerns. Addressing this, we introduce a practical DA paradigm where a source-trained model is used to facilitate adaptation in the absence of the source dataset in future. To this end, we formalize knowledge inheritability as a novel concept and propose a simple yet effective solution to realize inheritable models suitable for the above practical paradigm. Further, we present an objective way to quantify inheritability to enable the selection of the most suitable source model for a given target domain, even in the absence of the source data. We provide theoretical insights followed by a thorough empirical evaluation demonstrating state-of-the-art open-set domain adaptation performance. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Kundu_Towards_Inheritable_Models_for_Open-Set_Domain_Adaptation_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.04388 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Kundu_Towards_Inheritable_Models_for_Open-Set_Domain_Adaptation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Kundu_Towards_Inheritable_Models_for_Open-Set_Domain_Adaptation_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Kundu_Towards_Inheritable_Models_CVPR_2020_supplemental.pdf | null | null |
Multi-Task Collaborative Network for Joint Referring Expression Comprehension and Segmentation | Gen Luo, Yiyi Zhou, Xiaoshuai Sun, Liujuan Cao, Chenglin Wu, Cheng Deng, Rongrong Ji | Referring expression comprehension (REC) and segmentation (RES) are two highly-related tasks, which both aim at identifying the referent according to a natural language expression. In this paper, we propose a novel Multi-task Collaborative Network (MCN) to achieve a joint learning of REC and RES for the first time. In MCN, RES can help REC to achieve better language-vision alignment, while REC can help RES to better locate the referent. In addition, we address a key challenge in this multi-task setup, i.e., the prediction conflict, with two innovative designs namely, Consistency Energy Maximization (CEM) and Adaptive Soft Non-Located Suppression (ASNLS). Specifically, CEM enables REC and RES to focus on similar visual regions by maximizing the consistency energy between two tasks. ASNLS supresses the response of unrelated regions in RES based on the prediction of REC. To validate our model, we conduct extensive experiments on three benchmark datasets of REC and RES, i.e., RefCOCO, RefCOCO+ and RefCOCOg. The experimental results report the significant performance gains of MCN over all existing methods, i.e., up to +7.13% for REC and +11.50% for RES over SOTA, which well confirm the validity of our model for joint REC and RES learning. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Luo_Multi-Task_Collaborative_Network_for_Joint_Referring_Expression_Comprehension_and_Segmentation_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.08813 | https://www.youtube.com/watch?v=-nCg_z0yqj0 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Luo_Multi-Task_Collaborative_Network_for_Joint_Referring_Expression_Comprehension_and_Segmentation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Luo_Multi-Task_Collaborative_Network_for_Joint_Referring_Expression_Comprehension_and_Segmentation_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Where, What, Whether: Multi-Modal Learning Meets Pedestrian Detection | Yan Luo, Chongyang Zhang, Muming Zhao, Hao Zhou, Jun Sun | Pedestrian detection benefits greatly from deep convolutional neural networks (CNNs). However, it is inherently hard for CNNs to handle situations in the presence of occlusion and scale variation. In this paper, we propose W^3Net, which attempts to address above challenges by decomposing the pedestrian detection task into Where, What and Whether problem directing against pedestrian localization, scale prediction and classification correspondingly. Specifically, for a pedestrian instance, we formulate its feature by three steps. i) We generate a bird view map, which is naturally free from occlusion issues, and scan all points on it to look for suitable locations for each pedestrian instance. ii) Instead of utilizing pre-fixed anchors, we model the interdependency between depth and scale aiming at generating depth-guided scales at different locations for better matching instances of different sizes. iii) We learn a latent vector shared by both visual and corpus space, by which false positives with similar vertical structure but lacking human partial features would be filtered out. We achieve state-of-the-art results on widely used datasets (Citypersons and Caltech). In particular. when evaluating on heavy occlusion subset, our results reduce MR^ -2 from 49.3% to 18.7% on Citypersons, and from 45.18% to 28.33% on Caltech. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Luo_Where_What_Whether_Multi-Modal_Learning_Meets_Pedestrian_Detection_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Luo_Where_What_Whether_Multi-Modal_Learning_Meets_Pedestrian_Detection_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Luo_Where_What_Whether_Multi-Modal_Learning_Meets_Pedestrian_Detection_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Learning Depth-Guided Convolutions for Monocular 3D Object Detection | Mingyu Ding, Yuqi Huo, Hongwei Yi, Zhe Wang, Jianping Shi, Zhiwu Lu, Ping Luo | 3D object detection from a single image without LiDAR is a challenging task due to the lack of accurate depth information. Conventional 2D convolutions are unsuitable for this task because they fail to capture local object and its scale information, which are vital for 3D object detection. To better represent 3D structure, prior arts typically transform depth maps estimated from 2D images into a pseudo-LiDAR representation, and then apply existing 3D point-cloud based object detectors. However, their results depend heavily on the accuracy of the estimated depth maps, resulting in suboptimal performance. In this work, instead of using pseudo-LiDAR representation, we improve the fundamental 2D fully convolutions by proposing a new local convolutional network (LCN), termed Depth-guided Dynamic-Depthwise-Dilated LCN (D4LCN), where the filters and their receptive fields can be automatically learned from image-based depth maps, making different pixels of different images have different filters. D4LCN overcomes the limitation of conventional 2D convolutions and narrows the gap between image representation and 3D point cloud representation. Extensive experiments show that D^4LCN outperforms existing works by large margins. For example, the relative improvement of D4LCN against the state-of-the-art on KITTI is 9.1% in the moderate setting. D4LCN ranks 1st on KITTI monocular 3D object detection benchmark at the time of submission (car, December 2019). The code is available at https://github.com/dingmyu/D4LCN | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Ding_Learning_Depth-Guided_Convolutions_for_Monocular_3D_Object_Detection_CVPR_2020_paper.pdf | http://arxiv.org/abs/1912.04799 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Ding_Learning_Depth-Guided_Convolutions_for_Monocular_3D_Object_Detection_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Ding_Learning_Depth-Guided_Convolutions_for_Monocular_3D_Object_Detection_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Ding_Learning_Depth-Guided_Convolutions_CVPR_2020_supplemental.pdf | null | null |
Your Local GAN: Designing Two Dimensional Local Attention Mechanisms for Generative Models | Giannis Daras, Augustus Odena, Han Zhang, Alexandros G. Dimakis | We introduce a new local sparse attention layer that preserves two-dimensional geometry and locality. We show that by just replacing the dense attention layer of SAGAN with our construction, we obtain very significant FID, Inception score and pure visual improvements. FID score is improved from 18.65 to 15.94 on ImageNet, keeping all other parameters the same. The sparse attention patterns that we propose for our new layer are designed using a novel information theoretic criterion that uses information flow graphs. We also present a novel way to invert Generative Adversarial Networks with attention. Our method uses the attention layer of the discriminator to create an innovative loss function. This allows us to visualize the newly introduced attention heads and show that they indeed capture interesting aspects of two-dimensional geometry of real images. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Daras_Your_Local_GAN_Designing_Two_Dimensional_Local_Attention_Mechanisms_for_CVPR_2020_paper.pdf | http://arxiv.org/abs/1911.12287 | https://www.youtube.com/watch?v=Ialaus6cu9U | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Daras_Your_Local_GAN_Designing_Two_Dimensional_Local_Attention_Mechanisms_for_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Daras_Your_Local_GAN_Designing_Two_Dimensional_Local_Attention_Mechanisms_for_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Daras_Your_Local_GAN_CVPR_2020_supplemental.pdf | null | null |
Context Aware Graph Convolution for Skeleton-Based Action Recognition | Xikun Zhang, Chang Xu, Dacheng Tao | Graph convolutional models have gained impressive successes on skeleton based human action recognition task. As graph convolution is a local operation, it cannot fully investigate non-local joints that could be vital to recognizing the action. For example, actions like typing and clapping request the cooperation of two hands, which are distant from each other in a human skeleton graph. Multiple graph convolutional layers thus tend to be stacked together to increase receptive field, which brings in computational inefficiency and optimization difficulty. But there is still no guarantee that distant joints (e.g. two hands) can be well integrated. In this paper, we propose a context aware graph convolutional network (CA-GCN). Besides the computation of localized graph convolution, CA-GCN considers a context term for each vertex by integrating information of all other vertices. Long range dependencies among joints are thus naturally integrated in context information, which then eliminates the need of stacking multiple layers to enlarge receptive field and greatly simplifies the network. Moreover, we further propose an advanced CA-GCN, in which asymmetric relevance measurement and higher level representation are utilized to compute context information for more flexibility and better performance. Besides the joint features, our CA-GCN could also be extended to handle graphs with edge (limb) features. Extensive experiments on two real-world datasets demonstrate the importance of context information and the effectiveness of the proposed CA-GCN in skeleton based action recognition. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zhang_Context_Aware_Graph_Convolution_for_Skeleton-Based_Action_Recognition_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_Context_Aware_Graph_Convolution_for_Skeleton-Based_Action_Recognition_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_Context_Aware_Graph_Convolution_for_Skeleton-Based_Action_Recognition_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Probabilistic Video Prediction From Noisy Data With a Posterior Confidence | Yunbo Wang, Jiajun Wu, Mingsheng Long, Joshua B. Tenenbaum | We study a new research problem of probabilistic future frames prediction from a sequence of noisy inputs, which is useful because it is difficult to guarantee the quality of input frames in practical spatiotemporal prediction applications. It is also challenging because it involves two levels of uncertainty: the perceptual uncertainty from noisy observations and the dynamics uncertainty in forward modeling. In this paper, we propose to tackle this problem with an end-to-end trainable model named Bayesian Predictive Network (BP-Net). Unlike previous work in stochastic video prediction that assumes spatiotemporal coherence and therefore fails to deal with perceptual uncertainty, BP-Net models both levels of uncertainty in an integrated framework. Furthermore, unlike previous work that can only provide unsorted estimations of future frames, BP-Net leverages a differentiable sequential importance sampling (SIS) approach to make future predictions based on the inference of underlying physical states, thereby providing sorted prediction candidates in accordance with the SIS importance weights, i.e., the confidences. Our experiment results demonstrate that BP-Net remarkably outperforms existing approaches on predicting future frames from noisy data. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wang_Probabilistic_Video_Prediction_From_Noisy_Data_With_a_Posterior_Confidence_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Probabilistic_Video_Prediction_From_Noisy_Data_With_a_Posterior_Confidence_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Probabilistic_Video_Prediction_From_Noisy_Data_With_a_Posterior_Confidence_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Generalizing Hand Segmentation in Egocentric Videos With Uncertainty-Guided Model Adaptation | Minjie Cai, Feng Lu, Yoichi Sato | Although the performance of hand segmentation in egocentric videos has been significantly improved by using CNNs, it still remains a challenging issue to generalize the trained models to new domains, e.g., unseen environments. In this work, we solve the hand segmentation generalization problem without requiring segmentation labels in the target domain. To this end, we propose a Bayesian CNN-based model adaptation framework for hand segmentation, which introduces and considers two key factors: 1) prediction uncertainty when the model is applied in a new domain and 2) common information about hand shapes shared across domains. Consequently, we propose an iterative self-training method for hand segmentation in the new domain, which is guided by the model uncertainty estimated by a Bayesian CNN. We further use an adversarial component in our framework to utilize shared information about hand shapes to constrain the model adaptation process. Experiments on multiple egocentric datasets show that the proposed method significantly improves the generalization performance of hand segmentation. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Cai_Generalizing_Hand_Segmentation_in_Egocentric_Videos_With_Uncertainty-Guided_Model_Adaptation_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=DElT5R-hE5Q | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Cai_Generalizing_Hand_Segmentation_in_Egocentric_Videos_With_Uncertainty-Guided_Model_Adaptation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Cai_Generalizing_Hand_Segmentation_in_Egocentric_Videos_With_Uncertainty-Guided_Model_Adaptation_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Revisiting Pose-Normalization for Fine-Grained Few-Shot Recognition | Luming Tang, Davis Wertheimer, Bharath Hariharan | Few-shot, fine-grained classification requires a model to learn subtle, fine-grained distinctions between different classes (e.g., birds) based on a few images alone. This requires a remarkable degree of invariance to pose, articulation and background. A solution is to use pose-normalized representations: first localize semantic parts in each image, and then describe images by characterizing the appearance of each part. While such representations are out of favor for fully supervised classification, we show that they are extremely effective for few-shot fine-grained classification. With a minimal increase in model capacity, pose normalization improves accuracy between 10 and 20 percentage points for shallow and deep architectures, generalizes better to new domains, and is effective for multiple few-shot algorithms and network backbones. Code is available at https://github.com/Tsingularity/PoseNorm_Fewshot. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Tang_Revisiting_Pose-Normalization_for_Fine-Grained_Few-Shot_Recognition_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.00705 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Tang_Revisiting_Pose-Normalization_for_Fine-Grained_Few-Shot_Recognition_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Tang_Revisiting_Pose-Normalization_for_Fine-Grained_Few-Shot_Recognition_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Tang_Revisiting_Pose-Normalization_for_CVPR_2020_supplemental.pdf | null | null |
Weakly-Supervised Salient Object Detection via Scribble Annotations | Jing Zhang, Xin Yu, Aixuan Li, Peipei Song, Bowen Liu, Yuchao Dai | Compared with laborious pixel-wise dense labeling, it is much easier to label data by scribbles, which only costs 1 2 seconds to label one image. However, using scribble labels to learn salient object detection has not been explored. In this paper, we propose a weakly-supervised salient object detection model to learn saliency from such annotations. In doing so, we first relabel an existing large-scale salient object detection dataset with scribbles, namely S-DUTS dataset. Since object structure and detail information is not identified by scribbles, directly training with scribble labels will lead to saliency maps of poor boundary localization. To mitigate this problem, we propose an auxiliary edge detection task to localize object edges explicitly, and a gated structure-aware loss to place constraints on the scope of structure to be recovered. Moreover, we design a scribble boosting scheme to iteratively consolidate our scribble annotations, which are then employed as supervision to learn high-quality saliency maps. As existing saliency evaluation metrics neglect to measure structure alignment of the predictions, the saliency map ranking may not comply with human perception. We present a new metric, termed saliency structure measure, as a complementary metric to evaluate sharpness of the prediction. Extensive experiments on six benchmark datasets demonstrate that our method not only outperforms existing weakly-supervised/unsupervised methods, but also is on par with several fully-supervised state-of-the-art models (Our code and data is publicly available at: https://github.com/JingZhang617/Scribble_Saliency). | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zhang_Weakly-Supervised_Salient_Object_Detection_via_Scribble_Annotations_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.07685 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_Weakly-Supervised_Salient_Object_Detection_via_Scribble_Annotations_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_Weakly-Supervised_Salient_Object_Detection_via_Scribble_Annotations_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Correspondence Networks With Adaptive Neighbourhood Consensus | Shuda Li, Kai Han, Theo W. Costain, Henry Howard-Jenkins, Victor Prisacariu | In this paper, we tackle the task of establishing dense visual correspondences between images containing objects of the same category. This is a challenging task due to large intra-class variations and a lack of dense pixel level annotations. We propose a convolutional neural network architecture, called adaptive neighbourhood consensus network (ANC-Net), that can be trained end-to-end with sparse key-point annotations, to handle this challenge. At the core of ANC-Net is our proposed non-isotropic 4D convolution kernel, which forms the building block for the adaptive neighbourhood consensus module for robust matching. We also introduce a simple and efficient multi-scale self-similarity module in ANC-Net to make the learned feature robust to intra-class variations. Furthermore, we propose a novel orthogonal loss that can enforce the one-to-one matching constraint. We thoroughly evaluate the effectiveness of our method on various benchmarks, where it substantially outperforms state-of-the-art methods. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Li_Correspondence_Networks_With_Adaptive_Neighbourhood_Consensus_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.12059 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Correspondence_Networks_With_Adaptive_Neighbourhood_Consensus_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Correspondence_Networks_With_Adaptive_Neighbourhood_Consensus_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Interactive Object Segmentation With Inside-Outside Guidance | Shiyin Zhang, Jun Hao Liew, Yunchao Wei, Shikui Wei, Yao Zhao | This paper explores how to harvest precise object segmentation masks while minimizing the human interaction cost. To achieve this, we propose an Inside-Outside Guidance (IOG) approach in this work. Concretely, we leverage an inside point that is clicked near the object center and two outside points at the symmetrical corner locations (top-left and bottom-right or top-right and bottom-left) of a tight bounding box that encloses the target object. This results in a total of one foreground click and four background clicks for segmentation. The advantages of our IOG is four-fold: 1) the two outside points can help to remove distractions from other objects or background; 2) the inside point can help to eliminate the unrelated regions inside the bounding box; 3) the inside and outside points are easily identified, reducing the confusion raised by the state-of-the-art DEXTR in labeling some extreme samples; 4) our approach naturally supports additional clicks annotations for further correction. Despite its simplicity, our IOG not only achieves state-of-the-art performance on several popular benchmarks, but also demonstrates strong generalization capability across different domains such as street scenes, aerial imagery and medical images, without fine-tuning. In addition, we also propose a simple two-stage solution that enables our IOG to produce high quality instance segmentation masks from existing datasets with off-the-shelf bounding boxes such as ImageNet and Open Images, demonstrating the superiority of our IOG as an annotation tool. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zhang_Interactive_Object_Segmentation_With_Inside-Outside_Guidance_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_Interactive_Object_Segmentation_With_Inside-Outside_Guidance_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_Interactive_Object_Segmentation_With_Inside-Outside_Guidance_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Zhang_Interactive_Object_Segmentation_CVPR_2020_supplemental.zip | null | null |
GraspNet-1Billion: A Large-Scale Benchmark for General Object Grasping | Hao-Shu Fang, Chenxi Wang, Minghao Gou, Cewu Lu | Object grasping is critical for many applications, which is also a challenging computer vision problem. However, for cluttered scene, current researches suffer from the problems of insufficient training data and the lacking of evaluation benchmarks. In this work, we contribute a large-scale grasp pose detection dataset with a unified evaluation system. Our dataset contains 97,280 RGB-D image with over one billion grasp poses. Meanwhile, our evaluation system directly reports whether a grasping is successful by analytic computation, which is able to evaluate any kind of grasp poses without exhaustively labeling ground-truth. In addition, we propose an end-to-end grasp pose prediction network given point cloud inputs, where we learn approaching direction and operation parameters in a decoupled manner. A novel grasp affinity field is also designed to improve the grasping robustness. We conduct extensive experiments to show that our dataset and evaluation system can align well with real-world experiments and our proposed network achieves the state-of-the-art performance. Our dataset, source code and models are publicly available at www.graspnet.net. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Fang_GraspNet-1Billion_A_Large-Scale_Benchmark_for_General_Object_Grasping_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Fang_GraspNet-1Billion_A_Large-Scale_Benchmark_for_General_Object_Grasping_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Fang_GraspNet-1Billion_A_Large-Scale_Benchmark_for_General_Object_Grasping_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Fang_GraspNet-1Billion_A_Large-Scale_CVPR_2020_supplemental.pdf | null | null |
Meshed-Memory Transformer for Image Captioning | Marcella Cornia, Matteo Stefanini, Lorenzo Baraldi, Rita Cucchiara | Transformer-based architectures represent the state of the art in sequence modeling tasks like machine translation and language understanding. Their applicability to multi-modal contexts like image captioning, however, is still largely under-explored. With the aim of filling this gap, we present M2 - a Meshed Transformer with Memory for Image Captioning. The architecture improves both the image encoding and the language generation steps: it learns a multi-level representation of the relationships between image regions integrating learned a priori knowledge, and uses a mesh-like connectivity at decoding stage to exploit low- and high-level features. Experimentally, we investigate the performance of the M2 Transformer and different fully-attentive models in comparison with recurrent ones. When tested on COCO, our proposal achieves a new state of the art in single-model and ensemble configurations on the "Karpathy" test split and on the online test server. We also assess its performances when describing objects unseen in the training set. Trained models and code for reproducing the experiments are publicly available at: https://github.com/aimagelab/meshed-memory-transformer. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Cornia_Meshed-Memory_Transformer_for_Image_Captioning_CVPR_2020_paper.pdf | http://arxiv.org/abs/1912.08226 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Cornia_Meshed-Memory_Transformer_for_Image_Captioning_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Cornia_Meshed-Memory_Transformer_for_Image_Captioning_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Cornia_Meshed-Memory_Transformer_for_CVPR_2020_supplemental.pdf | null | null |
HCNAF: Hyper-Conditioned Neural Autoregressive Flow and its Application for Probabilistic Occupancy Map Forecasting | Geunseob Oh, Jean-Sebastien Valois | We introduce Hyper-Conditioned Neural Autoregressive Flow (HCNAF); a powerful universal distribution approximator designed to model arbitrarily complex conditional probability density functions. HCNAF consists of a neural-net based conditional autoregressive flow (AF) and a hyper-network that can take large conditions in non-autoregressive fashion and outputs the network parameters of the AF. Like other flow models, HCNAF performs exact likelihood inference. We conduct a number of density estimation tasks on toy experiments and MNIST to demonstrate the effectiveness and attributes of HCNAF, including its generalization capability over unseen conditions and expressivity. Finally, we show that HCNAF scales up to complex high-dimensional prediction problems of the magnitude of self-driving and that HCNAF yields a state-of-the-art performance in a public self-driving dataset. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Oh_HCNAF_Hyper-Conditioned_Neural_Autoregressive_Flow_and_its_Application_for_Probabilistic_CVPR_2020_paper.pdf | http://arxiv.org/abs/1912.08111 | https://www.youtube.com/watch?v=cJeVQ_RgEs0 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Oh_HCNAF_Hyper-Conditioned_Neural_Autoregressive_Flow_and_its_Application_for_Probabilistic_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Oh_HCNAF_Hyper-Conditioned_Neural_Autoregressive_Flow_and_its_Application_for_Probabilistic_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Oh_HCNAF_Hyper-Conditioned_Neural_CVPR_2020_supplemental.pdf | null | null |
Non-Local Neural Networks With Grouped Bilinear Attentional Transforms | Lu Chi, Zehuan Yuan, Yadong Mu, Changhu Wang | Modeling spatial or temporal long-range dependency plays a key role in deep neural networks. Conventional dominant solutions include recurrent operations on sequential data or deeply stacking convolutional layers with small kernel size. Recently, a number of non-local operators (such as self-attention based) have been devised. They are typically generic and can be plugged into many existing network pipelines for globally computing among any two neurons in a feature map. This work proposes a novel non-local operator. It is inspired by the attention mechanism of human visual system, which can quickly attend to important local parts in sight and suppress other less-relevant information. The core of our method is learnable and data-adaptive bilinear attentional transform (BA-Transform), whose merits are three-folds: first, BA-Transform is versatile to model a wide spectrum of local or global attentional operations, such as emphasizing specific local regions. Each BA-Transform is learned in a data-adaptive way; Secondly, to address the discrepancy among features, we further design grouped BA-Transforms, which essentially apply different attentional operations to different groups of feature channels; Thirdly, many existing non-local operators are computation-intensive. The proposed BA-Transform is implemented by simple matrix multiplication and admits better efficacy. For empirical evaluation, we perform comprehensive experiments on two large-scale benchmarks, ImageNet and Kinetics, for image / video classification respectively. The achieved accuracies and various ablation experiments consistently demonstrate significant improvement by large margins. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Chi_Non-Local_Neural_Networks_With_Grouped_Bilinear_Attentional_Transforms_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Chi_Non-Local_Neural_Networks_With_Grouped_Bilinear_Attentional_Transforms_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Chi_Non-Local_Neural_Networks_With_Grouped_Bilinear_Attentional_Transforms_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Chi_Non-Local_Neural_Networks_CVPR_2020_supplemental.pdf | null | null |
Data-Free Knowledge Amalgamation via Group-Stack Dual-GAN | Jingwen Ye, Yixin Ji, Xinchao Wang, Xin Gao, Mingli Song | Recent advances in deep learning have provided procedures for learning one network to amalgamate multiple streams of knowledge from the pre-trained Convolutional Neural Network (CNN) models, thus reduce the annotation cost. However, almost all existing methods demand massive training data, which may be unavailable due to privacy or transmission issues. In this paper, we propose a data-free knowledge amalgamate strategy to craft a well-behaved multi-task student network from multiple single/multi-task teachers. The main idea is to construct the group-stack generative adversarial networks (GANs) which have two dual generators. First one generator is trained to collect the knowledge by reconstructing the images approximating the original dataset utilized for pre-training the teachers. Then a dual generator is trained by taking the output from the former generator as input. Finally we treat the dual part generator as the target network and regroup it. As demonstrated on several benchmarks of multi-label classification, the proposed method without any training data achieves the surprisingly competitive results, even compared with some full-supervised methods. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Ye_Data-Free_Knowledge_Amalgamation_via_Group-Stack_Dual-GAN_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.09088 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Ye_Data-Free_Knowledge_Amalgamation_via_Group-Stack_Dual-GAN_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Ye_Data-Free_Knowledge_Amalgamation_via_Group-Stack_Dual-GAN_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Ye_Data-Free_Knowledge_Amalgamation_CVPR_2020_supplemental.pdf | null | null |
JA-POLS: A Moving-Camera Background Model via Joint Alignment and Partially-Overlapping Local Subspaces | Irit Chelly, Vlad Winter, Dor Litvak, David Rosen, Oren Freifeld | Background models are widely used in computer vision. While successful Static-camera Background (SCB) models exist, Moving-camera Background (MCB) models are limited. Seemingly, there is a straightforward solution: 1) align the video frames; 2) learn an SCB model; 3) warp either original or previously-unseen frames toward the model. This approach, however, has drawbacks, especially when the accumulative camera motion is large and/or the video is long. Here we propose a purely-2D unsupervised modular method that systematically eliminates those issues. First, to estimate warps in the original video, we solve a joint-alignment problem while leveraging a certifiably-correct initialization. Next, we learn both multiple partially-overlapping local subspaces and how to predict alignments. Lastly, in test time, we warp a previously-unseen frame, based on the prediction, and project it on a subset of those subspaces to obtain a background/foreground separation. We show the method handles even large scenes with a relatively-free camera motion (provided the camera-to-scene distance does not change much) and that it not only yields State-of-the-Art results on the original video but also generalizes gracefully to previously-unseen videos of the same scene. Our code is available at https://github.com/BGU-CS-VIL/JA-POLS. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Chelly_JA-POLS_A_Moving-Camera_Background_Model_via_Joint_Alignment_and_Partially-Overlapping_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=GYhP4lXQyQQ | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Chelly_JA-POLS_A_Moving-Camera_Background_Model_via_Joint_Alignment_and_Partially-Overlapping_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Chelly_JA-POLS_A_Moving-Camera_Background_Model_via_Joint_Alignment_and_Partially-Overlapping_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Chelly_JA-POLS_A_Moving-Camera_CVPR_2020_supplemental.zip | null | null |
Mnemonics Training: Multi-Class Incremental Learning Without Forgetting | Yaoyao Liu, Yuting Su, An-An Liu, Bernt Schiele, Qianru Sun | Multi-Class Incremental Learning (MCIL) aims to learn new concepts by incrementally updating a model trained on previous concepts. However, there is an inherent trade-off to effectively learning new concepts without catastrophic forgetting of previous ones. To alleviate this issue, it has been proposed to keep around a few examples of the previous concepts but the effectiveness of this approach heavily depends on the representativeness of these examples. This paper proposes a novel and automatic framework we call mnemonics, where we parameterize exemplars and make them optimizable in an end-to-end manner. We train the framework through bilevel optimizations, i.e., model-level and exemplar-level. We conduct extensive experiments on three MCIL benchmarks, CIFAR-100, ImageNet-Subset and ImageNet, and show that using mnemonics exemplars can surpass the state-of-the-art by a large margin. Interestingly and quite intriguingly, the mnemonics exemplars tend to be on the boundaries between different classes. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Liu_Mnemonics_Training_Multi-Class_Incremental_Learning_Without_Forgetting_CVPR_2020_paper.pdf | http://arxiv.org/abs/2002.10211 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Liu_Mnemonics_Training_Multi-Class_Incremental_Learning_Without_Forgetting_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Liu_Mnemonics_Training_Multi-Class_Incremental_Learning_Without_Forgetting_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Liu_Mnemonics_Training_Multi-Class_CVPR_2020_supplemental.zip | null | null |
Orderless Recurrent Models for Multi-Label Classification | Vacit Oguz Yazici, Abel Gonzalez-Garcia, Arnau Ramisa, Bartlomiej Twardowski, Joost van de Weijer | Recurrent neural networks (RNN) are popular for many computer vision tasks, including multi-label classification. Since RNNs produce sequential outputs, labels need to be ordered for the multi-label classification task. Current approaches sort labels according to their frequency, typically ordering them in either rare-first or frequent-first. These imposed orderings do not take into account that the natural order to generate the labels can change for each image, e.g. first the dominant object before summing up the smaller objects in the image. Therefore, in this paper, we propose ways to dynamically order the ground truth labels with the predicted label sequence. This allows for the faster training of more optimal LSTM models for multi-label classification. Analysis evidences that our method does not suffer from duplicate generation, something which is common for other models. Furthermore, it outperforms other CNN-RNN models, and we show that a standard architecture of an image encoder and language decoder trained with our proposed loss obtains the state-of-the-art results on the challenging MS-COCO, WIDER Attribute and PA-100K and competitive results on NUS-WIDE. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Yazici_Orderless_Recurrent_Models_for_Multi-Label_Classification_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Yazici_Orderless_Recurrent_Models_for_Multi-Label_Classification_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Yazici_Orderless_Recurrent_Models_for_Multi-Label_Classification_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Yazici_Orderless_Recurrent_Models_CVPR_2020_supplemental.pdf | null | null |
Exploring Category-Agnostic Clusters for Open-Set Domain Adaptation | Yingwei Pan, Ting Yao, Yehao Li, Chong-Wah Ngo, Tao Mei | Unsupervised domain adaptation has received significant attention in recent years. Most of existing works tackle the closed-set scenario, assuming that the source and target domains share the exactly same categories. In practice, nevertheless, a target domain often contains samples of classes unseen in source domain (i.e., unknown class). The extension of domain adaptation from closed-set to such open-set situation is not trivial since the target samples in unknown class are not expected to align with the source. In this paper, we address this problem by augmenting the state-of-the-art domain adaptation technique, Self-Ensembling, with category-agnostic clusters in target domain. Specifically, we present Self-Ensembling with Category-agnostic Clusters (SE-CC) --- a novel architecture that steers domain adaptation with the additional guidance of category-agnostic clusters that are specific to target domain. These clustering information provides domain-specific visual cues, facilitating the generalization of Self-Ensembling for both closed-set and open-set scenarios. Technically, clustering is firstly performed over all the unlabeled target samples to obtain the category-agnostic clusters, which reveal the underlying data space structure peculiar to target domain. A clustering branch is capitalized on to ensure that the learnt representation preserves such underlying structure by matching the estimated assignment distribution over clusters to the inherent cluster distribution for each target sample. Furthermore, SE-CC enhances the learnt representation with mutual information maximization. Extensive experiments are conducted on Office and VisDA datasets for both open-set and closed-set domain adaptation, and superior results are reported when comparing to the state-of-the-art approaches. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Pan_Exploring_Category-Agnostic_Clusters_for_Open-Set_Domain_Adaptation_CVPR_2020_paper.pdf | http://arxiv.org/abs/2006.06567 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Pan_Exploring_Category-Agnostic_Clusters_for_Open-Set_Domain_Adaptation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Pan_Exploring_Category-Agnostic_Clusters_for_Open-Set_Domain_Adaptation_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
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