Search is not available for this dataset
title
string | authors
string | abstract
string | pdf
string | arXiv
string | video
string | bibtex
string | url
string | detail_url
string | tags
string | supp
string | dataset
string | string |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
LiDARsim: Realistic LiDAR Simulation by Leveraging the Real World | Sivabalan Manivasagam, Shenlong Wang, Kelvin Wong, Wenyuan Zeng, Mikita Sazanovich, Shuhan Tan, Bin Yang, Wei-Chiu Ma, Raquel Urtasun | We tackle the problem of producing realistic simulations of LiDAR point clouds, the sensor of preference for most self-driving vehicles. We argue that, by leveraging real data, we can simulate the complex world more realistically compared to employing virtual worlds built from CAD/procedural models. Towards this goal, we first build a large catalog of 3D static maps and 3D dynamic objects by driving around several cities with our self-driving fleet. We can then generate scenarios by selecting a scene from our catalog and "virtually" placing the self-driving vehicle (SDV) and a set of dynamic objects from the catalog in plausible locations in the scene. To produce realistic simulations, we develop a novel simulator that captures both the power of physics-based and learning-based simulation. We first utilize raycasting over the 3D scene and then use a deep neural network to produce deviations from the physics-based simulation, producing realistic LiDAR point clouds. We showcase LiDARsim's usefulness for perception algorithms-testing on long-tail events and end-to-end closed-loop evaluation on safety-critical scenarios. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Manivasagam_LiDARsim_Realistic_LiDAR_Simulation_by_Leveraging_the_Real_World_CVPR_2020_paper.pdf | http://arxiv.org/abs/2006.09348 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Manivasagam_LiDARsim_Realistic_LiDAR_Simulation_by_Leveraging_the_Real_World_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Manivasagam_LiDARsim_Realistic_LiDAR_Simulation_by_Leveraging_the_Real_World_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Manivasagam_LiDARsim_Realistic_LiDAR_CVPR_2020_supplemental.zip | null | null |
Counting Out Time: Class Agnostic Video Repetition Counting in the Wild | Debidatta Dwibedi, Yusuf Aytar, Jonathan Tompson, Pierre Sermanet, Andrew Zisserman | We present an approach for estimating the period with which an action is repeated in a video. The crux of the approach lies in constraining the period prediction module to use temporal self-similarity as an intermediate representation bottleneck that allows generalization to unseen repetitions in videos in the wild. We train this model, called RepNet, with a synthetic dataset that is generated from a large unlabeled video collection by sampling short clips of varying lengths and repeating them with different periods and counts. This combination of synthetic data and a powerful yet constrained model, allows us to predict periods in a class-agnostic fashion. Our model substantially exceeds the state of the art performance on existing periodicity (PERTUBE) and repetition counting (QUVA) benchmarks. We also collect a new challenging dataset called Countix ( 90 times larger than existing datasets) which captures the challenges of repetition counting in real-world videos. Project webpage: https://sites.google.com/view/repnet . | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Dwibedi_Counting_Out_Time_Class_Agnostic_Video_Repetition_Counting_in_the_CVPR_2020_paper.pdf | http://arxiv.org/abs/2006.15418 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Dwibedi_Counting_Out_Time_Class_Agnostic_Video_Repetition_Counting_in_the_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Dwibedi_Counting_Out_Time_Class_Agnostic_Video_Repetition_Counting_in_the_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Inducing Hierarchical Compositional Model by Sparsifying Generator Network | Xianglei Xing, Tianfu Wu, Song-Chun Zhu, Ying Nian Wu | This paper proposes to learn hierarchical compositional AND-OR model for interpretable image synthesis by sparsifying the generator network. The proposed method adopts the scene-objects-parts-subparts-primitives hierarchy in image representation. A scene has different types (i.e., OR) each of which consists of a number of objects (i.e., AND). This can be recursively formulated across the scene-objects-parts-subparts hierarchy and is terminated at the primitive level (e.g., wavelets-like basis). To realize this AND-OR hierarchy in image synthesis, we learn a generator network that consists of the following two components: (i) Each layer of the hierarchy is represented by an over-completed set of convolutional basis functions. Off-the-shelf convolutional neural architectures are exploited to implement the hierarchy. (ii) Sparsity-inducing constraints are introduced in end-to-end training, which induces a sparsely activated and sparsely connected AND-OR model from the initially densely connected generator network. A straightforward sparsity-inducing constraint is utilized, that is to only allow the top-k basis functions to be activated at each layer (where k is a hyper-parameter). The learned basis functions are also capable of image reconstruction to explain the input images. In experiments, the proposed method is tested on four benchmark datasets. The results show that meaningful and interpretable hierarchical representations are learned with better qualities of image synthesis and reconstruction obtained than baselines. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Xing_Inducing_Hierarchical_Compositional_Model_by_Sparsifying_Generator_Network_CVPR_2020_paper.pdf | http://arxiv.org/abs/1909.04324 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Xing_Inducing_Hierarchical_Compositional_Model_by_Sparsifying_Generator_Network_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Xing_Inducing_Hierarchical_Compositional_Model_by_Sparsifying_Generator_Network_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
What Deep CNNs Benefit From Global Covariance Pooling: An Optimization Perspective | Qilong Wang, Li Zhang, Banggu Wu, Dongwei Ren, Peihua Li, Wangmeng Zuo, Qinghua Hu | Recent works have demonstrated that global covariance pooling (GCP) has the ability to improve performance of deep convolutional neural networks (CNNs) on visual classification task. Despite considerable advance, the reasons on effectiveness of GCP on deep CNNs have not been well studied. In this paper, we make an attempt to understand what deep CNNs benefit from GCP in a viewpoint of optimization. Specifically, we explore the effect of GCP on deep CNNs in terms of the Lipschitzness of optimization loss and the predictiveness of gradients, and show that GCP can make the optimization landscape more smooth and the gradients more predictive. Furthermore, we discuss the connection between GCP and second-order optimization for deep CNNs. More importantly, above findings can account for several merits of covariance pooling for training deep CNNs that have not been recognized previously or fully explored, including significant acceleration of network convergence (i.e., the networks trained with GCP can support rapid decay of learning rates, achieving favorable performance while significantly reducing number of training epochs), stronger robustness to distorted examples generated by image corruptions and perturbations, and good generalization ability to different vision tasks, e.g., object detection and instance segmentation. We conduct extensive experiments using various deep CNN architectures on diversified tasks, and the results provide strong support to our findings. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wang_What_Deep_CNNs_Benefit_From_Global_Covariance_Pooling_An_Optimization_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.11241 | https://www.youtube.com/watch?v=Fjv7oR47V40 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_What_Deep_CNNs_Benefit_From_Global_Covariance_Pooling_An_Optimization_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_What_Deep_CNNs_Benefit_From_Global_Covariance_Pooling_An_Optimization_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Wang_What_Deep_CNNs_CVPR_2020_supplemental.pdf | null | null |
EmotiCon: Context-Aware Multimodal Emotion Recognition Using Frege's Principle | Trisha Mittal, Pooja Guhan, Uttaran Bhattacharya, Rohan Chandra, Aniket Bera, Dinesh Manocha | We present EmotiCon, a learning-based algorithm for context-aware perceived human emotion recognition from videos and images. Motivated by Frege's Context Principle from psychology, our approach combines three interpretations of context for emotion recognition. Our first interpretation is based on using multiple modalities (e.g.faces and gaits) for emotion recognition. For the second interpretation, we gather semantic context from the input image and use a self-attention-based CNN to encode this information. Finally, we use depth maps to model the third interpretation related to socio-dynamic interactions and proximity among agents. We demonstrate the efficiency of our network through experiments on EMOTIC, a benchmark dataset. We report an Average Precision (AP) score of 35.48 across 26 classes, which is an improvement of 7-8 over prior methods. We also introduce a new dataset, GroupWalk, which is a collection of videos captured in multiple real-world settings of people walking. We report an AP of 65.83 across 4 categories on GroupWalk, which is also an improvement over prior methods. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Mittal_EmotiCon_Context-Aware_Multimodal_Emotion_Recognition_Using_Freges_Principle_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=kYOFkL7n0AI | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Mittal_EmotiCon_Context-Aware_Multimodal_Emotion_Recognition_Using_Freges_Principle_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Mittal_EmotiCon_Context-Aware_Multimodal_Emotion_Recognition_Using_Freges_Principle_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Mittal_EmotiCon_Context-Aware_Multimodal_CVPR_2020_supplemental.pdf | null | null |
Universal Weighting Metric Learning for Cross-Modal Matching | Jiwei Wei, Xing Xu, Yang Yang, Yanli Ji, Zheng Wang, Heng Tao Shen | Cross-modal matching has been a highlighted research topic in both vision and language areas. Learning appropriate mining strategy to sample and weight informative pairs is crucial for the cross-modal matching performance. However, most existing metric learning methods are developed for unimodal matching, which is unsuitable for cross-modal matching on multimodal data with heterogeneous features. To address this problem, we propose a simple and interpretable universal weighting framework for cross-modal matching, which provides a tool to analyze the interpretability of various loss functions. Furthermore, we introduce a new polynomial loss under the universal weighting framework, which defines a weight function for the positive and negative informative pairs respectively. Experimental results on two image-text matching benchmarks and two video-text matching benchmarks validate the efficacy of the proposed method. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wei_Universal_Weighting_Metric_Learning_for_Cross-Modal_Matching_CVPR_2020_paper.pdf | http://arxiv.org/abs/2010.03403 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Wei_Universal_Weighting_Metric_Learning_for_Cross-Modal_Matching_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Wei_Universal_Weighting_Metric_Learning_for_Cross-Modal_Matching_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Learning a Dynamic Map of Visual Appearance | Tawfiq Salem, Scott Workman, Nathan Jacobs | The appearance of the world varies dramatically not only from place to place but also from hour to hour and month to month. Every day billions of images capture this complex relationship, many of which are associated with precise time and location metadata. We propose to use these images to construct a global-scale, dynamic map of visual appearance attributes. Such a map enables fine-grained understanding of the expected appearance at any geographic location and time. Our approach integrates dense overhead imagery with location and time metadata into a general framework capable of mapping a wide variety of visual attributes. A key feature of our approach is that it requires no manual data annotation. We demonstrate how this approach can support various applications, including image-driven mapping, image geolocalization, and metadata verification. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Salem_Learning_a_Dynamic_Map_of_Visual_Appearance_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Salem_Learning_a_Dynamic_Map_of_Visual_Appearance_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Salem_Learning_a_Dynamic_Map_of_Visual_Appearance_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Salem_Learning_a_Dynamic_CVPR_2020_supplemental.pdf | null | null |
Learning From Synthetic Animals | Jiteng Mu, Weichao Qiu, Gregory D. Hager, Alan L. Yuille | Despite great success in human parsing, progress for parsing other deformable articulated objects, like animals, is still limited by the lack of labeled data. In this paper, we use synthetic images and ground truth generated from CAD animal models to address this challenge. To bridge the domain gap between real and synthetic images, we propose a novel consistency-constrained semi-supervised learning method (CC-SSL). Our method leverages both spatial and temporal consistencies, to bootstrap weak models trained on synthetic data with unlabeled real images. We demonstrate the effectiveness of our method on highly deformable animals, such as horses and tigers. Without using any real image label, our method allows for accurate keypoint prediction on real images. Moreover, we quantitatively show that models using synthetic data achieve better generalization performance than models trained on real images across different domains in the Visual Domain Adaptation Challenge dataset. Our synthetic dataset contains 10+ animals with diverse poses and rich ground truth, which enables us to use the multi-task learning strategy to further boost models' performance. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Mu_Learning_From_Synthetic_Animals_CVPR_2020_paper.pdf | http://arxiv.org/abs/1912.08265 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Mu_Learning_From_Synthetic_Animals_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Mu_Learning_From_Synthetic_Animals_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Mu_Learning_From_Synthetic_CVPR_2020_supplemental.pdf | null | null |
3D Part Guided Image Editing for Fine-Grained Object Understanding | Zongdai Liu, Feixiang Lu, Peng Wang, Hui Miao, Liangjun Zhang, Ruigang Yang, Bin Zhou | Holistically understanding an object with its 3D movable parts is essential for visual models of a robot to interact with the world. For example, only by understanding many possible part dynamics of other vehicles (e.g., door or trunk opening, taillight blinking for changing lane), a self-driving vehicle can be success in dealing with emergency cases. However, existing visual models tackle rarely on these situations, but focus on bounding box detection. In this paper, we fill this important missing piece in autonomous driving by solving two critical issues. First, for dealing with data scarcity, we propose an effective training data generation process by fitting a 3D car model with dynamic parts to cars in real images. This allows us to directly edit the real images using the aligned 3D parts, yielding effective training data for learning robust deep neural networks (DNNs). Secondly, to benchmark the quality of 3D part understanding, we collected a large dataset in real driving scenario with cars in uncommon states (CUS), i.e. with door or trunk opened etc., which demonstrates that our trained network with edited images largely outperforms other baselines in terms of 2D detection and instance segmentation accuracy. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Liu_3D_Part_Guided_Image_Editing_for_Fine-Grained_Object_Understanding_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Liu_3D_Part_Guided_Image_Editing_for_Fine-Grained_Object_Understanding_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Liu_3D_Part_Guided_Image_Editing_for_Fine-Grained_Object_Understanding_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Learning Unseen Concepts via Hierarchical Decomposition and Composition | Muli Yang, Cheng Deng, Junchi Yan, Xianglong Liu, Dacheng Tao | Composing and recognizing new concepts from known sub-concepts has been a fundamental and challenging vision task, mainly due to 1) the diversity of sub-concepts and 2) the intricate contextuality between sub-concepts and their corresponding visual features. However, most of the current methods simply treat the contextuality as rigid semantic relationships and fail to capture fine-grained contextual correlations. We propose to learn unseen concepts in a hierarchical decomposition-and-composition manner. Considering the diversity of sub-concepts, our method decomposes each seen image into visual elements according to its labels, and learns corresponding sub-concepts in their individual subspaces. To model intricate contextuality between sub-concepts and their visual features, compositions are generated from these subspaces in three hierarchical forms, and the composed concepts are learned in a unified composition space. To further refine the captured contextual relationships, adaptively semi-positive concepts are defined and then learned with pseudo supervision exploited from the generated compositions. We validate the proposed approach on two challenging benchmarks, and demonstrate its superiority over state-of-the-art approaches. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Yang_Learning_Unseen_Concepts_via_Hierarchical_Decomposition_and_Composition_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Yang_Learning_Unseen_Concepts_via_Hierarchical_Decomposition_and_Composition_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Yang_Learning_Unseen_Concepts_via_Hierarchical_Decomposition_and_Composition_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Multi-Modality Cross Attention Network for Image and Sentence Matching | Xi Wei, Tianzhu Zhang, Yan Li, Yongdong Zhang, Feng Wu | The key of image and sentence matching is to accurately measure the visual-semantic similarity between an image and a sentence. However, most existing methods make use of only the intra-modality relationship within each modality or the inter-modality relationship between image regions and sentence words for the cross-modal matching task. Different from them, in this work, we propose a novel MultiModality Cross Attention (MMCA) Network for image and sentence matching by jointly modeling the intra-modality and inter-modality relationships of image regions and sentence words in a unified deep model. In the proposed MMCA, we design a novel cross-attention mechanism, which is able to exploit not only the intra-modality relationship within each modality, but also the inter-modality relationship between image regions and sentence words to complement and enhance each other for image and sentence matching. Extensive experimental results on two standard benchmarks including Flickr30K and MS-COCO demonstrate that the proposed model performs favorably against state-of-the-art image and sentence matching methods. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wei_Multi-Modality_Cross_Attention_Network_for_Image_and_Sentence_Matching_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Wei_Multi-Modality_Cross_Attention_Network_for_Image_and_Sentence_Matching_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Wei_Multi-Modality_Cross_Attention_Network_for_Image_and_Sentence_Matching_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Self-Supervised Domain-Aware Generative Network for Generalized Zero-Shot Learning | Jiamin Wu, Tianzhu Zhang, Zheng-Jun Zha, Jiebo Luo, Yongdong Zhang, Feng Wu | Generalized Zero-Shot Learning (GZSL) aims at recognizing both seen and unseen classes by constructing correspondence between visual and semantic embedding. However, existing methods have severely suffered from the strong bias problem, where unseen instances in target domain tend to be recognized as seen classes in source domain. To address this issue, we propose an end-to-end Self-supervised Domain-aware Generative Network (SDGN) by integrating self-supervised learning into feature generating model for unbiased GZSL. The proposed SDGN model enjoys several merits. First, we design a cross-domain feature generating module to synthesize samples with high fidelity based on class embeddings, which involves a novel target domain discriminator to preserve the domain consistency. Second, we propose a self-supervised learning module to investigate inter-domain relationships, where a set of anchors are introduced as a bridge between seen and unseen categories. In the shared space, we pull the distribution of target domain away from source domain, and obtain domain-aware features with high discriminative power for both seen and unseen classes. To our best knowledge, this is the first work to introduce self-supervised learning into GZSL as a learning guidance. Extensive experimental results on five standard benchmarks demonstrate that our model performs favorably against state-of-the-art GZSL methods. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wu_Self-Supervised_Domain-Aware_Generative_Network_for_Generalized_Zero-Shot_Learning_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=ciVpRiWzOyU | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Wu_Self-Supervised_Domain-Aware_Generative_Network_for_Generalized_Zero-Shot_Learning_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Wu_Self-Supervised_Domain-Aware_Generative_Network_for_Generalized_Zero-Shot_Learning_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Wu_Self-Supervised_Domain-Aware_Generative_CVPR_2020_supplemental.zip | null | null |
EPOS: Estimating 6D Pose of Objects With Symmetries | Tomas Hodan, Daniel Barath, Jiri Matas | We present a new method for estimating the 6D pose of rigid objects with available 3D models from a single RGB input image. The method is applicable to a broad range of objects, including challenging ones with global or partial symmetries. An object is represented by compact surface fragments which allow handling symmetries in a systematic manner. Correspondences between densely sampled pixels and the fragments are predicted using an encoder-decoder network. At each pixel, the network predicts: (i) the probability of each object's presence, (ii) the probability of the fragments given the object's presence, and (iii) the precise 3D location on each fragment. A data-dependent number of corresponding 3D locations is selected per pixel, and poses of possibly multiple object instances are estimated using a robust and efficient variant of the PnP-RANSAC algorithm. In the BOP Challenge 2019, the method outperforms all RGB and most RGB-D and D methods on the T-LESS and LM-O datasets. On the YCB-V dataset, it is superior to all competitors, with a large margin over the second-best RGB method. Source code is at: cmp.felk.cvut.cz/epos. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Hodan_EPOS_Estimating_6D_Pose_of_Objects_With_Symmetries_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.00605 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Hodan_EPOS_Estimating_6D_Pose_of_Objects_With_Symmetries_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Hodan_EPOS_Estimating_6D_Pose_of_Objects_With_Symmetries_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Object Relational Graph With Teacher-Recommended Learning for Video Captioning | Ziqi Zhang, Yaya Shi, Chunfeng Yuan, Bing Li, Peijin Wang, Weiming Hu, Zheng-Jun Zha | Taking full advantage of the information from both vision and language is critical for the video captioning task. Existing models lack adequate visual representation due to the neglect of interaction between object, and sufficient training for content-related words due to long-tailed problems. In this paper, we propose a complete video captioning system including both a novel model and an effective training strategy. Specifically, we propose an object relational graph (ORG) based encoder, which captures more detailed interaction features to enrich visual representation. Meanwhile, we design a teacher-recommended learning (TRL) method to make full use of the successful external language model (ELM) to integrate the abundant linguistic knowledge into the caption model. The ELM generates more semantically similar word proposals which extend the groundtruth words used for training to deal with the long-tailed problem. Experimental evaluations on three benchmarks: MSVD, MSR-VTT and VATEX show the proposed ORG-TRL system achieves state-of-the-art performance. Extensive ablation studies and visualizations illustrate the effectiveness of our system. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zhang_Object_Relational_Graph_With_Teacher-Recommended_Learning_for_Video_Captioning_CVPR_2020_paper.pdf | http://arxiv.org/abs/2002.11566 | https://www.youtube.com/watch?v=iB_aYjITWic | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_Object_Relational_Graph_With_Teacher-Recommended_Learning_for_Video_Captioning_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_Object_Relational_Graph_With_Teacher-Recommended_Learning_for_Video_Captioning_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Zhang_Object_Relational_Graph_CVPR_2020_supplemental.pdf | null | null |
Texture and Shape Biased Two-Stream Networks for Clothing Classification and Attribute Recognition | Yuwei Zhang, Peng Zhang, Chun Yuan, Zhi Wang | Clothes category classification and attribute recognition have achieved distinguished success with the development of deep learning. People have found that landmark detection plays a positive role in these tasks. However, little research is committed to analyzing these tasks from the perspective of clothing attributes. In our work, we explore the usefulness of landmarks and find that landmarks can assist in extracting shape features; and using landmarks for joint learning can increase classification and recognition accuracy effectively. We also find that texture features have an impelling effect on these tasks and that the pre-trained ImageNet model has good performance in extracting texture features. To this end, we propose to use two streams to enhance the extraction of shape and texture, respectively. In particular, this paper proposes a simple implementation, Texture and Shape biased Fashion Networks (TS-FashionNet). Comprehensive and rich experiments demonstrate our discoveries and the effectiveness of our model. We improve the top-3 classification accuracy by 0.83% and improve the top-3 attribute recognition recall rate by 1.39% compared to the state-of-the-art models. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zhang_Texture_and_Shape_Biased_Two-Stream_Networks_for_Clothing_Classification_and_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=BSCfxDixVN4 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_Texture_and_Shape_Biased_Two-Stream_Networks_for_Clothing_Classification_and_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_Texture_and_Shape_Biased_Two-Stream_Networks_for_Clothing_Classification_and_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Combining Detection and Tracking for Human Pose Estimation in Videos | Manchen Wang, Joseph Tighe, Davide Modolo | We propose a novel top-down approach that tackles the problem of multi-person human pose estimation and tracking in videos. In contrast to existing top-down approaches, our method is not limited by the performance of its person detector and can predict the poses of person instances not localized. It achieves this capability by propagating known person locations forward and backward in time and searching for poses in those regions. Our approach consists of three components: (i) a Clip Tracking Network that performs body joint detection and tracking simultaneously on small video clips; (ii) a Video Tracking Pipeline that merges the fixed-length tracklets produced by the Clip Tracking Network to arbitrary length tracks; and (iii) a Spatial-Temporal Merging procedure that refines the joint locations based on spatial and temporal smoothing terms. Thanks to the precision of our Clip Tracking Network and our merging procedure, our approach produces very accurate joint predictions and can fix common mistakes on hard scenarios like heavily entangled people. Our approach achieves state-of-the-art results on both joint detection and tracking, on both the PoseTrack 2017 and 2018 datasets, and against all top-down and bottom-down approaches. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wang_Combining_Detection_and_Tracking_for_Human_Pose_Estimation_in_Videos_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.13743 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Combining_Detection_and_Tracking_for_Human_Pose_Estimation_in_Videos_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Combining_Detection_and_Tracking_for_Human_Pose_Estimation_in_Videos_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Few Sample Knowledge Distillation for Efficient Network Compression | Tianhong Li, Jianguo Li, Zhuang Liu, Changshui Zhang | Deep neural network compression techniques such as pruning and weight tensor decomposition usually require fine-tuning to recover the prediction accuracy when the compression ratio is high. However, conventional fine-tuning suffers from the requirement of a large training set and the time-consuming training procedure. This paper proposes a novel solution for knowledge distillation from label-free few samples to realize both data efficiency and training/processing efficiency. We treat the original network as "teacher-net" and the compressed network as "student-net". A 1x1 convolution layer is added at the end of each layer block of the student-net, and we fit the block-level outputs of the student-net to the teacher-net by estimating the parameters of the added layers. We prove that the added layer can be merged without adding extra parameters and computation cost during inference. Experiments on multiple datasets and network architectures verify the method's effectiveness on student-nets obtained by various network pruning and weight decomposition methods. Our method can recover student-net's accuracy to the same level as conventional fine-tuning methods in minutes while using only 1% label-free data of the full training data. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Li_Few_Sample_Knowledge_Distillation_for_Efficient_Network_Compression_CVPR_2020_paper.pdf | http://arxiv.org/abs/1812.01839 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Few_Sample_Knowledge_Distillation_for_Efficient_Network_Compression_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Few_Sample_Knowledge_Distillation_for_Efficient_Network_Compression_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Li_Few_Sample_Knowledge_CVPR_2020_supplemental.pdf | null | null |
Context R-CNN: Long Term Temporal Context for Per-Camera Object Detection | Sara Beery, Guanhang Wu, Vivek Rathod, Ronny Votel, Jonathan Huang | In static monitoring cameras, useful contextual information can stretch far beyond the few seconds typical video understanding models might see: subjects may exhibit similar behavior over multiple days, and background objects remain static. Due to power and storage constraints, sampling frequencies are low, often no faster than one frame per second, and sometimes are irregular due to the use of a motion trigger. In order to perform well in this setting, models must be robust to irregular sampling rates. In this paper we propose a method that leverages temporal context from the unlabeled frames of a novel camera to improve performance at that camera. Specifically, we propose an attention-based approach that allows our model, Context R-CNN, to index into a long term memory bank constructed on a per-camera basis and aggregate contextual features from other frames to boost object detection performance on the current frame. We apply Context R-CNN to two settings: (1) species detection using camera traps, and (2) vehicle detection in traffic cameras, showing in both settings that Context R-CNN leads to performance gains over strong baselines. Moreover, we show that increasing the contextual time horizon leads to improved results. When applied to camera trap data from the Snapshot Serengeti dataset, Context R-CNN with context from up to a month of images outperforms a single-frame baseline by 17.9% mAP, and outperforms S3D (a 3d convolution based baseline) by 11.2% mAP. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Beery_Context_R-CNN_Long_Term_Temporal_Context_for_Per-Camera_Object_Detection_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Beery_Context_R-CNN_Long_Term_Temporal_Context_for_Per-Camera_Object_Detection_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Beery_Context_R-CNN_Long_Term_Temporal_Context_for_Per-Camera_Object_Detection_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Beery_Context_R-CNN_Long_CVPR_2020_supplemental.pdf | null | null |
Temporal-Context Enhanced Detection of Heavily Occluded Pedestrians | Jialian Wu, Chunluan Zhou, Ming Yang, Qian Zhang, Yuan Li, Junsong Yuan | State-of-the-art pedestrian detectors have performed promisingly on non-occluded pedestrians, yet they are still confronted by heavy occlusions. Although many previous works have attempted to alleviate the pedestrian occlusion issue, most of them rest on still images. In this paper, we exploit the local temporal context of pedestrians in videos and propose a tube feature aggregation network (TFAN) aiming at enhancing pedestrian detectors against severe occlusions. Specifically, for an occluded pedestrian in the current frame, we iteratively search for its relevant counterparts along temporal axis to form a tube. Then, features from the tube are aggregated according to an adaptive weight to enhance the feature representations of the occluded pedestrian. Furthermore, we devise a temporally discriminative embedding module (TDEM) and a part-based relation module (PRM), respectively, which adapts our approach to better handle tube drifting and heavy occlusions. Extensive experiments are conducted on three datasets, Caltech, NightOwls and KAIST, showing that our proposed method is significantly effective for heavily occluded pedestrian detection. Moreover, we achieve the state-of-the-art performance on the Caltech and NightOwls datasets. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wu_Temporal-Context_Enhanced_Detection_of_Heavily_Occluded_Pedestrians_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Wu_Temporal-Context_Enhanced_Detection_of_Heavily_Occluded_Pedestrians_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Wu_Temporal-Context_Enhanced_Detection_of_Heavily_Occluded_Pedestrians_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Wu_Temporal-Context_Enhanced_Detection_CVPR_2020_supplemental.pdf | null | null |
NMS by Representative Region: Towards Crowded Pedestrian Detection by Proposal Pairing | Xin Huang, Zheng Ge, Zequn Jie, Osamu Yoshie | Although significant progress has been made in pedestrian detection recently, pedestrian detection in crowded scenes is still challenging. The heavy occlusion between pedestrians imposes great challenges to the standard Non-Maximum Suppression (NMS). A relative low threshold of intersection over union (IoU) leads to missing highly overlapped pedestrians, while a higher one brings in plenty of false positives. To avoid such a dilemma, this paper proposes a novel Representative Region NMS (R2NMS) approach leveraging the less occluded visible parts, effectively removing the redundant boxes without bringing in many false positives. To acquire the visible parts, a novel Paired-Box Model (PBM) is proposed to simultaneously predict the full and visible boxes of a pedestrian. The full and visible boxes constitute a pair serving as the sample unit of the model, thus guaranteeing a strong correspondence between the two boxes throughout the detection pipeline. Moreover, convenient feature integration of the two boxes is allowed for the better performance on both full and visible pedestrian detection tasks. Experiments on the challenging CrowdHuman and CityPersons benchmarks sufficiently validate the effectiveness of the proposed approach on pedestrian detection in the crowded situation. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Huang_NMS_by_Representative_Region_Towards_Crowded_Pedestrian_Detection_by_Proposal_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.12729 | https://www.youtube.com/watch?v=DWtPU_LkW2w | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Huang_NMS_by_Representative_Region_Towards_Crowded_Pedestrian_Detection_by_Proposal_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Huang_NMS_by_Representative_Region_Towards_Crowded_Pedestrian_Detection_by_Proposal_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
PhraseCut: Language-Based Image Segmentation in the Wild | Chenyun Wu, Zhe Lin, Scott Cohen, Trung Bui, Subhransu Maji | We consider the problem of segmenting image regions given a natural language phrase, and study it on a novel dataset of 77,262 images and 345,486 phrase-region pairs. Our dataset is collected on top of the Visual Genome dataset and uses the existing annotations to generate a challenging set of referring phrases for which the corresponding regions are manually annotated. Phrases in our dataset correspond to multiple regions and describe a large number of object and stuff categories as well as their attributes such as color, shape, parts, and relationships with other entities in the image. Our experiments show that the scale and diversity of concepts in our dataset poses significant challenges to the existing state-of-the-art. We systematically handle the long-tail nature of these concepts and present a modular approach to combine category, attribute, and relationship cues that outperforms existing approaches. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wu_PhraseCut_Language-Based_Image_Segmentation_in_the_Wild_CVPR_2020_paper.pdf | http://arxiv.org/abs/2008.01187 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Wu_PhraseCut_Language-Based_Image_Segmentation_in_the_Wild_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Wu_PhraseCut_Language-Based_Image_Segmentation_in_the_Wild_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Wu_PhraseCut_Language-Based_Image_CVPR_2020_supplemental.pdf | https://cove.thecvf.com/datasets/355 | null |
Learning User Representations for Open Vocabulary Image Hashtag Prediction | Thibaut Durand | In this paper, we introduce an open vocabulary model for image hashtag prediction - the task of mapping an image to its accompanying hashtags. Recent work shows that to build an accurate hashtag prediction model, it is necessary to model the user because of the self-expression problem, in which similar image content may be labeled with different tags. To take into account the user behaviour, we propose a new model that extracts a representation of a user based on his/her image history. Our model allows to improve a user representation with new images or add a new user without retraining the model. Because new hashtags appear all the time on social networks, we design an open vocabulary model which can deal with new hashtags without retraining the model. Our model learns a cross-modal embedding between user conditional visual representations and hashtag word representations. Experiments on a subset of the YFCC100M dataset demonstrate the efficacy of our user representation in user conditional hashtag prediction and user retrieval. We further validate the open vocabulary prediction ability of our model. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Durand_Learning_User_Representations_for_Open_Vocabulary_Image_Hashtag_Prediction_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Durand_Learning_User_Representations_for_Open_Vocabulary_Image_Hashtag_Prediction_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Durand_Learning_User_Representations_for_Open_Vocabulary_Image_Hashtag_Prediction_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Durand_Learning_User_Representations_CVPR_2020_supplemental.pdf | null | null |
PFCNN: Convolutional Neural Networks on 3D Surfaces Using Parallel Frames | Yuqi Yang, Shilin Liu, Hao Pan, Yang Liu, Xin Tong | Surface meshes are widely used shape representations and capture finer geometry data than point clouds or volumetric grids, but are challenging to apply CNNs directly due to their non-Euclidean structure. We use parallel frames on surface to define PFCNNs that enable effective feature learning on surface meshes by mimicking standard convolutions faithfully. In particular, the convolution of PFCNN not only maps local surface patches onto flat tangent planes, but also aligns the tangent planes such that they locally form a flat Euclidean structure, thus enabling recovery of standard convolutions. The alignment is achieved by the tool of locally flat connections borrowed from discrete differential geometry, which can be efficiently encoded and computed by parallel frame fields. In addition, the lack of canonical axis on surface is handled by sampling with the frame directions. Experiments show that for tasks including classification, segmentation and registration on deformable geometric domains, as well as semantic scene segmentation on rigid domains, PFCNNs achieve robust and superior performances without using sophisticated input features than state-of-the-art surface based CNNs. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Yang_PFCNN_Convolutional_Neural_Networks_on_3D_Surfaces_Using_Parallel_Frames_CVPR_2020_paper.pdf | http://arxiv.org/abs/1808.04952 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Yang_PFCNN_Convolutional_Neural_Networks_on_3D_Surfaces_Using_Parallel_Frames_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Yang_PFCNN_Convolutional_Neural_Networks_on_3D_Surfaces_Using_Parallel_Frames_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Yang_PFCNN_Convolutional_Neural_CVPR_2020_supplemental.pdf | null | null |
Learning Weighted Submanifolds With Variational Autoencoders and Riemannian Variational Autoencoders | Nina Miolane, Susan Holmes | Manifold-valued data naturally arises in medical imaging. In cognitive neuroscience for instance, brain connectomes base the analysis of coactivation patterns between different brain regions on the analysis of the correlations of their functional Magnetic Resonance Imaging (fMRI) time series - an object thus constrained by construction to belong to the manifold of symmetric positive definite matrices. One of the challenges that naturally arises in these studies consists in finding a lower-dimensional subspace for representing such manifold-valued and typically high-dimensional data. Traditional techniques, like principal component analysis, are ill-adapted to tackle non-Euclidean spaces and may fail to achieve a lower-dimensional representation of the data - thus potentially pointing to the absence of lower-dimensional representation of the data. However, these techniques are restricted in that: (i) they do not leverage the assumption that the connectomes belong on a pre-specified manifold, therefore discarding information; (ii) they can only fit a linear subspace to the data. In this paper, we are interested in variants to learn potentially highly curved submanifolds of manifold-valued data. Motivated by the brain connectomes example, we investigate a latent variable generative model, which has the added benefit of providing us with uncertainty estimates - a crucial quantity in the medical applications we are considering. While latent variable models have been proposed to learn linear and nonlinear spaces for Euclidean data, or geodesic subspaces for manifold data, no intrinsic latent variable model exists to learn non-geodesic subspaces for manifold data. This paper fills this gap and formulates a Riemannian variational autoencoder with an intrinsic generative model of manifold-valued data. We evaluate its performances on synthetic and real datasets, by introducing the formalism of weighted Riemannian submanifolds. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Miolane_Learning_Weighted_Submanifolds_With_Variational_Autoencoders_and_Riemannian_Variational_Autoencoders_CVPR_2020_paper.pdf | http://arxiv.org/abs/1911.08147 | https://www.youtube.com/watch?v=n6YgWQIz-Ac | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Miolane_Learning_Weighted_Submanifolds_With_Variational_Autoencoders_and_Riemannian_Variational_Autoencoders_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Miolane_Learning_Weighted_Submanifolds_With_Variational_Autoencoders_and_Riemannian_Variational_Autoencoders_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Miolane_Learning_Weighted_Submanifolds_CVPR_2020_supplemental.pdf | null | null |
Learning Situational Driving | Eshed Ohn-Bar, Aditya Prakash, Aseem Behl, Kashyap Chitta, Andreas Geiger | Human drivers have a remarkable ability to drive in diverse visual conditions and situations, e.g., from maneuvering in rainy, limited visibility conditions with no lane markings to turning in a busy intersection while yielding to pedestrians. In contrast, we find that state-of-the-art sensorimotor driving models struggle when encountering diverse settings with varying relationships between observation and action. To generalize when making decisions across diverse conditions, humans leverage multiple types of situation-specific reasoning and learning strategies. Motivated by this observation, we develop a framework for learning a situational driving policy that effectively captures reasoning under varying types of scenarios. Our key idea is to learn a mixture model with a set of policies that can capture multiple driving modes. We first optimize the mixture model through behavior cloning and show it to result in significant gains in terms of driving performance in diverse conditions. We then refine the model by directly optimizing for the driving task itself, i.e., supervised with the navigation task reward. Our method is more scalable than methods assuming access to privileged information, e.g., perception labels, as it only assumes demonstration and reward-based supervision. We achieve over 98% success rate on the CARLA driving benchmark as well as state-of-the-art performance on a newly introduced generalization benchmark. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Ohn-Bar_Learning_Situational_Driving_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Ohn-Bar_Learning_Situational_Driving_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Ohn-Bar_Learning_Situational_Driving_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Pose-Guided Visible Part Matching for Occluded Person ReID | Shang Gao, Jingya Wang, Huchuan Lu, Zimo Liu | Occluded person re-identification is a challenging task as the appearance varies substantially with various obstacles, especially in the crowd scenario. To address this issue, we propose a Pose-guided Visible Part Matching (PVPM) method that jointly learns the discriminative features with pose-guided attention and self-mines the part visibility in an end-to-end framework. Specifically, the proposed PVPM includes two key components: 1) pose-guided attention (PGA) method for part feature pooling that exploits more discriminative local features; 2) pose-guided visibility predictor (PVP) that estimates whether a part suffers the occlusion or not. As there are no ground truth training annotations for the occluded part, we turn to utilize the characteristic of part correspondence in positive pairs and self-mining the correspondence scores via graph matching. The generated correspondence scores are then utilized as pseudo-labels for visibility predictor (PVP). Experimental results on three reported occluded benchmarks show that the proposed method achieves competitive performance to state-of-the-art methods. The source codes are available at https://github.com/hh23333/PVPM | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Gao_Pose-Guided_Visible_Part_Matching_for_Occluded_Person_ReID_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.00230 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Gao_Pose-Guided_Visible_Part_Matching_for_Occluded_Person_ReID_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Gao_Pose-Guided_Visible_Part_Matching_for_Occluded_Person_ReID_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Online Knowledge Distillation via Collaborative Learning | Qiushan Guo, Xinjiang Wang, Yichao Wu, Zhipeng Yu, Ding Liang, Xiaolin Hu, Ping Luo | This work presents an efficient yet effective online Knowledge Distillation method via Collaborative Learning, termed KDCL, which is able to consistently improve the generalization ability of deep neural networks (DNNs) that have different learning capacities. Unlike existing two-stage knowledge distillation approaches that pre-train a DNN with large capacity as the "teacher" and then transfer the teacher's knowledge to another "student" DNN unidirectionally (i.e. one-way), KDCL treats all DNNs as "students" and collaboratively trains them in a single stage (knowledge is transferred among arbitrary students during collaborative training), enabling parallel computing, fast computations, and appealing generalization ability. Specifically, we carefully design multiple methods to generate soft target as supervisions by effectively ensembling predictions of students and distorting the input images. Extensive experiments show that KDCL consistently improves all the "students" on different datasets, including CIFAR-100 and ImageNet. For example, when trained together by using KDCL, ResNet-50 and MobileNetV2 achieve 78.2% and 74.0% top-1 accuracy on ImageNet, outperforming the original results by 1.4% and 2.0% respectively. We also verify that models pre-trained with KDCL transfer well to object detection and semantic segmentation on MS COCO dataset. For instance, the FPN detector is improved by 0.9% mAP. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Guo_Online_Knowledge_Distillation_via_Collaborative_Learning_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Guo_Online_Knowledge_Distillation_via_Collaborative_Learning_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Guo_Online_Knowledge_Distillation_via_Collaborative_Learning_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Probabilistic Pixel-Adaptive Refinement Networks | Anne S. Wannenwetsch, Stefan Roth | Encoder-decoder networks have found widespread use in various dense prediction tasks. However, the strong reduction of spatial resolution in the encoder leads to a loss of location information as well as boundary artifacts. To address this, image-adaptive post-processing methods have shown beneficial by leveraging the high-resolution input image(s) as guidance data. We extend such approaches by considering an important orthogonal source of information: the network's confidence in its own predictions. We introduce probabilistic pixel-adaptive convolutions (PPACs), which not only depend on image guidance data for filtering, but also respect the reliability of per-pixel predictions. As such, PPACs allow for image-adaptive smoothing and simultaneously propagating pixels of high confidence into less reliable regions, while respecting object boundaries. We demonstrate their utility in refinement networks for optical flow and semantic segmentation, where PPACs lead to a clear reduction in boundary artifacts. Moreover, our proposed refinement step is able to substantially improve the accuracy on various widely used benchmarks. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wannenwetsch_Probabilistic_Pixel-Adaptive_Refinement_Networks_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.14407 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Wannenwetsch_Probabilistic_Pixel-Adaptive_Refinement_Networks_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Wannenwetsch_Probabilistic_Pixel-Adaptive_Refinement_Networks_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Wannenwetsch_Probabilistic_Pixel-Adaptive_Refinement_CVPR_2020_supplemental.pdf | null | null |
"Looking at the Right Stuff" - Guided Semantic-Gaze for Autonomous Driving | Anwesan Pal, Sayan Mondal, Henrik I. Christensen | In recent years, predicting driver's focus of attention has been a very active area of research in the autonomous driving community. Unfortunately, existing state-of-the-art techniques achieve this by relying only on human gaze information, thereby ignoring scene semantics. We propose a novel Semantics Augmented GazE (SAGE) detection approach that captures driving specific contextual information, in addition to the raw gaze. Such a combined attention mechanism serves as a powerful tool to focus on the relevant regions in an image frame in order to make driving both safe and efficient. Using this, we design a complete saliency prediction framework - SAGE-Net, which modifies the initial prediction from SAGE by taking into account vital aspects such as distance to objects (depth), ego vehicle speed, and pedestrian crossing intent. Exhaustive experiments conducted through four popular saliency algorithms show that on 49/56 (87.5%) cases - considering both the overall dataset and crucial driving scenarios, SAGE outperforms existing techniques without any additional computational overhead during the training process. The augmented dataset along with the relevant code are available as part of the supplementary material. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Pal_Looking_at_the_Right_Stuff_-_Guided_Semantic-Gaze_for_Autonomous_CVPR_2020_paper.pdf | http://arxiv.org/abs/1911.10455 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Pal_Looking_at_the_Right_Stuff_-_Guided_Semantic-Gaze_for_Autonomous_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Pal_Looking_at_the_Right_Stuff_-_Guided_Semantic-Gaze_for_Autonomous_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Pal_Looking_at_the_CVPR_2020_supplemental.pdf | null | null |
Social-STGCNN: A Social Spatio-Temporal Graph Convolutional Neural Network for Human Trajectory Prediction | Abduallah Mohamed, Kun Qian, Mohamed Elhoseiny, Christian Claudel | Better machine understanding of pedestrian behaviors enables faster progress in modeling interactions between agents such as autonomous vehicles and humans. Pedestrian trajectories are not only influenced by the pedestrian itself but also by interaction with surrounding objects. Previous methods modeled these interactions by using a variety of aggregation methods that integrate different learned pedestrians states. We propose the Social Spatio-Temporal Graph Convolutional Neural Network (Social-STGCNN), which substitutes the need of aggregation methods by modeling the interactions as a graph. Our results show an improvement over the state of art by 20% on the Final Displacement Error (FDE) and an improvement on the Average Displacement Error (ADE) with 8.5 times less parameters and up to 48 times faster inference speed than previously reported methods. In addition, our model is data efficient, and exceeds previous state of the art on the ADE metric with only 20% of the training data. We propose a kernel function to embed the social interactions between pedestrians within the adjacency matrix. Through qualitative analysis, we show that our model inherited social behaviors that can be expected between pedestrians trajectories. Code is available at https://github.com/abduallahmohamed/Social-STGCNN. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Mohamed_Social-STGCNN_A_Social_Spatio-Temporal_Graph_Convolutional_Neural_Network_for_Human_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=NQwkH9ejHsg | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Mohamed_Social-STGCNN_A_Social_Spatio-Temporal_Graph_Convolutional_Neural_Network_for_Human_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Mohamed_Social-STGCNN_A_Social_Spatio-Temporal_Graph_Convolutional_Neural_Network_for_Human_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Efficient Neural Vision Systems Based on Convolutional Image Acquisition | Pedram Pad, Simon Narduzzi, Clement Kundig, Engin Turetken, Siavash A. Bigdeli, L. Andrea Dunbar | Despite the substantial progress made in deep learning in recent years, advanced approaches remain computationally intensive. The trade-off between accuracy and computation time and energy limits their use in real-time applications on low power and other resource-constrained systems. In this paper, we tackle this fundamental challenge by introducing a hybrid optical-digital implementation of a convolutional neural network (CNN) based on engineering of the point spread function (PSF) of an optical imaging system. This is done by coding an imaging aperture such that its PSF replicates a large convolution kernel of the first layer of a pre-trained CNN. As the convolution takes place in the optical domain, it has zero cost in terms of energy consumption and has zero latency independent of the kernel size. Experimental results on two datasets demonstrate that our approach yields more than two orders of magnitude reduction in the computational cost while achieving near-state-of-the-art accuracy, or equivalently, better accuracy at the same computational cost. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Pad_Efficient_Neural_Vision_Systems_Based_on_Convolutional_Image_Acquisition_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Pad_Efficient_Neural_Vision_Systems_Based_on_Convolutional_Image_Acquisition_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Pad_Efficient_Neural_Vision_Systems_Based_on_Convolutional_Image_Acquisition_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
DAVD-Net: Deep Audio-Aided Video Decompression of Talking Heads | Xi Zhang, Xiaolin Wu, Xinliang Zhai, Xianye Ben, Chengjie Tu | Close-up talking heads are among the most common and salient object in video contents, such as face-to-face conversations in social media, teleconferences, news broadcasting, talk shows, etc. Due to the high sensitivity of human visual system to faces, compression distortions in talking heads videos are highly visible and annoying. To address this problem, we present a novel deep convolutional neural network (DCNN) method for very low bit rate video reconstruction of talking heads. The key innovation is a new DCNN architecture that can exploit the audio-video correlations to repair compression defects in the face region. We further improve reconstruction quality by embedding into our DCNN the encoder information of the video compression standards and introducing a constraining projection module in the network. Extensive experiments demonstrate that the proposed DCNN method outperforms the existing state-of-the-art methods on videos of talking heads. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zhang_DAVD-Net_Deep_Audio-Aided_Video_Decompression_of_Talking_Heads_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_DAVD-Net_Deep_Audio-Aided_Video_Decompression_of_Talking_Heads_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_DAVD-Net_Deep_Audio-Aided_Video_Decompression_of_Talking_Heads_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Zhang_DAVD-Net_Deep_Audio-Aided_CVPR_2020_supplemental.zip | null | null |
Referring Image Segmentation via Cross-Modal Progressive Comprehension | Shaofei Huang, Tianrui Hui, Si Liu, Guanbin Li, Yunchao Wei, Jizhong Han, Luoqi Liu, Bo Li | Referring image segmentation aims at segmenting the foreground masks of the entities that can well match the description given in the natural language expression. Previous approaches tackle this problem using implicit feature interaction and fusion between visual and linguistic modalities, but usually fail to explore informative words of the expression to well align features from the two modalities for accurately identifying the referred entity. In this paper, we propose a Cross-Modal Progressive Comprehension (CMPC) module and a Text-Guided Feature Exchange (TGFE) module to effectively address the challenging task. Concretely, the CMPC module first employs entity and attribute words to perceive all the related entities that might be considered by the expression. Then, the relational words are adopted to highlight the correct entity as well as suppress other irrelevant ones by multimodal graph reasoning. In addition to the CMPC module, we further leverage a simple yet effective TGFE module to integrate the reasoned multimodal features from different levels with the guidance of textual information. In this way, features from multi-levels could communicate with each other and be refined based on the textual context. We conduct extensive experiments on four popular referring segmentation benchmarks and achieve new state-of-the-art performances. Code is available at https://github.com/spyflying/CMPC-Refseg. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Huang_Referring_Image_Segmentation_via_Cross-Modal_Progressive_Comprehension_CVPR_2020_paper.pdf | http://arxiv.org/abs/2010.00514 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Huang_Referring_Image_Segmentation_via_Cross-Modal_Progressive_Comprehension_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Huang_Referring_Image_Segmentation_via_Cross-Modal_Progressive_Comprehension_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
SAPIEN: A SimulAted Part-Based Interactive ENvironment | Fanbo Xiang, Yuzhe Qin, Kaichun Mo, Yikuan Xia, Hao Zhu, Fangchen Liu, Minghua Liu, Hanxiao Jiang, Yifu Yuan, He Wang, Li Yi, Angel X. Chang, Leonidas J. Guibas, Hao Su | Building home assistant robots has long been a goal for vision and robotics researchers. To achieve this task, a simulated environment with physically realistic simulation, sufficient articulated objects, and transferability to the real robot is indispensable. Existing environments achieve these requirements for robotics simulation with different levels of simplification and focus. We take one step further in constructing an environment that supports household tasks for training robot learning algorithm. Our work, SAPIEN, is a realistic and physics-rich simulated environment that hosts a large-scale set of articulated objects. SAPIEN enables various robotic vision and interaction tasks that require detailed part-level understanding.We evaluate state-of-the-art vision algorithms for part detection and motion attribute recognition as well as demonstrate robotic interaction tasks using heuristic approaches and reinforcement learning algorithms. We hope that SAPIEN will open research directions yet to be explored, including learning cognition through interaction, part motion discovery, and construction of robotics-ready simulated game environment. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Xiang_SAPIEN_A_SimulAted_Part-Based_Interactive_ENvironment_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.08515 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Xiang_SAPIEN_A_SimulAted_Part-Based_Interactive_ENvironment_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Xiang_SAPIEN_A_SimulAted_Part-Based_Interactive_ENvironment_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Xiang_SAPIEN_A_SimulAted_CVPR_2020_supplemental.pdf | null | null |
Appearance Shock Grammar for Fast Medial Axis Extraction From Real Images | Charles-Olivier Dufresne Camaro, Morteza Rezanejad, Stavros Tsogkas, Kaleem Siddiqi, Sven Dickinson | We combine ideas from shock graph theory with more recent appearance-based methods for medial axis extraction from complex natural scenes, improving upon the present best unsupervised method, in terms of efficiency and performance. We make the following specific contributions: i) we extend the shock graph representation to the domain of real images, by generalizing the shock type definitions using local, appearance-based criteria; ii) we then use the rules of a Shock Grammar to guide our search for medial points, drastically reducing run time when compared to other methods, which exhaustively consider all points in the input image; iii) we remove the need for typical post-processing steps including thinning, non-maximum suppression, and grouping, by adhering to the Shock Grammar rules while deriving the medial axis solution; iv) finally, we raise some fundamental concerns with the evaluation scheme used in previous work and propose a more appropriate alternative for assessing the performance of medial axis extraction from scenes. Our experiments on the BMAX500 and SK-LARGE datasets demonstrate the effectiveness of our approach. We outperform the present state-of-the-art, excelling particularly in the high-precision regime, while running an order of magnitude faster and requiring no post-processing. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Camaro_Appearance_Shock_Grammar_for_Fast_Medial_Axis_Extraction_From_Real_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.02677 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Camaro_Appearance_Shock_Grammar_for_Fast_Medial_Axis_Extraction_From_Real_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Camaro_Appearance_Shock_Grammar_for_Fast_Medial_Axis_Extraction_From_Real_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Camaro_Appearance_Shock_Grammar_CVPR_2020_supplemental.pdf | null | null |
TransMatch: A Transfer-Learning Scheme for Semi-Supervised Few-Shot Learning | Zhongjie Yu, Lin Chen, Zhongwei Cheng, Jiebo Luo | The successful application of deep learning to many visual recognition tasks relies heavily on the availability of a large amount of labeled data which is usually expensive to obtain. The few-shot learning problem has attracted increasing attention from researchers for building a robust model upon only a few labeled samples. Most existing works tackle this problem under the meta-learning framework by mimicking the few-shot learning task with an episodic training strategy. In this paper, we propose a new transfer-learning framework for semi-supervised few-shot learning to fully utilize the auxiliary information from labeled base-class data and unlabeled novel-class data. The framework consists of three components: 1) pre-training a feature extractor on base-class data; 2) using the feature extractor to initialize the classifier weights for the novel classes; and 3) further updating the model with a semi-supervised learning method. Under the proposed framework, we develop a novel method for semi-supervised few-shot learning called TransMatch by instantiating the three components with imprinting and MixMatch. Extensive experiments on two popular benchmark datasets for few-shot learning, CUB-200-2011 and miniImageNet, demonstrate that our proposed method can effectively utilize the auxiliary information from labeled base-class data and unlabeled novel-class data to significantly improve the accuracy of few-shot learning task, and achieve new state-of-the-art results. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Yu_TransMatch_A_Transfer-Learning_Scheme_for_Semi-Supervised_Few-Shot_Learning_CVPR_2020_paper.pdf | http://arxiv.org/abs/1912.09033 | https://www.youtube.com/watch?v=GTJFe2ZCEms | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Yu_TransMatch_A_Transfer-Learning_Scheme_for_Semi-Supervised_Few-Shot_Learning_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Yu_TransMatch_A_Transfer-Learning_Scheme_for_Semi-Supervised_Few-Shot_Learning_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Solving Mixed-Modal Jigsaw Puzzle for Fine-Grained Sketch-Based Image Retrieval | Kaiyue Pang, Yongxin Yang, Timothy M. Hospedales, Tao Xiang, Yi-Zhe Song | ImageNet pre-training has long been considered crucial by the fine-grained sketch-based image retrieval (FG-SBIR) community due to the lack of large sketch-photo paired datasets for FG-SBIR training. In this paper, we propose a self-supervised alternative for representation pre-training. Specifically, we consider the jigsaw puzzle game of recomposing images from shuffled parts. We identify two key facets of jigsaw task design that are required for effective FG-SBIR pre-training. The first is formulating the puzzle in a mixed-modality fashion. Second we show that framing the optimisation as permutation matrix inference via Sinkhorn iterations is more effective than the common classifier formulation of Jigsaw self-supervision. Experiments show that this self-supervised pre-training strategy significantly outperforms the standard ImageNet-based pipeline across all four product-level FG-SBIR benchmarks. Interestingly it also leads to improved cross-category generalisation across both pre-train/fine-tune and fine-tune/testing stages. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Pang_Solving_Mixed-Modal_Jigsaw_Puzzle_for_Fine-Grained_Sketch-Based_Image_Retrieval_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=MQcvisO2zoY | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Pang_Solving_Mixed-Modal_Jigsaw_Puzzle_for_Fine-Grained_Sketch-Based_Image_Retrieval_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Pang_Solving_Mixed-Modal_Jigsaw_Puzzle_for_Fine-Grained_Sketch-Based_Image_Retrieval_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection | Shaoshuai Shi, Chaoxu Guo, Li Jiang, Zhe Wang, Jianping Shi, Xiaogang Wang, Hongsheng Li | We present a novel and high-performance 3D object detection framework, named PointVoxel-RCNN (PV-RCNN), for accurate 3D object detection from point clouds. Our proposed method deeply integrates both 3D voxel Convolutional Neural Network (CNN) and PointNet-based set abstraction to learn more discriminative point cloud features. It takes advantages of efficient learning and high-quality proposals of the 3D voxel CNN and the flexible receptive fields of the PointNet-based networks. Specifically, the proposed framework summarizes the 3D scene with a 3D voxel CNN into a small set of keypoints via a novel voxel set abstraction module to save follow-up computations and also to encode representative scene features. Given the high-quality 3D proposals generated by the voxel CNN, the RoI-grid pooling is proposed to abstract proposal-specific features from the keypoints to the RoI-grid points via keypoint set abstraction. Compared with conventional pooling operations, the RoI-grid feature points encode much richer context information for accurately estimating object confidences and locations. Extensive experiments on both the KITTI dataset and the Waymo Open dataset show that our proposed PV-RCNN surpasses state-of-the-art 3D detection methods with remarkable margins. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Shi_PV-RCNN_Point-Voxel_Feature_Set_Abstraction_for_3D_Object_Detection_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Shi_PV-RCNN_Point-Voxel_Feature_Set_Abstraction_for_3D_Object_Detection_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Shi_PV-RCNN_Point-Voxel_Feature_Set_Abstraction_for_3D_Object_Detection_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
A Real-Time Cross-Modality Correlation Filtering Method for Referring Expression Comprehension | Yue Liao, Si Liu, Guanbin Li, Fei Wang, Yanjie Chen, Chen Qian, Bo Li | Referring expression comprehension aims to localize the object instance described by a natural language expression. Current referring expression methods have achieved good performance. However, none of them is able to achieve real-time inference without accuracy drop. The reason for the relatively slow inference speed is that these methods artificially split the referring expression comprehension into two sequential stages including proposal generation and proposal ranking. It does not exactly conform to the habit of human cognition. To this end, we propose a novel Realtime Cross-modality Correlation Filtering method (RCCF). RCCF reformulates the referring expression comprehension as a correlation filtering process. The expression is first mapped from the language domain to the visual domain and then treated as a template (kernel) to perform correlation filtering on the image feature map. The peak value in the correlation heatmap indicates the center points of the target box. In addition, RCCF also regresses a 2-D object size and 2-D offset. The center point coordinates, object size and center point offset together to form the target bounding box. Our method runs at 40 FPS while achieving leading performance in RefClef, RefCOCO, RefCOCO+ and RefCOCOg benchmarks. In the challenging RefClef dataset, our methods almost double the state-of-the-art performance (34.70% increased to 63.79%). We hope this work can arouse more attention and studies to the new cross-modality correlation filtering framework as well as the one-stage framework for referring expression comprehension. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Liao_A_Real-Time_Cross-Modality_Correlation_Filtering_Method_for_Referring_Expression_Comprehension_CVPR_2020_paper.pdf | http://arxiv.org/abs/1909.07072 | https://www.youtube.com/watch?v=uUtzj6qKgZY | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Liao_A_Real-Time_Cross-Modality_Correlation_Filtering_Method_for_Referring_Expression_Comprehension_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Liao_A_Real-Time_Cross-Modality_Correlation_Filtering_Method_for_Referring_Expression_Comprehension_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Cross-Modal Cross-Domain Moment Alignment Network for Person Search | Ya Jing, Wei Wang, Liang Wang, Tieniu Tan | Text-based person search has drawn increasing attention due to its wide applications in video surveillance. However, most of the existing models depend heavily on paired image-text data, which is very expensive to acquire. Moreover, they always face huge performance drop when directly exploiting them to new domains. To overcome this problem, we make the first attempt to adapt the model to new target domains in the absence of pairwise labels, which combines the challenges from both cross-modal (text-based) person search and cross-domain person search. Specially, we propose a moment alignment network (MAN) to solve the cross-modal cross-domain person search task in this paper. The idea is to learn three effective moment alignments including domain alignment (DA), cross-modal alignment (CA) and exemplar alignment (EA), which together can learn domain-invariant and semantic aligned cross-modal representations to improve model generalization. Extensive experiments are conducted on CUHK Person Description dataset (CUHK-PEDES) and Richly Annotated Pedestrian dataset (RAP). Experimental results show that our proposed model achieves the state-of-the-art performances on five transfer tasks. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Jing_Cross-Modal_Cross-Domain_Moment_Alignment_Network_for_Person_Search_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Jing_Cross-Modal_Cross-Domain_Moment_Alignment_Network_for_Person_Search_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Jing_Cross-Modal_Cross-Domain_Moment_Alignment_Network_for_Person_Search_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Smooth Shells: Multi-Scale Shape Registration With Functional Maps | Marvin Eisenberger, Zorah Lahner, Daniel Cremers | We propose a novel 3D shape correspondence method based on the iterative alignment of so-called smooth shells. Smooth shells define a series of coarse-to-fine shape approximations designed to work well with multiscale algorithms. The main idea is to first align rough approximations of the geometry and then add more and more details to refine the correspondence. We fuse classical shape registration with Functional Maps by embedding the input shapes into an intrinsic-extrinsic product space. Moreover, we disambiguate intrinsic symmetries by applying a surrogate based Markov chain Monte Carlo initialization. Our method naturally handles various types of noise that commonly occur in real scans, like non-isometry or incompatible meshing. Finally, we demonstrate state-of-the-art quantitative results on several datasets and show that our pipeline produces smoother, more realistic results than other automatic matching methods in real world applications. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Eisenberger_Smooth_Shells_Multi-Scale_Shape_Registration_With_Functional_Maps_CVPR_2020_paper.pdf | http://arxiv.org/abs/1905.12512 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Eisenberger_Smooth_Shells_Multi-Scale_Shape_Registration_With_Functional_Maps_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Eisenberger_Smooth_Shells_Multi-Scale_Shape_Registration_With_Functional_Maps_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Eisenberger_Smooth_Shells_Multi-Scale_CVPR_2020_supplemental.pdf | null | null |
PnPNet: End-to-End Perception and Prediction With Tracking in the Loop | Ming Liang, Bin Yang, Wenyuan Zeng, Yun Chen, Rui Hu, Sergio Casas, Raquel Urtasun | We tackle the problem of joint perception and motion forecasting in the context of self-driving vehicles. Towards this goal we propose PnPNet, an end-to-end model that takes as input sequential sensor data, and outputs at each time step object tracks and their future trajectories. The key component is a novel tracking module that generates object tracks online from detections and exploits trajectory level features for motion forecasting. Specifically, the object tracks get updated at each time step by solving both the data association problem and the trajectory estimation problem. Importantly, the whole model is end-to-end trainable and benefits from joint optimization of all tasks. We validate PnPNet on two large-scale driving datasets, and show significant improvements over the state-of-the-art with better occlusion recovery and more accurate future prediction. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Liang_PnPNet_End-to-End_Perception_and_Prediction_With_Tracking_in_the_Loop_CVPR_2020_paper.pdf | http://arxiv.org/abs/2005.14711 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Liang_PnPNet_End-to-End_Perception_and_Prediction_With_Tracking_in_the_Loop_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Liang_PnPNet_End-to-End_Perception_and_Prediction_With_Tracking_in_the_Loop_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Liang_PnPNet_End-to-End_Perception_CVPR_2020_supplemental.zip | null | null |
Exemplar Normalization for Learning Deep Representation | Ruimao Zhang, Zhanglin Peng, Lingyun Wu, Zhen Li, Ping Luo | Normalization techniques are important in different advanced neural networks and different tasks. This work investigates a novel dynamic learning-to-normalize (L2N) problem by proposing Exemplar Normalization (EN), which is able to learn different normalization methods for different convolutional layers and image samples of a deep network. EN significantly improves the flexibility of the recently proposed switchable normalization (SN), which solves a static L2N problem by linearly combining several normalizers in each normalization layer (the combination is the same for all samples). Instead of directly employing a multi-layer perceptron (MLP) to learn data-dependent parameters as conditional batch normalization (cBN) did, the internal architecture of EN is carefully designed to stabilize its optimization, leading to many appealing benefits. (1) EN enables different convolutional layers, image samples, categories, benchmarks, and tasks to use different normalization methods, shedding light on analyzing them in a holistic view. (2) EN is effective for various network architectures and tasks. (3) It could replace any normalization layers in a deep network and still produce stable model training. Extensive experiments demonstrate the effectiveness of EN in a wide spectrum of tasks including image recognition, noisy label learning, and semantic segmentation. For example, by replacing BN in the ordinary ResNet50, improvement produced by EN is 300% more than that of SN on both ImageNet and the noisy WebVision dataset. The codes and models will be released. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zhang_Exemplar_Normalization_for_Learning_Deep_Representation_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.08761 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_Exemplar_Normalization_for_Learning_Deep_Representation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_Exemplar_Normalization_for_Learning_Deep_Representation_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Zhang_Exemplar_Normalization_for_CVPR_2020_supplemental.pdf | null | null |
Graph-Structured Referring Expression Reasoning in the Wild | Sibei Yang, Guanbin Li, Yizhou Yu | Grounding referring expressions aims to locate in an image an object referred to by a natural language expression. The linguistic structure of a referring expression provides a layout of reasoning over the visual contents, and it is often crucial to align and jointly understand the image and the referring expression. In this paper, we propose a scene graph guided modular network (SGMN), which performs reasoning over a semantic graph and a scene graph with neural modules under the guidance of the linguistic structure of the expression. In particular, we model the image as a structured semantic graph, and parse the expression into a language scene graph. The language scene graph not only decodes the linguistic structure of the expression, but also has a consistent representation with the image semantic graph. In addition to exploring structured solutions to grounding referring expressions, we also propose Ref-Reasoning, a large-scale real-world dataset for structured referring expression reasoning. We automatically generate referring expressions over the scene graphs of images using diverse expression templates and functional programs. This dataset is equipped with real-world visual contents as well as semantically rich expressions with different reasoning layouts. Experimental results show that our SGMN not only significantly outperforms existing state-of-the-art algorithms on the new Ref-Reasoning dataset, but also surpasses state-of-the-art structured methods on commonly used benchmark datasets. It can also provide interpretable visual evidences of reasoning. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Yang_Graph-Structured_Referring_Expression_Reasoning_in_the_Wild_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.08814 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Yang_Graph-Structured_Referring_Expression_Reasoning_in_the_Wild_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Yang_Graph-Structured_Referring_Expression_Reasoning_in_the_Wild_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Feature-Metric Registration: A Fast Semi-Supervised Approach for Robust Point Cloud Registration Without Correspondences | Xiaoshui Huang, Guofeng Mei, Jian Zhang | We present a fast feature-metric point cloud registration framework, which enforces the optimisation of registration by minimising a feature-metric projection error without correspondences. The advantage of the feature-metric projection error is robust to noise, outliers and density difference in contrast to the geometric projection error. Besides, minimising the feature-metric projection error does not need to search the correspondences so that the optimisation speed is fast. The principle behind the proposed method is that the feature difference is smallest if point clouds are aligned very well. We train the proposed method in a semi-supervised or unsupervised approach, which requires limited or no registration label data. Experiments demonstrate our method obtains higher accuracy and robustness than the state-of-the-art methods. Besides, experimental results show that the proposed method can handle significant noise and density difference, and solve both same-source and cross-source point cloud registration. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Huang_Feature-Metric_Registration_A_Fast_Semi-Supervised_Approach_for_Robust_Point_Cloud_CVPR_2020_paper.pdf | http://arxiv.org/abs/2005.01014 | https://www.youtube.com/watch?v=KRrCzCQNICI | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Huang_Feature-Metric_Registration_A_Fast_Semi-Supervised_Approach_for_Robust_Point_Cloud_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Huang_Feature-Metric_Registration_A_Fast_Semi-Supervised_Approach_for_Robust_Point_Cloud_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
The Garden of Forking Paths: Towards Multi-Future Trajectory Prediction | Junwei Liang, Lu Jiang, Kevin Murphy, Ting Yu, Alexander Hauptmann | This paper studies the problem of predicting the distribution over multiple possible future paths of people as they move through various visual scenes. We make two main contributions. The first contribution is a new dataset, created in a realistic 3D simulator, which is based on real world trajectory data, and then extrapolated by human annotators to achieve different latent goals. This provides the first benchmark for quantitative evaluation of the models to predict multi-future trajectories. The second contribution is a new model to generate multiple plausible future trajectories, which contains novel designs of using multi-scale location encodings and convolutional RNNs over graphs. We refer to our model as Multiverse. We show that our model achieves the best results on our dataset, as well as on the real-world VIRAT/ActEV dataset (which just contains one possible future). | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Liang_The_Garden_of_Forking_Paths_Towards_Multi-Future_Trajectory_Prediction_CVPR_2020_paper.pdf | http://arxiv.org/abs/1912.06445 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Liang_The_Garden_of_Forking_Paths_Towards_Multi-Future_Trajectory_Prediction_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Liang_The_Garden_of_Forking_Paths_Towards_Multi-Future_Trajectory_Prediction_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
PolarMask: Single Shot Instance Segmentation With Polar Representation | Enze Xie, Peize Sun, Xiaoge Song, Wenhai Wang, Xuebo Liu, Ding Liang, Chunhua Shen, Ping Luo | In this paper, we introduce an anchor-box free and single shot instance segmentation method, which is conceptually simple, fully convolutional and can be used by easily embedding it into most off-the-shelf detection methods. Our method, termed PolarMask, formulates the instance segmentation problem as predicting contour of instance through instance center classification and dense distance regression in a polar coordinate. Moreover, we propose two effective approaches to deal with sampling high-quality center examples and optimization for dense distance regression, respectively, which can significantly improve the performance and simplify the training process. Without any bells and whistles, PolarMask achieves 32.9% in mask mAP with single-model and single-scale training/testing on the challenging COCO dataset. For the first time, we show that the complexity of instance segmentation, in terms of both design and computation complexity, can be the same as bounding box object detection and this much simpler and flexible instance segmentation framework can achieve competitive accuracy. We hope that the proposed PolarMask framework can serve as a fundamental and strong baseline for single shot instance segmentation task. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Xie_PolarMask_Single_Shot_Instance_Segmentation_With_Polar_Representation_CVPR_2020_paper.pdf | http://arxiv.org/abs/1909.13226 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Xie_PolarMask_Single_Shot_Instance_Segmentation_With_Polar_Representation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Xie_PolarMask_Single_Shot_Instance_Segmentation_With_Polar_Representation_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Towards Learning a Generic Agent for Vision-and-Language Navigation via Pre-Training | Weituo Hao, Chunyuan Li, Xiujun Li, Lawrence Carin, Jianfeng Gao | Learning to navigate in a visual environment following natural-language instructions is a challenging task, because the multimodal inputs to the agent are highly variable, and the training data on a new task is often limited. In this paper, we present the first pre-training and fine-tuning paradigm for vision-and-language navigation (VLN) tasks. By training on a large amount of image-text-action triplets in a self-supervised learning manner, the pre-trained model provides generic representations of visual environments and language instructions. It can be easily used as a drop-in for existing VLN frameworks, leading to the proposed agent PREVALENT. It learns more effectively in new tasks and generalizes better in a previously unseen environment. The performance is validated on three VLN tasks. On the Room-to-Room benchmark, our model improves the state-of-the-art from 47% to 51% on success rate weighted by path length. Further, the learned representation is transferable to other VLN tasks. On two recent tasks, vision-and-dialog navigation and "Help, Anna!", the proposed PREVALENT leads to significant improvement over existing methods, achieving a new state of the art. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Hao_Towards_Learning_a_Generic_Agent_for_Vision-and-Language_Navigation_via_Pre-Training_CVPR_2020_paper.pdf | http://arxiv.org/abs/2002.10638 | https://www.youtube.com/watch?v=8ErgbHYfOzI | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Hao_Towards_Learning_a_Generic_Agent_for_Vision-and-Language_Navigation_via_Pre-Training_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Hao_Towards_Learning_a_Generic_Agent_for_Vision-and-Language_Navigation_via_Pre-Training_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Hao_Towards_Learning_a_CVPR_2020_supplemental.pdf | null | null |
Boosting Few-Shot Learning With Adaptive Margin Loss | Aoxue Li, Weiran Huang, Xu Lan, Jiashi Feng, Zhenguo Li, Liwei Wang | Few-shot learning (FSL) has attracted increasing attention in recent years but remains challenging, due to the intrinsic difficulty in learning to generalize from a few examples. This paper proposes an adaptive margin principle to improve the generalization ability of metric-based meta-learning approaches for few-shot learning problems. Specifically, we first develop a class-relevant additive margin loss, where semantic similarity between each pair of classes is considered to separate samples in the feature embedding space from similar classes. Further, we incorporate the semantic context among all classes in a sampled training task and develop a task-relevant additive margin loss to better distinguish samples from different classes. Our adaptive margin method can be easily extended to a more realistic generalized FSL setting. Extensive experiments demonstrate that the proposed method can boost the performance of current metric-based meta-learning approaches, under both the standard FSL and generalized FSL settings. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Li_Boosting_Few-Shot_Learning_With_Adaptive_Margin_Loss_CVPR_2020_paper.pdf | http://arxiv.org/abs/2005.13826 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Boosting_Few-Shot_Learning_With_Adaptive_Margin_Loss_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Boosting_Few-Shot_Learning_With_Adaptive_Margin_Loss_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
From Depth What Can You See? Depth Completion via Auxiliary Image Reconstruction | Kaiyue Lu, Nick Barnes, Saeed Anwar, Liang Zheng | Depth completion recovers dense depth from sparse measurements, e.g., LiDAR. Existing depth-only methods use sparse depth as the only input. However, these methods may fail to recover semantics consistent boundaries, or small/thin objects due to 1) the sparse nature of depth points and 2) the lack of images to provide semantic cues. This paper continues this line of research and aims to overcome the above shortcomings. The unique design of our depth completion model is that it simultaneously outputs a reconstructed image and a dense depth map. Specifically, we formulate image reconstruction from sparse depth as an auxiliary task during training that is supervised by the unlabelled gray-scale images. During testing, our system accepts sparse depth as the only input, i.e., the image is not required. Our design allows the depth completion network to learn complementary image features that help to better understand object structures. The extra supervision incurred by image reconstruction is minimal, because no annotations other than the image are needed. We evaluate our method on the KITTI depth completion benchmark and show that depth completion can be significantly improved via the auxiliary supervision of image reconstruction. Our algorithm consistently outperforms depth-only methods and is also effective for indoor scenes like NYUv2. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Lu_From_Depth_What_Can_You_See_Depth_Completion_via_Auxiliary_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=aIxvUuoT0Cg | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Lu_From_Depth_What_Can_You_See_Depth_Completion_via_Auxiliary_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Lu_From_Depth_What_Can_You_See_Depth_Completion_via_Auxiliary_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
PuppeteerGAN: Arbitrary Portrait Animation With Semantic-Aware Appearance Transformation | Zhuo Chen, Chaoyue Wang, Bo Yuan, Dacheng Tao | Portrait animation, which aims to animate a still portrait to life using poses extracted from target frames, is an important technique for many real-world entertainment applications. Although recent works have achieved highly realistic results on synthesizing or controlling human head images, the puppeteering of arbitrary portraits is still confronted by the following challenges: 1) identity/personality mismatch; 2) training data/domain limitations; and 3) low-efficiency in training/fine-tuning. In this paper, we devised a novel two-stage framework called PuppeteerGAN for solving these challenges. Specifically, we first learn identity-preserved semantic segmentation animation which executes pose retargeting between any portraits. As a general representation, the semantic segmentation results could be adapted to different datasets, environmental conditions or appearance domains. Furthermore, the synthesized semantic segmentation is filled with the appearance of the source portrait. To this end, an appearance transformation network is presented to produce fidelity output by jointly considering the wrapping of semantic features and conditional generation. After training, the two networks can directly perform end-to-end inference on unseen subjects without any retraining or fine-tuning. Extensive experiments on cross-identity/domain/resolution situations demonstrate the superiority of the proposed PuppetterGAN over existing portrait animation methods in both generation quality and inference speed. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Chen_PuppeteerGAN_Arbitrary_Portrait_Animation_With_Semantic-Aware_Appearance_Transformation_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=qcRlxI4Q-iI | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Chen_PuppeteerGAN_Arbitrary_Portrait_Animation_With_Semantic-Aware_Appearance_Transformation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Chen_PuppeteerGAN_Arbitrary_Portrait_Animation_With_Semantic-Aware_Appearance_Transformation_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Chen_PuppeteerGAN_Arbitrary_Portrait_CVPR_2020_supplemental.pdf | null | null |
Active Speakers in Context | Juan Leon Alcazar, Fabian Caba, Long Mai, Federico Perazzi, Joon-Young Lee, Pablo Arbelaez, Bernard Ghanem | Current methods for active speaker detection focus on modeling audiovisual information from a single speaker. This strategy can be adequate for addressing single-speaker scenarios, but it prevents accurate detection when the task is to identify who of many candidate speakers are talking. This paper introduces the Active Speaker Context, a novel representation that models relationships between multiple speakers over long time horizons. Our new model learns pairwise and temporal relations from a structured ensemble of audiovisual observations. Our experiments show that a structured feature ensemble already benefits active speaker detection performance. We also find that the proposed Active Speaker Context improves the state-of-the-art on the AVA-ActiveSpeaker dataset achieving an mAP of 87.1%. Moreover, ablation studies verify that this result is a direct consequence of our long-term multi-speaker analysis. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Alcazar_Active_Speakers_in_Context_CVPR_2020_paper.pdf | http://arxiv.org/abs/2005.09812 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Alcazar_Active_Speakers_in_Context_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Alcazar_Active_Speakers_in_Context_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
3DSSD: Point-Based 3D Single Stage Object Detector | Zetong Yang, Yanan Sun, Shu Liu, Jiaya Jia | Prevalence of voxel-based 3D single-stage detectors contrast with underexplored point-based methods. In this paper, we present a lightweight point-based 3D single stage object detector 3DSSD to achieve decent balance of accuracy and efficiency. In this paradigm, all upsampling layers and the refinement stage, which are indispensable in all existing point-based methods, are abandoned. We instead propose a fusion sampling strategy in downsampling process to make detection on less representative points feasible. A delicate box prediction network, including a candidate generation layer and an anchor-free regression head with a 3D center-ness assignment strategy, is developed to meet the demand of high accuracy and speed. Our 3DSSD paradigm is an elegant single-stage anchor-free one. We evaluate it on widely used KITTI dataset and more challenging nuScenes dataset. Our method outperforms all state-of-the-art voxel-based single-stage methods by a large margin, and even yields comparable performance with two-stage point-based methods, with amazing inference speed of 25+ FPS, 2x faster than former state-of-the-art point-based methods. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Yang_3DSSD_Point-Based_3D_Single_Stage_Object_Detector_CVPR_2020_paper.pdf | http://arxiv.org/abs/2002.10187 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Yang_3DSSD_Point-Based_3D_Single_Stage_Object_Detector_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Yang_3DSSD_Point-Based_3D_Single_Stage_Object_Detector_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Learning to Learn Cropping Models for Different Aspect Ratio Requirements | Debang Li, Junge Zhang, Kaiqi Huang | Image cropping aims at improving the framing of an image by removing its extraneous outer areas, which is widely used in the photography and printing industry. In some cases, the aspect ratio of cropping results is specified depending on some conditions. In this paper, we propose a meta-learning (learning to learn) based aspect ratio specified image cropping method called Mars, which can generate cropping results of different expected aspect ratios. In the proposed method, a base model and two meta-learners are obtained during the training stage. Given an aspect ratio in the test stage, a new model with new parameters can be generated from the base model. Specifically, the two meta-learners predict the parameters of the base model based on the given aspect ratio. The learning process of the proposed method is learning how to learn cropping models for different aspect ratio requirements, which is a typical meta-learning process. In the experiments, the proposed method is evaluated on three datasets and outperforms most state-of-the-art methods in terms of accuracy and speed. In addition, both the intermediate and final results show that the proposed model can predict different cropping windows for an image depending on different aspect ratio requirements. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Li_Learning_to_Learn_Cropping_Models_for_Different_Aspect_Ratio_Requirements_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Learning_to_Learn_Cropping_Models_for_Different_Aspect_Ratio_Requirements_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Learning_to_Learn_Cropping_Models_for_Different_Aspect_Ratio_Requirements_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Li_Learning_to_Learn_CVPR_2020_supplemental.pdf | null | null |
nuScenes: A Multimodal Dataset for Autonomous Driving | Holger Caesar, Varun Bankiti, Alex H. Lang, Sourabh Vora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Giancarlo Baldan, Oscar Beijbom | Robust detection and tracking of objects is crucial for the deployment of autonomous vehicle technology. Image based benchmark datasets have driven development in computer vision tasks such as object detection, tracking and segmentation of agents in the environment. Most autonomous vehicles, however, carry a combination of cameras and range sensors such as lidar and radar. As machine learning based methods for detection and tracking become more prevalent, there is a need to train and evaluate such methods on datasets containing range sensor data along with images. In this work we present nuTonomy scenes (nuScenes), the first dataset to carry the full autonomous vehicle sensor suite: 6 cameras, 5 radars and 1 lidar, all with full 360 degree field of view. nuScenes comprises 1000 scenes, each 20s long and fully annotated with 3D bounding boxes for 23 classes and 8 attributes. It has 7x as many annotations and 100x as many images as the pioneering KITTI dataset. We define novel 3D detection and tracking metrics. We also provide careful dataset analysis as well as baselines for lidar and image based detection and tracking. Data, development kit and more information are available online. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Caesar_nuScenes_A_Multimodal_Dataset_for_Autonomous_Driving_CVPR_2020_paper.pdf | http://arxiv.org/abs/1903.11027 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Caesar_nuScenes_A_Multimodal_Dataset_for_Autonomous_Driving_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Caesar_nuScenes_A_Multimodal_Dataset_for_Autonomous_Driving_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Caesar_nuScenes_A_Multimodal_CVPR_2020_supplemental.pdf | null | null |
Learning Visual Emotion Representations From Web Data | Zijun Wei, Jianming Zhang, Zhe Lin, Joon-Young Lee, Niranjan Balasubramanian, Minh Hoai, Dimitris Samaras | We present a scalable approach for learning powerful visual features for emotion recognition. A critical bottleneck in emotion recognition is the lack of large scale datasets that can be used for learning visual emotion features. To this end, we curate a webly derived large scale dataset, StockEmotion, which has more than a million images. StockEmotion uses 690 emotion related tags as labels giving us a fine-grained and diverse set of emotion labels, circumventing the difficulty in manually obtaining emotion annotations. We use this dataset to train a feature extraction network, EmotionNet, which we further regularize using joint text and visual embedding and text distillation. Our experimental results establish that EmotionNet trained on the StockEmotion dataset outperforms SOTA models on four different visual emotion tasks. An aded benefit of our joint embedding training approach is that EmotionNet achieves competitive zero-shot recognition performance against fully supervised baselines on a challenging visual emotion dataset, EMOTIC, which further highlights the generalizability of the learned emotion features. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wei_Learning_Visual_Emotion_Representations_From_Web_Data_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Wei_Learning_Visual_Emotion_Representations_From_Web_Data_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Wei_Learning_Visual_Emotion_Representations_From_Web_Data_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Fine-Grained Video-Text Retrieval With Hierarchical Graph Reasoning | Shizhe Chen, Yida Zhao, Qin Jin, Qi Wu | Cross-modal retrieval between videos and texts has attracted growing attentions due to the rapid emergence of videos on the web. The current dominant approach is to learn a joint embedding space to measure cross-modal similarities. However, simple embeddings are insufficient to represent complicated visual and textual details, such as scenes, objects, actions and their compositions. To improve fine-grained video-text retrieval, we propose a Hierarchical Graph Reasoning (HGR) model, which decomposes video-text matching into global-to-local levels. The model disentangles text into a hierarchical semantic graph including three levels of events, actions, entities, and generates hierarchical textual embeddings via attention-based graph reasoning. Different levels of texts can guide the learning of diverse and hierarchical video representations for cross-modal matching to capture both global and local details. Experimental results on three video-text datasets demonstrate the advantages of our model. Such hierarchical decomposition also enables better generalization across datasets and improves the ability to distinguish fine-grained semantic differences. Code will be released at https://github.com/cshizhe/hgr_v2t. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Chen_Fine-Grained_Video-Text_Retrieval_With_Hierarchical_Graph_Reasoning_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.00392 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Chen_Fine-Grained_Video-Text_Retrieval_With_Hierarchical_Graph_Reasoning_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Chen_Fine-Grained_Video-Text_Retrieval_With_Hierarchical_Graph_Reasoning_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Chen_Fine-Grained_Video-Text_Retrieval_CVPR_2020_supplemental.pdf | null | null |
Generative-Discriminative Feature Representations for Open-Set Recognition | Pramuditha Perera, Vlad I. Morariu, Rajiv Jain, Varun Manjunatha, Curtis Wigington, Vicente Ordonez, Vishal M. Patel | We address the problem of open-set recognition, where the goal is to determine if a given sample belongs to one of the classes used for training a model (known classes). The main challenge in open-set recognition is to disentangle open-set samples that produce high class activations from known-set samples. We propose two techniques to force class activations of open-set samples to be low. First, we train a generative model for all known classes and then augment the input with the representation obtained from the generative model to learn a classifier. This network learns to associate high classification probabilities both when image content is from the correct class as well as when the input and the reconstructed image are consistent with each other. Second, we use self-supervision to force the network to learn more informative featues when assigning class scores to improve separation of classes from each other and from open-set samples. We evaluate the performance of the proposed method with recent open-set recognition works across three datasets, where we obtain state-of-the-art results. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Perera_Generative-Discriminative_Feature_Representations_for_Open-Set_Recognition_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Perera_Generative-Discriminative_Feature_Representations_for_Open-Set_Recognition_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Perera_Generative-Discriminative_Feature_Representations_for_Open-Set_Recognition_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Perera_Generative-Discriminative_Feature_Representations_CVPR_2020_supplemental.pdf | null | null |
RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds | Qingyong Hu, Bo Yang, Linhai Xie, Stefano Rosa, Yulan Guo, Zhihua Wang, Niki Trigoni, Andrew Markham | We study the problem of efficient semantic segmentation for large-scale 3D point clouds. By relying on expensive sampling techniques or computationally heavy pre/post-processing steps, most existing approaches are only able to be trained and operate over small-scale point clouds. In this paper, we introduce RandLA-Net, an efficient and lightweight neural architecture to directly infer per-point semantics for large-scale point clouds. The key to our approach is to use random point sampling instead of more complex point selection approaches. Although remarkably computation and memory efficient, random sampling can discard key features by chance. To overcome this, we introduce a novel local feature aggregation module to progressively increase the receptive field for each 3D point, thereby effectively preserving geometric details. Extensive experiments show that our RandLA-Net can process 1 million points in a single pass with up to 200x faster than existing approaches. Moreover, our RandLA-Net clearly surpasses state-of-the-art approaches for semantic segmentation on two large-scale benchmarks Semantic3D and SemanticKITTI. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Hu_RandLA-Net_Efficient_Semantic_Segmentation_of_Large-Scale_Point_Clouds_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Hu_RandLA-Net_Efficient_Semantic_Segmentation_of_Large-Scale_Point_Clouds_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Hu_RandLA-Net_Efficient_Semantic_Segmentation_of_Large-Scale_Point_Clouds_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Hu_RandLA-Net_Efficient_Semantic_CVPR_2020_supplemental.pdf | null | null |
Learning to Structure an Image With Few Colors | Yunzhong Hou, Liang Zheng, Stephen Gould | Color and structure are the two pillars that construct an image. Usually, the structure is well expressed through a rich spectrum of colors, allowing objects in an image to be recognized by neural networks. However, under extreme limitations of color space, the structure tends to vanish, and thus a neural network might fail to understand the image. Interested in exploring this interplay between color and structure, we study the scientific problem of identifying and preserving the most informative image structures while constraining the color space to just a few bits, such that the resulting image can be recognized with possibly high accuracy. To this end, we propose a color quantization network, ColorCNN, which learns to structure the images from the classification loss in an end-to-end manner. Given a color space size, ColorCNN quantizes colors in the original image by generating a color index map and an RGB color palette. Then, this color-quantized image is fed to a pre-trained task network to evaluate its performance. In our experiment, with only a 1-bit color space (i.e., two colors), the proposed network achieves 82.1% top-1 accuracy on the CIFAR10 dataset, outperforming traditional color quantization methods by a large margin. For applications, when encoded with PNG, the proposed color quantization shows superiority over other image compression methods in the extremely low bit-rate regime. The code is available at https://github.com/hou-yz/color_distillation. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Hou_Learning_to_Structure_an_Image_With_Few_Colors_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.07848 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Hou_Learning_to_Structure_an_Image_With_Few_Colors_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Hou_Learning_to_Structure_an_Image_With_Few_Colors_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Discriminative Multi-Modality Speech Recognition | Bo Xu, Cheng Lu, Yandong Guo, Jacob Wang | Vision is often used as a complementary modality for audio speech recognition (ASR), especially in the noisy environment where performance of solo audio modality significantly deteriorates. After combining visual modality, ASR is upgraded to the multi-modality speech recognition (MSR). In this paper, we propose a two-stage speech recognition model. In the first stage, the target voice is separated from background noises with help from the corresponding visual information of lip movements, making the model 'listen' clearly. At the second stage, the audio modality combines visual modality again to better understand the speech by a MSR sub-network, further improving the recognition rate. There are some other key contributions: we introduce a pseudo-3D residual convolution (P3D)-based visual front-end to extract more discriminative features; we upgrade the temporal convolution block from 1D ResNet with the temporal convolutional network (TCN), which is more suitable for the temporal tasks; the MSR sub-network is built on the top of Element-wise-Attention Gated Recurrent Unit (EleAtt-GRU), which is more effective than Transformer in long sequences. We conducted extensive experiments on the LRS3-TED and the LRW datasets. Our two-stage model (audio enhanced multi-modality speech recognition, AE-MSR) consistently achieves the state-of-the-art performance by a significant margin, which demonstrates the necessity and effectiveness of AE-MSR. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Xu_Discriminative_Multi-Modality_Speech_Recognition_CVPR_2020_paper.pdf | http://arxiv.org/abs/2005.05592 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Xu_Discriminative_Multi-Modality_Speech_Recognition_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Xu_Discriminative_Multi-Modality_Speech_Recognition_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Xu_Discriminative_Multi-Modality_Speech_CVPR_2020_supplemental.pdf | null | null |
Improving Convolutional Networks With Self-Calibrated Convolutions | Jiang-Jiang Liu, Qibin Hou, Ming-Ming Cheng, Changhu Wang, Jiashi Feng | Recent advances on CNNs are mostly devoted to designing more complex architectures to enhance their representation learning capacity. In this paper, we consider how to improve the basic convolutional feature transformation process of CNNs without tuning the model architectures. To this end, we present a novel self-calibrated convolutions that explicitly expand fields-of-view of each convolutional layers through internal communications and hence enrich the output features. In particular, unlike the standard convolutions that fuse spatial and channel-wise information using small kernels (e.g., 3x3), self-calibrated convolutions adaptively build long-range spatial and inter-channel dependencies around each spatial location through a novel self-calibration operation. Thus, it can help CNNs generate more discriminative representations by explicitly incorporating richer information. Our self-calibrated convolution design is simple and generic, and can be easily applied to augment standard convolutional layers without introducing extra parameters and complexity. Extensive experiments demonstrate that when applying self-calibrated convolutions into different backbones, our networks can significantly improve the baseline models in a variety of vision tasks, including image recognition, object detection, instance segmentation, and keypoint detection, with no need to change the network architectures. We hope this work could provide a promising way for future research in designing novel convolutional feature transformations for improving convolutional networks. Code is available on the project page. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Liu_Improving_Convolutional_Networks_With_Self-Calibrated_Convolutions_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Liu_Improving_Convolutional_Networks_With_Self-Calibrated_Convolutions_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Liu_Improving_Convolutional_Networks_With_Self-Calibrated_Convolutions_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
CenterMask: Real-Time Anchor-Free Instance Segmentation | Youngwan Lee, Jongyoul Park | We propose a simple yet efficient anchor-free instance segmentation, called CenterMask, that adds a novel spatial attention-guided mask (SAG-Mask) branch to anchor-free one stage object detector (FCOS) in the same vein with Mask R-CNN. Plugged into the FCOS object detector, the SAG-Mask branch predicts a segmentation mask on each box with the spatial attention map that helps to focus on informative pixels and suppress noise. We also present an improved backbone networks, VoVNetV2, with two effective strategies: (1) residual connection for alleviating the optimization problem of larger VoVNet [??] and (2) effective Squeeze-Excitation (eSE) dealing with the channel information loss problem of original SE. With SAG-Mask and VoVNetV2, we deign CenterMask and CenterMask-Lite that are targeted to large and small models, respectively. Using the same ResNet-101-FPN backbone, CenterMask achieves 38.3%, surpassing all previous state-of-the-art methods while at a much faster speed. CenterMask-Lite also outperforms the state-of-the-art by large margins at over 35fps on Titan Xp. We hope that CenterMask and VoVNetV2 can serve as a solid baseline of real-time instance segmentation and backbone network for various vision tasks, respectively. The Code is available at https://github.com/youngwanLEE/CenterMask. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Lee_CenterMask_Real-Time_Anchor-Free_Instance_Segmentation_CVPR_2020_paper.pdf | http://arxiv.org/abs/1911.06667 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Lee_CenterMask_Real-Time_Anchor-Free_Instance_Segmentation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Lee_CenterMask_Real-Time_Anchor-Free_Instance_Segmentation_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Where Does It Exist: Spatio-Temporal Video Grounding for Multi-Form Sentences | Zhu Zhang, Zhou Zhao, Yang Zhao, Qi Wang, Huasheng Liu, Lianli Gao | In this paper, we consider a novel task, Spatio-Temporal Video Grounding for Multi-Form Sentences (STVG). Given an untrimmed video and a declarative/interrogative sentence depicting an object, STVG aims to localize the spatio-temporal tube of the queried object. STVG has two challenging settings: (1) We need to localize spatio-temporal object tubes from untrimmed videos, where the object may only exist in a very small segment of the video; (2) We deal with multi-form sentences, including the declarative sentences with explicit objects and interrogative sentences with unknown objects. Existing methods cannot tackle the STVG task due to the ineffective tube pre-generation and the lack of object relationship modeling. Thus, we then propose a novel Spatio-Temporal Graph Reasoning Network (STGRN) for this task. First, we build a spatio-temporal region graph to capture the region relationships with temporal object dynamics, which involves the implicit and explicit spatial subgraphs in each frame and the temporal dynamic subgraph across frames. We then incorporate textual clues into the graph and develop the multi-step cross-modal graph reasoning. Next, we introduce a spatio-temporal localizer with a dynamic selection method to directly retrieve the spatio-temporal tubes without tube pre-generation. Moreover, we contribute a large-scale video grounding dataset VidSTG based on video relation dataset VidOR. The extensive experiments demonstrate the effectiveness of our method. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zhang_Where_Does_It_Exist_Spatio-Temporal_Video_Grounding_for_Multi-Form_Sentences_CVPR_2020_paper.pdf | http://arxiv.org/abs/2001.06891 | https://www.youtube.com/watch?v=c25XccOQ7UQ | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_Where_Does_It_Exist_Spatio-Temporal_Video_Grounding_for_Multi-Form_Sentences_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_Where_Does_It_Exist_Spatio-Temporal_Video_Grounding_for_Multi-Form_Sentences_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Zhang_Where_Does_It_CVPR_2020_supplemental.pdf | null | null |
Autolabeling 3D Objects With Differentiable Rendering of SDF Shape Priors | Sergey Zakharov, Wadim Kehl, Arjun Bhargava, Adrien Gaidon | We present an automatic annotation pipeline to recover 9D cuboids and 3D shapes from pre-trained off-the-shelf 2D detectors and sparse LIDAR data. Our autolabeling method solves an ill-posed inverse problem by considering learned shape priors and optimizing geometric and physical parameters. To address this challenging problem, we apply a novel differentiable shape renderer to signed distance fields (SDF), leveraged together with normalized object coordinate spaces (NOCS). Initially trained on synthetic data to predict shape and coordinates, our method uses these predictions for projective and geometric alignment over real samples. Moreover, we also propose a curriculum learning strategy, iteratively retraining on samples of increasing difficulty in subsequent self-improving annotation rounds. Our experiments on the KITTI3D dataset show that we can recover a substantial amount of accurate cuboids, and that these autolabels can be used to train 3D vehicle detectors with state-of-the-art results. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zakharov_Autolabeling_3D_Objects_With_Differentiable_Rendering_of_SDF_Shape_Priors_CVPR_2020_paper.pdf | http://arxiv.org/abs/1911.11288 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zakharov_Autolabeling_3D_Objects_With_Differentiable_Rendering_of_SDF_Shape_Priors_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zakharov_Autolabeling_3D_Objects_With_Differentiable_Rendering_of_SDF_Shape_Priors_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Zakharov_Autolabeling_3D_Objects_CVPR_2020_supplemental.zip | null | null |
Adaptive Fractional Dilated Convolution Network for Image Aesthetics Assessment | Qiuyu Chen, Wei Zhang, Ning Zhou, Peng Lei, Yi Xu, Yu Zheng, Jianping Fan | To leverage deep learning for image aesthetics assessment, one critical but unsolved issue is how to seamlessly incorporate the information of image aspect ratios to learn more robust models. In this paper, an adaptive fractional dilated convolution (AFDC), which is aspect-ratio-embedded, composition-preserving and parameter-free, is developed to tackle this issue natively in convolutional kernel level. Specifically, the fractional dilated kernel is adaptively constructed according to the image aspect ratios, where the interpolation of nearest two integer dilated kernels are used to cope with the misalignment of fractional sampling. Moreover, we provide a concise formulation for mini-batch training and utilize a grouping strategy to reduce computational overhead. As a result, it can be easily implemented by common deep learning libraries and plugged into popular CNN architectures in a computation-efficient manner. Our experimental results demonstrate that our proposed method achieves state-of-the-art performance on image aesthetics assessment over the AVA dataset. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Chen_Adaptive_Fractional_Dilated_Convolution_Network_for_Image_Aesthetics_Assessment_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.03015 | https://www.youtube.com/watch?v=f113k0CZyfw | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Chen_Adaptive_Fractional_Dilated_Convolution_Network_for_Image_Aesthetics_Assessment_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Chen_Adaptive_Fractional_Dilated_Convolution_Network_for_Image_Aesthetics_Assessment_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.