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ILFO: Adversarial Attack on Adaptive Neural Networks | Mirazul Haque, Anki Chauhan, Cong Liu, Wei Yang | With the increasing number of layers and parameters in neural networks, the energy consumption of neural networks has become a great concern to society, especially to users of handheld or embedded devices. In this paper, we investigate the robustness of neural networks against energy-oriented attacks. Specifically, we propose ILFO (Intermediate Output-Based Loss Function Optimization) attack against a common type of energy-saving neural networks, Adaptive Neural Networks (AdNN). AdNNs save energy consumption by dynamically deactivating part of its model based on the need of the inputs. ILFO leverages intermediate output as a proxy to infer the relation between input and its corresponding energy consumption. ILFO has shown an increase up to 100 % of the FLOPs (floating-point operations per second) reduced by AdNNs with minimum noise added to input images. To our knowledge, this is the first attempt to attack the energy consumption of an AdNN. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Haque_ILFO_Adversarial_Attack_on_Adaptive_Neural_Networks_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Haque_ILFO_Adversarial_Attack_on_Adaptive_Neural_Networks_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Haque_ILFO_Adversarial_Attack_on_Adaptive_Neural_Networks_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Haque_ILFO_Adversarial_Attack_CVPR_2020_supplemental.zip | null | null |
Learning to Evaluate Perception Models Using Planner-Centric Metrics | Jonah Philion, Amlan Kar, Sanja Fidler | Variants of accuracy and precision are the gold-standard by which the computer vision community measures progress of perception algorithms. One reason for the ubiquity of these metrics is that they are largely task-agnostic; we in general seek to detect zero false negatives or positives. The downside of these metrics is that, at worst, they penalize all incorrect detections equally without conditioning on the task or scene, and at best, heuristics need to be chosen to ensure that different mistakes count differently. In this paper, we propose a principled metric for 3D object detection specifically for the task of self-driving. The core idea behind our metric is to isolate the task of object detection and measure the impact the produced detections would induce on the downstream task of driving. Without hand-designing it to, we find that our metric penalizes many of the mistakes that other metrics penalize by design. In addition, our metric downweighs detections based on additional factors such as distance from a detection to the ego car and the speed of the detection in intuitive ways that other detection metrics do not. For human evaluation, we generate scenes in which standard metrics and our metric disagree and find that humans side with our metric 79% of the time. Our project page including an evaluation server can be found at https://nv-tlabs.github.io/detection-relevance. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Philion_Learning_to_Evaluate_Perception_Models_Using_Planner-Centric_Metrics_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.08745 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Philion_Learning_to_Evaluate_Perception_Models_Using_Planner-Centric_Metrics_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Philion_Learning_to_Evaluate_Perception_Models_Using_Planner-Centric_Metrics_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Hierarchical Clustering With Hard-Batch Triplet Loss for Person Re-Identification | Kaiwei Zeng, Munan Ning, Yaohua Wang, Yang Guo | For clustering-guided fully unsupervised person reidentification (re-ID) methods, the quality of pseudo labels generated by clustering directly decides the model performance. In order to improve the quality of pseudo labels in existing methods, we propose the HCT method which combines hierarchical clustering with hard-batch triplet loss. The key idea of HCT is to make full use of the similarity among samples in the target dataset through hierarchical clustering, reduce the influence of hard examples through hard-batch triplet loss, so as to generate high quality pseudo labels and improve model performance. Specifically, (1) we use hierarchical clustering to generate pseudo labels, (2) we use PK sampling in each iteration to generate a new dataset for training, (3) we conduct training with hard-batch triplet loss and evaluate model performance in each iteration. We evaluate our model on Market-1501 and DukeMTMC-reID. Results show that HCT achieves 56.4% mAP on Market-1501 and 50.7% mAP on DukeMTMC-reID which surpasses state-of-the-arts a lot in fully unsupervised re-ID and even better than most unsupervised domain adaptation (UDA) methods which use the labeled source dataset. Code will be released soon on https://github.com/zengkaiwei/HCT | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zeng_Hierarchical_Clustering_With_Hard-Batch_Triplet_Loss_for_Person_Re-Identification_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=xw5Yuo-m9bQ | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zeng_Hierarchical_Clustering_With_Hard-Batch_Triplet_Loss_for_Person_Re-Identification_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zeng_Hierarchical_Clustering_With_Hard-Batch_Triplet_Loss_for_Person_Re-Identification_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Fast Template Matching and Update for Video Object Tracking and Segmentation | Mingjie Sun, Jimin Xiao, Eng Gee Lim, Bingfeng Zhang, Yao Zhao | In this paper, the main task we aim to tackle is the multi-instance semi-supervised video object segmentation across a sequence of frames where only the first-frame box-level ground-truth is provided. Detection-based algorithms are widely adopted to handle this task, and the challenges lie in the selection of the matching method to predict the result as well as to decide whether to update the target template using the newly predicted result. The existing methods, however, make these selections in a rough and inflexible way, compromising their performance. To overcome this limitation, we propose a novel approach which utilizes reinforcement learning to make these two decisions at the same time. Specifically, the reinforcement learning agent learns to decide whether to update the target template according to the quality of the predicted result. The choice of the matching method will be determined at the same time, based on the action history of the reinforcement learning agent. Experiments show that our method is almost 10 times faster than the previous state-of-the-art method with even higher accuracy (region similarity of 69.1% on DAVIS 2017 dataset). | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Sun_Fast_Template_Matching_and_Update_for_Video_Object_Tracking_and_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.07538 | https://www.youtube.com/watch?v=DBI0V9W-9Gk | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Sun_Fast_Template_Matching_and_Update_for_Video_Object_Tracking_and_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Sun_Fast_Template_Matching_and_Update_for_Video_Object_Tracking_and_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
TCTS: A Task-Consistent Two-Stage Framework for Person Search | Cheng Wang, Bingpeng Ma, Hong Chang, Shiguang Shan, Xilin Chen | The state of the art person search methods separate person search into detection and re-ID stages, but ignore the consistency between these two stages. The general person detector has no special attention on the query target; The re-ID model is trained on hand-drawn bounding boxes which are not available in person search. To address the consistency problem, we introduce a Task-Consist Two-Stage (TCTS) person search framework, includes an identity-guided query (IDGQ) detector and a Detection Results Adapted (DRA) re-ID model. In the detection stage, the IDGQ detector learns an auxiliary identity branch to compute query similarity scores for proposals. With consideration of the query similarity scores and foreground score, IDGQ produces query-like bounding boxes for the re-ID stage. In the re-ID stage, we predict identity labels of detected bounding boxes, and use these examples to construct a more practical mixed train set for the DRA model. Training on the mixed train set improves the robustness of the re-ID stage to inaccurate detection. We evaluate our method on two benchmark datasets, CUHK-SYSU and PRW. Our framework achieves 93.9% of mAP and 95.1% of rank1 accuracy on CUHK-SYSU, outperforming the previous state of the art methods. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wang_TCTS_A_Task-Consistent_Two-Stage_Framework_for_Person_Search_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_TCTS_A_Task-Consistent_Two-Stage_Framework_for_Person_Search_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_TCTS_A_Task-Consistent_Two-Stage_Framework_for_Person_Search_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Cross-domain Object Detection through Coarse-to-Fine Feature Adaptation | Yangtao Zheng, Di Huang, Songtao Liu, Yunhong Wang | Recent years have witnessed great progress in deep learning based object detection. However, due to the domain shift problem, applying off-the-shelf detectors to an unseen domain leads to significant performance drop. To address such an issue, this paper proposes a novel coarse-to-fine feature adaptation approach to cross-domain object detection. At the coarse-grained stage, different from the rough image-level or instance-level feature alignment used in the literature, foreground regions are extracted by adopting the attention mechanism, and aligned according to their marginal distributions via multi-layer adversarial learning in the common feature space. At the fine-grained stage, we conduct conditional distribution alignment of foregrounds by minimizing the distance of global prototypes with the same category but from different domains. Thanks to this coarse-to-fine feature adaptation, domain knowledge in foreground regions can be effectively transferred. Extensive experiments are carried out in various cross-domain detection scenarios. The results are state-of-the-art, which demonstrate the broad applicability and effectiveness of the proposed approach. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zheng_Cross-domain_Object_Detection_through_Coarse-to-Fine_Feature_Adaptation_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.10275 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zheng_Cross-domain_Object_Detection_through_Coarse-to-Fine_Feature_Adaptation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zheng_Cross-domain_Object_Detection_through_Coarse-to-Fine_Feature_Adaptation_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Efficient Derivative Computation for Cumulative B-Splines on Lie Groups | Christiane Sommer, Vladyslav Usenko, David Schubert, Nikolaus Demmel, Daniel Cremers | Continuous-time trajectory representation has recently gained popularity for tasks where the fusion of high-frame-rate sensors and multiple unsynchronized devices is required. Lie group cumulative B-splines are a popular way of representing continuous trajectories without singularities. They have been used in near real-time SLAM and odometry systems with IMU, LiDAR, regular, RGB-D and event cameras, as well as for offline calibration. These applications require efficient computation of time derivatives (velocity, acceleration), but all prior works rely on a computationally suboptimal formulation. In this work we present an alternative derivation of time derivatives based on recurrence relations that needs O(k) instead of O(k^2) matrix operations (for a spline of order k) and results in simple and elegant expressions. While producing the same result, the proposed approach significantly speeds up the trajectory optimization and allows for computing simple analytic derivatives with respect to spline knots. The results presented in this paper pave the way for incorporating continuous-time trajectory representations into more applications where real-time performance is required. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Sommer_Efficient_Derivative_Computation_for_Cumulative_B-Splines_on_Lie_Groups_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Sommer_Efficient_Derivative_Computation_for_Cumulative_B-Splines_on_Lie_Groups_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Sommer_Efficient_Derivative_Computation_for_Cumulative_B-Splines_on_Lie_Groups_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Sommer_Efficient_Derivative_Computation_CVPR_2020_supplemental.pdf | null | null |
Counterfactual Vision and Language Learning | Ehsan Abbasnejad, Damien Teney, Amin Parvaneh, Javen Shi, Anton van den Hengel | The ongoing success of visual question answering methods has been somwehat surprising given that, at its most general, the problem requires understanding the entire variety of both visual and language stimuli. It is particularly remarkable that this success has been achieved on the basis of comparatively small datasets, given the scale of the problem. One explanation is that this has been accomplished partly by exploiting bias in the datasets rather than developing deeper multi-modal reasoning. This fundamentally limits the generalization of the method, and thus its practical applicability. We propose a method that addresses this problem by introducing counterfactuals in the training. In doing so we leverage structural causal models for counterfactual evaluation to formulate alternatives, for instance, questions that could be asked of the same image set. We show that simulating plausible alternative training data through this process results in better generalization. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Abbasnejad_Counterfactual_Vision_and_Language_Learning_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Abbasnejad_Counterfactual_Vision_and_Language_Learning_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Abbasnejad_Counterfactual_Vision_and_Language_Learning_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Abbasnejad_Counterfactual_Vision_and_CVPR_2020_supplemental.pdf | null | null |
Unsupervised Reinforcement Learning of Transferable Meta-Skills for Embodied Navigation | Juncheng Li, Xin Wang, Siliang Tang, Haizhou Shi, Fei Wu, Yueting Zhuang, William Yang Wang | Visual navigation is a task of training an embodied agent by intelligently navigating to a target object (e.g., television) using only visual observations. A key challenge for current deep reinforcement learning models lies in the requirements for a large amount of training data. It is exceedingly expensive to construct sufficient 3D synthetic environments annotated with the target object information. In this paper, we focus on visual navigation in the low-resource setting, where we have only a few training environments annotated with object information. We propose a novel unsupervised reinforcement learning approach to learn transferable meta-skills (e.g., bypass obstacles, go straight) from unannotated environments without any supervisory signals. The agent can then fast adapt to visual navigation through learning a high-level master policy to combine these meta-skills, when the visual-navigation-specified reward is provided. Experimental results show that our method significantly outperforms the baseline by 53.34% relatively on SPL, and further qualitative analysis demonstrates that our method learns transferable motor primitives for visual navigation. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Li_Unsupervised_Reinforcement_Learning_of_Transferable_Meta-Skills_for_Embodied_Navigation_CVPR_2020_paper.pdf | http://arxiv.org/abs/1911.07450 | https://www.youtube.com/watch?v=gJkMDyNYdIM | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Unsupervised_Reinforcement_Learning_of_Transferable_Meta-Skills_for_Embodied_Navigation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Unsupervised_Reinforcement_Learning_of_Transferable_Meta-Skills_for_Embodied_Navigation_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
M2m: Imbalanced Classification via Major-to-Minor Translation | Jaehyung Kim, Jongheon Jeong, Jinwoo Shin | In most real-world scenarios, labeled training datasets are highly class-imbalanced, where deep neural networks suffer from generalizing to a balanced testing criterion. In this paper, we explore a novel yet simple way to alleviate this issue by augmenting less-frequent classes via translating samples (e.g., images) from more-frequent classes. This simple approach enables a classifier to learn more generalizable features of minority classes, by transferring and leveraging the diversity of the majority information. Our experimental results on a variety of class-imbalanced datasets show that the proposed method improves the generalization on minority classes significantly compared to other existing re-sampling or re-weighting methods. The performance of our method even surpasses those of previous state-of-the-art methods for the imbalanced classification. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Kim_M2m_Imbalanced_Classification_via_Major-to-Minor_Translation_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.00431 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Kim_M2m_Imbalanced_Classification_via_Major-to-Minor_Translation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Kim_M2m_Imbalanced_Classification_via_Major-to-Minor_Translation_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Kim_M2m_Imbalanced_Classification_CVPR_2020_supplemental.pdf | null | null |
DSGN: Deep Stereo Geometry Network for 3D Object Detection | Yilun Chen, Shu Liu, Xiaoyong Shen, Jiaya Jia | Most state-of-the-art 3D object detectors rely heavily on LiDAR sensors and there remains a large gap in terms of performance between image-based and LiDAR-based methods, caused by inappropriate representation for the prediction in 3D scenarios. Our method, called Deep Stereo Geometry Network (DSGN), reduces this gap significantly by detecting 3D objects on a differentiable volumetric representation -- 3D geometric volume, which effectively encodes 3D geometric structure for 3D regular space. With this representation, we learn depth information and semantic cues simultaneously. For the first time, we provide a simple and effective one-stage stereo-based 3D detection pipeline that jointly estimates the depth and detects 3D objects in an end-to-end learning manner. Our approach outperforms previous stereo-based 3D detectors (about 10 higher in terms of AP) and even achieves comparable performance with a few LiDAR-based methods on the KITTI 3D object detection leaderboard. Code will be made publicly available at https://github.com/chenyilun95/DSGN. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Chen_DSGN_Deep_Stereo_Geometry_Network_for_3D_Object_Detection_CVPR_2020_paper.pdf | http://arxiv.org/abs/2001.03398 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Chen_DSGN_Deep_Stereo_Geometry_Network_for_3D_Object_Detection_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Chen_DSGN_Deep_Stereo_Geometry_Network_for_3D_Object_Detection_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Chen_DSGN_Deep_Stereo_CVPR_2020_supplemental.pdf | null | null |
Predicting Semantic Map Representations From Images Using Pyramid Occupancy Networks | Thomas Roddick, Roberto Cipolla | Autonomous vehicles commonly rely on highly detailed birds-eye-view maps of their environment, which capture both static elements of the scene such as road layout as well as dynamic elements such as other cars and pedestrians. Generating these map representations on the fly is a complex multi-stage process which incorporates many important vision-based elements, including ground plane estimation, road segmentation and 3D object detection. In this work we present a simple, unified approach for estimating these map representations directly from monocular images using a single end-to-end deep learning architecture. For the maps themselves we adopt a semantic Bayesian occupancy grid framework, allowing us to trivially accumulate information over multiple cameras and timesteps. We demonstrate the effectiveness of our approach by evaluating against several challenging baselines on the NuScenes and Argoverse datasets, and show that we are able to achieve a relative improvement of 9.1% and 22.3% respectively compared to the best-performing existing method. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Roddick_Predicting_Semantic_Map_Representations_From_Images_Using_Pyramid_Occupancy_Networks_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.13402 | https://www.youtube.com/watch?v=lbfre5ZURts | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Roddick_Predicting_Semantic_Map_Representations_From_Images_Using_Pyramid_Occupancy_Networks_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Roddick_Predicting_Semantic_Map_Representations_From_Images_Using_Pyramid_Occupancy_Networks_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Roddick_Predicting_Semantic_Map_CVPR_2020_supplemental.pdf | null | null |
Memory Aggregation Networks for Efficient Interactive Video Object Segmentation | Jiaxu Miao, Yunchao Wei, Yi Yang | Interactive video object segmentation (iVOS) aims at efficiently harvesting high-quality segmentation masks of the target object in a video with user interactions. Most previous state-of-the-arts tackle the iVOS with two independent networks for conducting user interaction and temporal propagation, respectively, leading to inefficiencies during the inference stage. In this work, we propose a unified framework, named Memory Aggregation Networks (MA-Net), to address the challenging iVOS in a more efficient way. Our MA-Net integrates the interaction and the propagation operations into a single network, which significantly promotes the efficiency of iVOS in the scheme of multi-round interactions. More importantly, we propose a simple yet effective memory aggregation mechanism to record the informative knowledge from the previous interaction rounds, improving the robustness in discovering challenging objects of interest greatly. We conduct extensive experiments on the validation set of DAVIS Challenge 2018 benchmark. In particular, our MA-Net achieves the J@60 score of 76.1% without any bells and whistles, outperforming the state-of-the-arts with more than 2.7%. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Miao_Memory_Aggregation_Networks_for_Efficient_Interactive_Video_Object_Segmentation_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.13246 | https://www.youtube.com/watch?v=y0Nm7QUusKw | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Miao_Memory_Aggregation_Networks_for_Efficient_Interactive_Video_Object_Segmentation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Miao_Memory_Aggregation_Networks_for_Efficient_Interactive_Video_Object_Segmentation_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
SegGCN: Efficient 3D Point Cloud Segmentation With Fuzzy Spherical Kernel | Huan Lei, Naveed Akhtar, Ajmal Mian | Fuzzy clustering is known to perform well in real-world applications. Inspired by this observation, we incorporate a fuzzy mechanism into discrete convolutional kernels for 3D point clouds as our first major contribution. The proposed fuzzy kernel is defined over a spherical volume that uses discrete bins. Discrete volumetric division can normally make a kernel vulnerable to boundary effects during learning as well as point density during inference. However, the proposed kernel remains robust to boundary conditions and point density due to the fuzzy mechanism. Our second major contribution comes as the proposal of an efficient graph convolutional network, SegGCN for segmenting point clouds. The proposed network exploits ResNet like blocks in the encoder and 1 x 1 convolutions in the decoder. SegGCN capitalizes on the separable convolution operation of the proposed fuzzy kernel for efficiency. We establish the effectiveness of the SegGCN with the proposed kernel on the challenging S3DIS and ScanNet real-world datasets. Our experiments demonstrate that the proposed network can segment over one million points per second with highly competitive performance. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Lei_SegGCN_Efficient_3D_Point_Cloud_Segmentation_With_Fuzzy_Spherical_Kernel_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Lei_SegGCN_Efficient_3D_Point_Cloud_Segmentation_With_Fuzzy_Spherical_Kernel_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Lei_SegGCN_Efficient_3D_Point_Cloud_Segmentation_With_Fuzzy_Spherical_Kernel_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Lei_SegGCN_Efficient_3D_CVPR_2020_supplemental.pdf | null | null |
AutoTrack: Towards High-Performance Visual Tracking for UAV With Automatic Spatio-Temporal Regularization | Yiming Li, Changhong Fu, Fangqiang Ding, Ziyuan Huang, Geng Lu | Most existing trackers based on discriminative correlation filters (DCF) try to introduce predefined regularization term to improve the learning of target objects, e.g., by suppressing background learning or by restricting change rate of correlation filters. However, predefined parameters introduce much effort in tuning them and they still fail to adapt to new situations that the designer did not think of. In this work, a novel approach is proposed to online automatically and adaptively learn spatio-temporal regularization term. Spatially local response map variation is introduced as spatial regularization to make DCF focus on the learning of trust-worthy parts of the object, and global response map variation determines the updating rate of the filter. Extensive experiments on four UAV benchmarks have proven the superiority of our method compared to the state-of-the-art CPU- and GPU-based trackers, with a speed of 60 frames per second running on a single CPU. Our tracker is additionally proposed to be applied in UAV localization. Considerable tests in the indoor practical scenarios have proven the effectiveness and versatility of our localization method. The code is available at https://github.com/vision4robotics/AutoTrack. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Li_AutoTrack_Towards_High-Performance_Visual_Tracking_for_UAV_With_Automatic_Spatio-Temporal_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.12949 | https://www.youtube.com/watch?v=2w8Mophui1k | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_AutoTrack_Towards_High-Performance_Visual_Tracking_for_UAV_With_Automatic_Spatio-Temporal_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_AutoTrack_Towards_High-Performance_Visual_Tracking_for_UAV_With_Automatic_Spatio-Temporal_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Li_AutoTrack_Towards_High-Performance_CVPR_2020_supplemental.pdf | null | null |
Multi-Mutual Consistency Induced Transfer Subspace Learning for Human Motion Segmentation | Tao Zhou, Huazhu Fu, Chen Gong, Jianbing Shen, Ling Shao, Fatih Porikli | Human motion segmentation based on transfer subspace learning is a rising interest in action-related tasks. Although progress has been made, there are still several issues within the existing methods. First, existing methods transfer knowledge from source data to target tasks by learning domain-invariant features, but they ignore to preserve domain-specific knowledge. Second, the transfer subspace learning is employed in either low-level or high-level feature spaces, but few methods consider fusing multi-level features for subspace learning. To this end, we propose a novel multi-mutual consistency induced transfer subspace learning framework for human motion segmentation. Specifically, our model factorizes the source and target data into distinct multi-layer feature spaces and reduces the distribution gap between them through a multi-mutual consistency learning strategy. In this way, the domain-specific knowledge and domain-invariant properties can be explored simultaneously. Our model also conducts the transfer subspace learning on different layers to capture multi-level structural information. Further, to preserve the temporal correlations, we project the learned representations into a block-like space. The proposed model is efficiently optimized by using the Augmented Lagrange Multiplier (ALM) algorithm. Experimental results on four human motion datasets demonstrate the effectiveness of our method over other state-of-the-art approaches. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zhou_Multi-Mutual_Consistency_Induced_Transfer_Subspace_Learning_for_Human_Motion_Segmentation_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=fszvR39x-SQ | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhou_Multi-Mutual_Consistency_Induced_Transfer_Subspace_Learning_for_Human_Motion_Segmentation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhou_Multi-Mutual_Consistency_Induced_Transfer_Subspace_Learning_for_Human_Motion_Segmentation_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Associate-3Ddet: Perceptual-to-Conceptual Association for 3D Point Cloud Object Detection | Liang Du, Xiaoqing Ye, Xiao Tan, Jianfeng Feng, Zhenbo Xu, Errui Ding, Shilei Wen | Object detection from 3D point clouds remains a challenging task, though recent studies pushed the envelope with the deep learning techniques. Owing to the severe spatial occlusion and inherent variance of point density with the distance to sensors, appearance of a same object varies a lot in point cloud data. Designing robust feature representation against such appearance changes is hence the key issue in a 3D object detection method. In this paper, we innovatively propose a domain adaptation like approach to enhance the robustness of the feature representation. More specifically, we bridge the gap between the perceptual domain where the feature comes from a real scene and the conceptual domain where the feature is extracted from an augmented scene consisting of non-occlusion point cloud rich of detailed information. This domain adaptation approach mimics the functionality of the human brain when proceeding object perception. Extensive experiments demonstrate that our simple yet effective approach fundamentally boosts the performance of 3D point cloud object detection and achieves the state-of-the-art results. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Du_Associate-3Ddet_Perceptual-to-Conceptual_Association_for_3D_Point_Cloud_Object_Detection_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=sH8AIwBVGlA | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Du_Associate-3Ddet_Perceptual-to-Conceptual_Association_for_3D_Point_Cloud_Object_Detection_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Du_Associate-3Ddet_Perceptual-to-Conceptual_Association_for_3D_Point_Cloud_Object_Detection_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Training a Steerable CNN for Guidewire Detection | Donghang Li, Adrian Barbu | Guidewires are thin wires used in coronary angioplasty to guide different tools to access and repair the obstructed artery. The whole procedure is monitored using fluoroscopic (real-time X-ray) images. Due to the guidewire being thin in the low quality fluoroscopic images, it is usually poorly visible. The poor quality of the X-ray images makes the guidewire detection a challenging problem in image-guided interventions. Localizing the guidewire could help in enhancing its visibility and for other automatic procedures. Guidewire localization methods usually contain a first step of computing a pixelwise guidewire response map on the entire image. In this paper, we present a steerable Convolutional Neural Network (CNN), which is a Fully Convolutional Neural Network (FCNN) that can detect objects rotated by an arbitrary 2D angle, without being rotation invariant. In fact, the steerable CNN has an angle parameter that can be changed to make it sensitive to objects rotated by that angle. We present an application of this idea to detecting the guidewire pixels, and compare it with an FCNN trained to be invariant to the guidewire orientation. Results reveal that the proposed method is a good choice, outperforming some popular filter-based and learning-based approaches such as Frangi Filter, Spherical Quadrature Filter, FCNN and a state of the art trained classifier based on hand-crafted feature. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Li_Training_a_Steerable_CNN_for_Guidewire_Detection_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Training_a_Steerable_CNN_for_Guidewire_Detection_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Training_a_Steerable_CNN_for_Guidewire_Detection_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
GIFnets: Differentiable GIF Encoding Framework | Innfarn Yoo, Xiyang Luo, Yilin Wang, Feng Yang, Peyman Milanfar | Graphics Interchange Format (GIF) is a widely used image file format. Due to the limited number of palette colors, GIF encoding often introduces color banding artifacts. Traditionally, dithering is applied to reduce color banding, but introducing dotted-pattern artifacts. To reduce artifacts and provide a better and more efficient GIF encoding, we introduce a differentiable GIF encoding pipeline, which includes three novel neural networks: PaletteNet, DitherNet, and BandingNet. Each of these three networks provides an important functionality within the GIF encoding pipeline. PaletteNet predicts a near-optimal color palette given an input image. DitherNet manipulates the input image to reduce color banding artifacts and provides an alternative to traditional dithering. Finally, BandingNet is designed to detect color banding, and provides a new perceptual loss specifically for GIF images. As far as we know, this is the first fully differentiable GIF encoding pipeline based on deep neural networks and compatible with existing GIF decoders. User study shows that our algorithm is better than Floyd-Steinberg based GIF encoding. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Yoo_GIFnets_Differentiable_GIF_Encoding_Framework_CVPR_2020_paper.pdf | http://arxiv.org/abs/2006.13434 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Yoo_GIFnets_Differentiable_GIF_Encoding_Framework_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Yoo_GIFnets_Differentiable_GIF_Encoding_Framework_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Yoo_GIFnets_Differentiable_GIF_CVPR_2020_supplemental.pdf | null | null |
TRPLP - Trifocal Relative Pose From Lines at Points | Ricardo Fabbri, Timothy Duff, Hongyi Fan, Margaret H. Regan, David da Costa de Pinho, Elias Tsigaridas, Charles W. Wampler, Jonathan D. Hauenstein, Peter J. Giblin, Benjamin Kimia, Anton Leykin, Tomas Pajdla | We present a method for solving two minimal problems for relative camera pose estimation from three views, which are based on three view correspondences of (i) three points and one line and (ii) three points and two lines through two of the points. These problems are too difficult to be efficiently solved by the state of the art Grobner basis methods. Our method is based on a new efficient homotopy continuation (HC) solver, which dramatically speeds up previous HC solving by specializing HC methods to generic cases of our problems. We show in simulated experiments that our solvers are numerically robust and stable under image noise. We show in real experiment that (i) SIFT features provide good enough point-and-line correspondences for three-view reconstruction and (ii) that we can solve difficult cases with too few or too noisy tentative matches where the state of the art structure from motion initialization fails. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Fabbri_TRPLP_-_Trifocal_Relative_Pose_From_Lines_at_Points_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Fabbri_TRPLP_-_Trifocal_Relative_Pose_From_Lines_at_Points_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Fabbri_TRPLP_-_Trifocal_Relative_Pose_From_Lines_at_Points_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Fabbri_TRPLP_-_Trifocal_CVPR_2020_supplemental.pdf | null | null |
SP-NAS: Serial-to-Parallel Backbone Search for Object Detection | Chenhan Jiang, Hang Xu, Wei Zhang, Xiaodan Liang, Zhenguo Li | Advanced object detectors usually adopt a backbone network designed and pretrained by ImageNet classification. Recently neural architecture search (NAS) has emerged to automatically design a task-specific backbone to bridge the gap between the tasks of classification and detection. In this paper, we propose a two-phase serial-to-parallel architecture search framework named SP-NAS towards a flexible task-oriented detection backbone. Specifically, the serial-searching round aims at finding a sequence of serial blocks with optimal scale and output channels in the feature hierarchy by a Swap-Expand-Reignite search algorithm; the parallel-searching phase then assembles several sub-architectures along with the previous searched backbone into a more powerful parallel-structured backbone. We efficiently search a detection backbone by exploring a network morphism strategy on multiple detection benchmarks. The resulting architectures achieve SOTA results, i.e. top performance (LAMR: 0.055) on the automotive detection leaderboard of EuroCityPersons benchmark, improving 2.3% mAP with less FLOPS than NAS-FPN on COCO, and reaching 84.1% AP50 on VOC better than DetNAS and Auto-FPN in terms of both accuracy and speed. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Jiang_SP-NAS_Serial-to-Parallel_Backbone_Search_for_Object_Detection_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Jiang_SP-NAS_Serial-to-Parallel_Backbone_Search_for_Object_Detection_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Jiang_SP-NAS_Serial-to-Parallel_Backbone_Search_for_Object_Detection_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Jiang_SP-NAS_Serial-to-Parallel_Backbone_CVPR_2020_supplemental.pdf | null | null |
Rethinking Depthwise Separable Convolutions: How Intra-Kernel Correlations Lead to Improved MobileNets | Daniel Haase, Manuel Amthor | We introduce blueprint separable convolutions (BSConv) as highly efficient building blocks for CNNs. They are motivated by quantitative analyses of kernel properties from trained models, which show the dominance of correlations along the depth axis. Based on our findings, we formulate a theoretical foundation from which we derive efficient implementations using only standard layers. Moreover, our approach provides a thorough theoretical derivation, interpretation, and justification for the application of depthwise separable convolutions (DSCs) in general, which have become the basis of many modern network architectures. Ultimately, we reveal that DSC-based architectures such as MobileNets implicitly rely on cross-kernel correlations, while our BSConv formulation is based on intra-kernel correlations and thus allows for a more efficient separation of regular convolutions. Extensive experiments on large-scale and fine-grained classification datasets show that BSConvs clearly and consistently improve MobileNets and other DSC-based architectures without introducing any further complexity. For fine-grained datasets, we achieve an improvement of up to 13.7 percentage points. In addition, if used as drop-in replacement for standard architectures such as ResNets, BSConv variants also outperform their vanilla counterparts by up to 9.5 percentage points on ImageNet. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Haase_Rethinking_Depthwise_Separable_Convolutions_How_Intra-Kernel_Correlations_Lead_to_Improved_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.13549 | https://www.youtube.com/watch?v=nC6C-74xmbY | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Haase_Rethinking_Depthwise_Separable_Convolutions_How_Intra-Kernel_Correlations_Lead_to_Improved_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Haase_Rethinking_Depthwise_Separable_Convolutions_How_Intra-Kernel_Correlations_Lead_to_Improved_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Vision-Dialog Navigation by Exploring Cross-Modal Memory | Yi Zhu, Fengda Zhu, Zhaohuan Zhan, Bingqian Lin, Jianbin Jiao, Xiaojun Chang, Xiaodan Liang | Vision-dialog navigation posed as a new holy-grail task in vision-language disciplinary targets at learning an agent endowed with the capability of constant conversation for help with natural language and navigating according to human responses. Besides the common challenges faced in visual language navigation, vision-dialog navigation also requires to handle well with the language intentions of a series of questions about the temporal context from dialogue history and co-reasoning both dialogs and visual scenes. In this paper, we propose the Cross-modal Memory Network (CMN) for remembering and understanding the rich information relevant to historical navigation actions. Our CMN consists of two memory modules, the language memory module (L-mem) and the visual memory module (V-mem). Specifically, L-mem learns latent relationships between the current language interaction and a dialog history by employing a multi-head attention mechanism. V-mem learns to associate the current visual views and the cross-modal memory about the previous navigation actions. The cross-modal memory is generated via a vision-to-language attention and a language-to-vision attention. Benefiting from the collaborative learning of the L-mem and the V-mem, our CMN is able to explore the memory about the decision making of historical navigation actions which is for the current step. Experiments on the CVDN dataset show that our CMN outperforms the previous state-of-the-art model by a significant margin on both seen and unseen environments. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zhu_Vision-Dialog_Navigation_by_Exploring_Cross-Modal_Memory_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.06745 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhu_Vision-Dialog_Navigation_by_Exploring_Cross-Modal_Memory_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhu_Vision-Dialog_Navigation_by_Exploring_Cross-Modal_Memory_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
PointRend: Image Segmentation As Rendering | Alexander Kirillov, Yuxin Wu, Kaiming He, Ross Girshick | We present a new method for efficient high-quality image segmentation of objects and scenes. By analogizing classical computer graphics methods for efficient rendering with over- and undersampling challenges faced in pixel labeling tasks, we develop a unique perspective of image segmentation as a rendering problem. From this vantage, we present the PointRend (Point-based Rendering) neural network module: a module that performs point-based segmentation predictions at adaptively selected locations based on an iterative subdivision algorithm. PointRend can be flexibly applied to both instance and semantic segmentation tasks by building on top of existing state-of-the-art models. While many concrete implementations of the general idea are possible, we show that a simple design already achieves excellent results. Qualitatively, PointRend outputs crisp object boundaries in regions that are over-smoothed by previous methods. Quantitatively, PointRend yields significant gains on COCO and Cityscapes, for both instance and semantic segmentation. PointRend's efficiency enables output resolutions that are otherwise impractical in terms of memory or computation compared to existing approaches. Code has been made available at https://github.com/facebookresearch/detectron2/tree/master/projects/PointRend. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Kirillov_PointRend_Image_Segmentation_As_Rendering_CVPR_2020_paper.pdf | http://arxiv.org/abs/1912.08193 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Kirillov_PointRend_Image_Segmentation_As_Rendering_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Kirillov_PointRend_Image_Segmentation_As_Rendering_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Kirillov_PointRend_Image_Segmentation_CVPR_2020_supplemental.pdf | null | null |
Differentiable Adaptive Computation Time for Visual Reasoning | Cristobal Eyzaguirre, Alvaro Soto | This paper presents a novel attention-based algorithm for achieving adaptive computation called DACT, which, unlike existing ones, is end-to-end differentiable. Our method can be used in conjunction with many networks; in particular, we study its application to the widely know MAC architecture, obtaining a significant reduction in the number of recurrent steps needed to achieve similar accuracies, therefore improving its performance to computation ratio. Furthermore, we show that by increasing the maximum number of steps used, we surpass the accuracy of even our best non-adaptive MAC in the CLEVR dataset, demonstrating that our approach is able to control the number of steps without significant loss of performance. Additional advantages provided by our approach include considerably improving interpretability by discarding useless steps and providing more insights into the underlying reasoning process. Finally, we present adaptive computation as an equivalent to an ensemble of models, similar to a mixture of expert formulation. Both the code and the configuration files for our experiments are made available to support further research in this area. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Eyzaguirre_Differentiable_Adaptive_Computation_Time_for_Visual_Reasoning_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.12770 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Eyzaguirre_Differentiable_Adaptive_Computation_Time_for_Visual_Reasoning_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Eyzaguirre_Differentiable_Adaptive_Computation_Time_for_Visual_Reasoning_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Eyzaguirre_Differentiable_Adaptive_Computation_CVPR_2020_supplemental.pdf | null | null |
Exploring Data Aggregation in Policy Learning for Vision-Based Urban Autonomous Driving | Aditya Prakash, Aseem Behl, Eshed Ohn-Bar, Kashyap Chitta, Andreas Geiger | Data aggregation techniques can significantly improve vision-based policy learning within a training environment, e.g., learning to drive in a specific simulation condition. However, as on-policy data is sequentially sampled and added in an iterative manner, the policy can specialize and overfit to the training conditions. For real-world applications, it is useful for the learned policy to generalize to novel scenarios that differ from the training conditions. To improve policy learning while maintaining robustness when training end-to-end driving policies, we perform an extensive analysis of data aggregation techniques in the CARLA environment. We demonstrate how the majority of them have poor generalization performance, and develop a novel approach with empirically better generalization performance compared to existing techniques. Our two key ideas are (1) to sample critical states from the collected on-policy data based on the utility they provide to the learned policy in terms of driving behavior, and (2) to incorporate a replay buffer which progressively focuses on the high uncertainty regions of the policy's state distribution. We evaluate the proposed approach on the CARLA NoCrash benchmark, focusing on the most challenging driving scenarios with dense pedestrian and vehicle traffic. Our approach improves driving success rate by 16% over state-of-the-art, achieving 87% of the expert performance while also reducing the collision rate by an order of magnitude without the use of any additional modality, auxiliary tasks, architectural modifications or reward from the environment. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Prakash_Exploring_Data_Aggregation_in_Policy_Learning_for_Vision-Based_Urban_Autonomous_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=X_aqJKuhefI | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Prakash_Exploring_Data_Aggregation_in_Policy_Learning_for_Vision-Based_Urban_Autonomous_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Prakash_Exploring_Data_Aggregation_in_Policy_Learning_for_Vision-Based_Urban_Autonomous_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Geometrically Principled Connections in Graph Neural Networks | Shunwang Gong, Mehdi Bahri, Michael M. Bronstein, Stefanos Zafeiriou | Graph convolution operators bring the advantages of deep learning to a variety of graph and mesh processing tasks previously deemed out of reach. With their continued success comes the desire to design more powerful architectures, often by adapting existing deep learning techniques to non-Euclidean data. In this paper, we argue geometry should remain the primary driving force behind innovation in the emerging field of geometric deep learning. We relate graph neural networks to widely successful computer graphics and data approximation models: radial basis functions (RBFs). We conjecture that, like RBFs, graph convolution layers would benefit from the addition of simple functions to the powerful convolution kernels. We introduce affine skip connections, a novel building block formed by combining a fully connected layer with any graph convolution operator. We experimentally demonstrate the effectiveness of our technique, and show the improved performance is the consequence of more than the increased number of parameters. Operators equipped with the affine skip connection markedly outperform their base performance on every task we evaluated, i.e., shape reconstruction, dense shape correspondence, and graph classification. We hope our simple and effective approach will serve as a solid baseline and help ease future research in graph neural networks. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Gong_Geometrically_Principled_Connections_in_Graph_Neural_Networks_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.02658 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Gong_Geometrically_Principled_Connections_in_Graph_Neural_Networks_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Gong_Geometrically_Principled_Connections_in_Graph_Neural_Networks_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Making Better Mistakes: Leveraging Class Hierarchies With Deep Networks | Luca Bertinetto, Romain Mueller, Konstantinos Tertikas, Sina Samangooei, Nicholas A. Lord | Deep neural networks have improved image classification dramatically over the past decade, but have done so by focusing on performance measures that treat all classes other than the ground truth as equally wrong. This has led to a situation in which mistakes are less likely to be made than before, but are equally likely to be absurd or catastrophic when they do occur. Past works have recognised and tried to address this issue of mistake severity, often by using graph distances in class hierarchies, but this has largely been neglected since the advent of the current deep learning era in computer vision. In this paper, we aim to renew interest in this problem by reviewing past approaches and proposing two simple methods which outperform the prior art under several metrics on two large datasets with complex class hierarchies: tieredImageNet and iNaturalist'19. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Bertinetto_Making_Better_Mistakes_Leveraging_Class_Hierarchies_With_Deep_Networks_CVPR_2020_paper.pdf | http://arxiv.org/abs/1912.09393 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Bertinetto_Making_Better_Mistakes_Leveraging_Class_Hierarchies_With_Deep_Networks_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Bertinetto_Making_Better_Mistakes_Leveraging_Class_Hierarchies_With_Deep_Networks_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Bertinetto_Making_Better_Mistakes_CVPR_2020_supplemental.pdf | null | null |
Telling Left From Right: Learning Spatial Correspondence of Sight and Sound | Karren Yang, Bryan Russell, Justin Salamon | Self-supervised audio-visual learning aims to capture useful representations of video by leveraging correspondences between visual and audio inputs. Existing approaches have focused primarily on matching semantic information between the sensory streams. We propose a novel self-supervised task to leverage an orthogonal principle: matching spatial information in the audio stream to the positions of sound sources in the visual stream. Our approach is simple yet effective. We train a model to determine whether the left and right audio channels have been flipped, forcing it to reason about spatial localization across the visual and audio streams. To train and evaluate our method, we introduce a large-scale video dataset, YouTube-ASMR-300K, with spatial audio comprising over 900 hours of footage. We demonstrate that understanding spatial correspondence enables models to perform better on three audio-visual tasks, achieving quantitative gains over supervised and self-supervised baselines that do not leverage spatial audio cues. We also show how to extend our self-supervised approach to 360 degree videos with ambisonic audio. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Yang_Telling_Left_From_Right_Learning_Spatial_Correspondence_of_Sight_and_CVPR_2020_paper.pdf | http://arxiv.org/abs/2006.06175 | https://www.youtube.com/watch?v=lDk6PlGMwy8 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Yang_Telling_Left_From_Right_Learning_Spatial_Correspondence_of_Sight_and_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Yang_Telling_Left_From_Right_Learning_Spatial_Correspondence_of_Sight_and_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Yang_Telling_Left_From_CVPR_2020_supplemental.zip | null | null |
Deep Adversarial Decomposition: A Unified Framework for Separating Superimposed Images | Zhengxia Zou, Sen Lei, Tianyang Shi, Zhenwei Shi, Jieping Ye | Separating individual image layers from a single mixed image has long been an important but challenging task. We propose a unified framework named "deep adversarial decomposition" for single superimposed image separation. Our method deals with both linear and non-linear mixtures under an adversarial training paradigm. Considering the layer separating ambiguity that given a single mixed input, there could be an infinite number of possible solutions, we introduce a "Separation-Critic" - a discriminative network which is trained to identify whether the output layers are well-separated and thus further improves the layer separation. We also introduce a "crossroad L1" loss function, which computes the distance between the unordered outputs and their references in a crossover manner so that the training can be well-instructed with pixel-wise supervision. Experimental results suggest that our method significantly outperforms other popular image separation frameworks. Without specific tuning, our method achieves the state of the art results on multiple computer vision tasks, including the image deraining, photo reflection removal, and image shadow removal. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zou_Deep_Adversarial_Decomposition_A_Unified_Framework_for_Separating_Superimposed_Images_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=Q8BG8mRiUNg | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zou_Deep_Adversarial_Decomposition_A_Unified_Framework_for_Separating_Superimposed_Images_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zou_Deep_Adversarial_Decomposition_A_Unified_Framework_for_Separating_Superimposed_Images_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Zou_Deep_Adversarial_Decomposition_CVPR_2020_supplemental.pdf | null | null |
Towards Accurate Scene Text Recognition With Semantic Reasoning Networks | Deli Yu, Xuan Li, Chengquan Zhang, Tao Liu, Junyu Han, Jingtuo Liu, Errui Ding | Scene text image contains two levels of contents: visual texture and semantic information. Although the previous scene text recognition methods have made great progress over the past few years, the research on mining semantic information to assist text recognition attracts less attention, only RNN-like structures are explored to implicitly model semantic information. However, we observe that RNN based methods have some obvious shortcomings, such as time-dependent decoding manner and one-way serial transmission of semantic context, which greatly limit the help of semantic information and the computation efficiency. To mitigate these limitations, we propose a novel end-to-end trainable framework named semantic reasoning network (SRN) for accurate scene text recognition, where a global semantic reasoning module (GSRM) is introduced to capture global semantic context through multi-way parallel transmission. The state-of-the-art results on 7 public benchmarks, including regular text, irregular text and non-Latin long text, verify the effectiveness and robustness of the proposed method. In addition, the speed of SRN has significant advantages over the RNN based methods, demonstrating its value in practical use. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Yu_Towards_Accurate_Scene_Text_Recognition_With_Semantic_Reasoning_Networks_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.12294 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Yu_Towards_Accurate_Scene_Text_Recognition_With_Semantic_Reasoning_Networks_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Yu_Towards_Accurate_Scene_Text_Recognition_With_Semantic_Reasoning_Networks_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Deep Relational Reasoning Graph Network for Arbitrary Shape Text Detection | Shi-Xue Zhang, Xiaobin Zhu, Jie-Bo Hou, Chang Liu, Chun Yang, Hongfa Wang, Xu-Cheng Yin | Arbitrary shape text detection is a challenging task due to the high variety and complexity of scenes texts. In this paper, we propose a novel unified relational reasoning graph network for arbitrary shape text detection. In our method, an innovative local graph bridges a text proposal model via Convolutional Neural Network (CNN) and a deep relational reasoning network via Graph Convolutional Network (GCN), making our network end-to-end trainable. To be concrete, every text instance will be divided into a series of small rectangular components, and the geometry attributes (e.g., height, width, and orientation) of the small components will be estimated by our text proposal model. Given the geometry attributes, the local graph construction model can roughly establish linkages between different text components. For further reasoning and deducing the likelihood of linkages between the component and its neighbors, we adopt a graph-based network to perform deep relational reasoning on local graphs. Experiments on public available datasets demonstrate the state-of-the-art performance of our method. Code is available at https://github.com/GXYM/DRRG. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zhang_Deep_Relational_Reasoning_Graph_Network_for_Arbitrary_Shape_Text_Detection_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.07493 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_Deep_Relational_Reasoning_Graph_Network_for_Arbitrary_Shape_Text_Detection_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_Deep_Relational_Reasoning_Graph_Network_for_Arbitrary_Shape_Text_Detection_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
GP-NAS: Gaussian Process Based Neural Architecture Search | Zhihang Li, Teng Xi, Jiankang Deng, Gang Zhang, Shengzhao Wen, Ran He | Neural architecture search (NAS) advances beyond the state-of-the-art in various computer vision tasks by automating the designs of deep neural networks. In this paper, we aim to address three important questions in NAS: (1) How to measure the correlation between architectures and their performances? (2) How to evaluate the correlation between different architectures? (3) How to learn these correlations with a small number of samples? To this end, we first model these correlations from a Bayesian perspective. Specifically, by introducing a novel Gaussian Process based NAS (GP-NAS) method, the correlations are modeled by the kernel function and mean function. The kernel function is also learnable to enable adaptive modeling for complex correlations in different search spaces. Furthermore, by incorporating a mutual information based sampling method, we can theoretically ensure the high-performance architecture with only a small set of samples. After addressing these problems, training GP-NAS once enables direct performance prediction of any architecture in different scenarios and may obtain efficient networks for different deployment platforms. Extensive experiments on both image classification and face recognition tasks verify the effectiveness of our algorithm. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Li_GP-NAS_Gaussian_Process_Based_Neural_Architecture_Search_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_GP-NAS_Gaussian_Process_Based_Neural_Architecture_Search_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_GP-NAS_Gaussian_Process_Based_Neural_Architecture_Search_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Basis Prediction Networks for Effective Burst Denoising With Large Kernels | Zhihao Xia, Federico Perazzi, Michael Gharbi, Kalyan Sunkavalli, Ayan Chakrabarti | Bursts of images exhibit significant self-similarity across both time and space. This motivates a representation of the kernels as linear combinations of a small set of basis elements. To this end, we introduce a novel basis prediction network that, given an input burst, predicts a set of global basis kernels --- shared within the image --- and the corresponding mixing coefficients --- which are specific to individual pixels. Compared to state-of-the-art techniques that output a large tensor of per-pixel spatiotemporal kernels, our formulation substantially reduces the dimensionality of the network output. This allows us to effectively exploit comparatively larger denoising kernels, achieving both significant quality improvements (over 1dB PSNR) and faster run-times over state-of-the-art methods. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Xia_Basis_Prediction_Networks_for_Effective_Burst_Denoising_With_Large_Kernels_CVPR_2020_paper.pdf | http://arxiv.org/abs/1912.04421 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Xia_Basis_Prediction_Networks_for_Effective_Burst_Denoising_With_Large_Kernels_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Xia_Basis_Prediction_Networks_for_Effective_Burst_Denoising_With_Large_Kernels_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Xia_Basis_Prediction_Networks_CVPR_2020_supplemental.pdf | null | null |
Real-World Person Re-Identification via Degradation Invariance Learning | Yukun Huang, Zheng-Jun Zha, Xueyang Fu, Richang Hong, Liang Li | Person re-identification (Re-ID) in real-world scenarios usually suffers from various degradation factors, e.g., low-resolution, weak illumination, blurring and adverse weather. On the one hand, these degradations lead to severe discriminative information loss, which significantly obstructs identity representation learning; on the other hand, the feature mismatch problem caused by low-level visual variations greatly reduces retrieval performance. An intuitive solution to this problem is to utilize low-level image restoration methods to improve the image quality. However, existing restoration methods cannot directly serve to real-world Re-ID due to various limitations, e.g., the requirements of reference samples, domain gap between synthesis and reality, and incompatibility between low-level and high-level methods. In this paper, to solve the above problem, we propose a degradation invariance learning framework for real-world person Re-ID. By introducing a self-supervised disentangled representation learning strategy, our method is able to simultaneously extract identity-related robust features and remove real-world degradations without extra supervision. We use low-resolution images as the main demonstration, and experiments show that our approach is able to achieve state-of-the-art performance on several Re-ID benchmarks. In addition, our framework can be easily extended to other real-world degradation factors, such as weak illumination, with only a few modifications. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Huang_Real-World_Person_Re-Identification_via_Degradation_Invariance_Learning_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.04933 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Huang_Real-World_Person_Re-Identification_via_Degradation_Invariance_Learning_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Huang_Real-World_Person_Re-Identification_via_Degradation_Invariance_Learning_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Huang_Real-World_Person_Re-Identification_CVPR_2020_supplemental.pdf | null | null |
Momentum Contrast for Unsupervised Visual Representation Learning | Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, Ross Girshick | We present Momentum Contrast (MoCo) for unsupervised visual representation learning. From a perspective on contrastive learning as dictionary look-up, we build a dynamic dictionary with a queue and a moving-averaged encoder. This enables building a large and consistent dictionary on-the-fly that facilitates contrastive unsupervised learning. MoCo provides competitive results under the common linear protocol on ImageNet classification. More importantly, the representations learned by MoCo transfer well to downstream tasks. MoCo can outperform its supervised pre-training counterpart in 7 detection/segmentation tasks on PASCAL VOC, COCO, and other datasets, sometimes surpassing it by large margins. This suggests that the gap between unsupervised and supervised representation learning has been largely closed in many vision tasks. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/He_Momentum_Contrast_for_Unsupervised_Visual_Representation_Learning_CVPR_2020_paper.pdf | http://arxiv.org/abs/1911.05722 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/He_Momentum_Contrast_for_Unsupervised_Visual_Representation_Learning_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/He_Momentum_Contrast_for_Unsupervised_Visual_Representation_Learning_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/He_Momentum_Contrast_for_CVPR_2020_supplemental.pdf | null | null |
Meta-Learning of Neural Architectures for Few-Shot Learning | Thomas Elsken, Benedikt Staffler, Jan Hendrik Metzen, Frank Hutter | The recent progress in neural architecture search (NAS) has allowed scaling the automated design of neural architectures to real-world domains, such as object detection and semantic segmentation. However, one prerequisite for the application of NAS are large amounts of labeled data and compute resources. This renders its application challenging in few-shot learning scenarios, where many related tasks need to be learned, each with limited amounts of data and compute time. Thus, few-shot learning is typically done with a fixed neural architecture. To improve upon this, we propose MetaNAS, the first method which fully integrates NAS with gradient-based meta-learning. MetaNAS optimizes a meta-architecture along with the meta-weights during meta-training. During meta-testing, architectures can be adapted to a novel task with a few steps of the task optimizer, that is: task adaptation becomes computationally cheap and requires only little data per task. Moreover, MetaNAS is agnostic in that it can be used with arbitrary model-agnostic meta-learning algorithms and arbitrary gradient-based NAS methods. Empirical results on standard few-shot classification benchmarks show that MetaNAS with a combination of DARTS and REPTILE yields state-of-the-art results. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Elsken_Meta-Learning_of_Neural_Architectures_for_Few-Shot_Learning_CVPR_2020_paper.pdf | http://arxiv.org/abs/1911.11090 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Elsken_Meta-Learning_of_Neural_Architectures_for_Few-Shot_Learning_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Elsken_Meta-Learning_of_Neural_Architectures_for_Few-Shot_Learning_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Elsken_Meta-Learning_of_Neural_CVPR_2020_supplemental.pdf | null | null |
Deep Generative Model for Robust Imbalance Classification | Xinyue Wang, Yilin Lyu, Liping Jing | Discovering hidden pattern from imbalanced data is a critical issue in various real-world applications including computer vision. The existing classification methods usually suffer from the limitation of data especially the minority classes, and result in unstable prediction and low performance. In this paper, a deep generative classifier is proposed to mitigate this issue via both data perturbation and model perturbation. Specially, the proposed generative classifier is modeled by a deep latent variable model where the latent variable aims to capture the direct cause of target label. Meanwhile, the latent variable is represented by a probability distribution over possible values rather than a single fixed value, which is able to enforce uncertainty of model and lead to stable prediction. Furthermore, this latent variable, as a confounder, affects the process of data (feature/label) generation, so that we can arrive at well-justified sampling variability considerations in statistics, and implement data perturbation. Extensive experiments have been conducted on widely-used real imbalanced image datasets. By comparing with the state-of-the-art methods, experimental results demonstrate the superiority of our proposed model on imbalance classification task. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wang_Deep_Generative_Model_for_Robust_Imbalance_Classification_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Deep_Generative_Model_for_Robust_Imbalance_Classification_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Deep_Generative_Model_for_Robust_Imbalance_Classification_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Wang_Deep_Generative_Model_CVPR_2020_supplemental.pdf | null | null |
Unsupervised Multi-Modal Image Registration via Geometry Preserving Image-to-Image Translation | Moab Arar, Yiftach Ginger, Dov Danon, Amit H. Bermano, Daniel Cohen-Or | Many applications, such as autonomous driving, heavily rely on multi-modal data where spatial alignment between the modalities is required. Most multi-modal registration methods struggle computing the spatial correspondence between the images using prevalent cross-modality similarity measures. In this work, we bypass the difficulties of developing cross-modality similarity measures, by training an image-to-image translation network on the two input modalities. This learned translation allows training the registration network using simple and reliable mono-modality metrics. We perform multi-modal registration using two networks - a spatial transformation network and a translation network. We show that by encouraging our translation network to be geometry preserving, we manage to train an accurate spatial transformation network. Compared to state-of-the-art multi-modal methods our presented method is unsupervised, requiring no pairs of aligned modalities for training, and can be adapted to any pair of modalities. We evaluate our method quantitatively and qualitatively on commercial datasets, showing that it performs well on several modalities and achieves accurate alignment. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Arar_Unsupervised_Multi-Modal_Image_Registration_via_Geometry_Preserving_Image-to-Image_Translation_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.08073 | https://www.youtube.com/watch?v=uRbL7j3j7Os | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Arar_Unsupervised_Multi-Modal_Image_Registration_via_Geometry_Preserving_Image-to-Image_Translation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Arar_Unsupervised_Multi-Modal_Image_Registration_via_Geometry_Preserving_Image-to-Image_Translation_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Arar_Unsupervised_Multi-Modal_Image_CVPR_2020_supplemental.pdf | null | null |
SCATTER: Selective Context Attentional Scene Text Recognizer | Ron Litman, Oron Anschel, Shahar Tsiper, Roee Litman, Shai Mazor, R. Manmatha | Scene Text Recognition (STR), the task of recognizing text against complex image backgrounds, is an active area of research. Current state-of-the-art (SOTA) methods still struggle to recognize text written in arbitrary shapes. In this paper, we introduce a novel architecture for STR, named Selective Context ATtentional Text Recognizer (SCATTER). SCATTER utilizes a stacked block architecture with intermediate supervision during training, that paves the way to successfully train a deep BiLSTM encoder, thus improving the encoding of contextual dependencies. Decoding is done using a two-step 1D attention mechanism. The first attention step re-weights visual features from a CNN backbone together with contextual features computed by a BiLSTM layer. The second attention step, similar to previous papers, treats the features as a sequence and attends to the intra-sequence relationships. Experiments show that the proposed approach surpasses SOTA performance on irregular text recognition benchmarks by 3.7% on average. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Litman_SCATTER_Selective_Context_Attentional_Scene_Text_Recognizer_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.11288 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Litman_SCATTER_Selective_Context_Attentional_Scene_Text_Recognizer_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Litman_SCATTER_Selective_Context_Attentional_Scene_Text_Recognizer_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Litman_SCATTER_Selective_Context_CVPR_2020_supplemental.pdf | null | null |
Incremental Few-Shot Object Detection | Juan-Manuel Perez-Rua, Xiatian Zhu, Timothy M. Hospedales, Tao Xiang | Existing object detection methods typically rely on the availability of abundant labelled training samples per class and offline model training in a batch mode. These requirements substantially limit their scalability to open-ended accommodation of novel classes with limited labelled training data, both in terms of model accuracy and training efficiency during deployment. We present the first study aiming to go beyond these limitations by considering the Incremental Few-Shot Detection (iFSD) problem setting, where new classes must be registered incrementally (without revisiting base classes) and with few examples. To this end we propose OpeN-ended Centre nEt (ONCE), a detector designed for incrementally learning to detect novel class objects with few examples. This is achieved by an elegant adaptation of the efficient CentreNet detector to the few-shot learning scenario, and meta-learning a class-wise code generator model for registering novel classes. ONCE fully respects the incremental learning paradigm, with novel class registration requiring only a single forward pass of few-shot training samples, and no access to base classes - thus making it suitable for deployment on embedded devices, etc. Extensive experiments conducted on both the standard object detection (COCO, PASCAL VOC) and fashion landmark detection (DeepFashion2) tasks show the feasibility of iFSD for the first time, opening an interesting and very important line of research. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Perez-Rua_Incremental_Few-Shot_Object_Detection_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Perez-Rua_Incremental_Few-Shot_Object_Detection_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Perez-Rua_Incremental_Few-Shot_Object_Detection_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Cloth in the Wind: A Case Study of Physical Measurement Through Simulation | Tom F. H. Runia, Kirill Gavrilyuk, Cees G. M. Snoek, Arnold W. M. Smeulders | For many of the physical phenomena around us, we have developed sophisticated models explaining their behavior. Nevertheless, measuring physical properties from visual observations is challenging due to the high number of causally underlying physical parameters -- including material properties and external forces. In this paper, we propose to measure latent physical properties for cloth in the wind without ever having seen a real example before. Our solution is an iterative refinement procedure with simulation at its core. The algorithm gradually updates the physical model parameters by running a simulation of the observed phenomenon and comparing the current simulation to a real-world observation. The correspondence is measured using an embedding function that maps physically similar examples to nearby points. We consider a case study of cloth in the wind, with curling flags as our leading example -- a seemingly simple phenomena but physically highly involved. Based on the physics of cloth and its visual manifestation, we propose an instantiation of the embedding function. For this mapping, modeled as a deep network, we introduce a spectral layer that decomposes a video volume into its temporal spectral power and corresponding frequencies. Our experiments demonstrate that the proposed method compares favorably to prior work on the task of measuring cloth material properties and external wind force from a real-world video. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Runia_Cloth_in_the_Wind_A_Case_Study_of_Physical_Measurement_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.05065 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Runia_Cloth_in_the_Wind_A_Case_Study_of_Physical_Measurement_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Runia_Cloth_in_the_Wind_A_Case_Study_of_Physical_Measurement_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Runia_Cloth_in_the_CVPR_2020_supplemental.pdf | null | null |
Generalized Zero-Shot Learning via Over-Complete Distribution | Rohit Keshari, Richa Singh, Mayank Vatsa | A well trained and generalized deep neural network (DNN) should be robust to both seen and unseen classes. However, the performance of most of the existing supervised DNN algorithms degrade for classes which are unseen in the training set. To learn a discriminative classifier which yields good performance in Zero-Shot Learning (ZSL) settings, we propose to generate an Over-Complete Distribution (OCD) using Conditional Variational Autoencoder (CVAE) of both seen and unseen classes. In order to enforce the separability between classes and reduce the class scatter, we propose the use of Online Batch Triplet Loss (OBTL) and Center Loss (CL) on the generated OCD. The effectiveness of the framework is evaluated using both Zero-Shot Learning and Generalized Zero-Shot Learning protocols on three publicly available benchmark databases, SUN, CUB and AWA2. The results show that generating over-complete distributions and enforcing the classifier to learn a transform function from overlapping to non-overlapping distributions can improve the performance on both seen and unseen classes. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Keshari_Generalized_Zero-Shot_Learning_via_Over-Complete_Distribution_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.00666 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Keshari_Generalized_Zero-Shot_Learning_via_Over-Complete_Distribution_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Keshari_Generalized_Zero-Shot_Learning_via_Over-Complete_Distribution_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
On the General Value of Evidence, and Bilingual Scene-Text Visual Question Answering | Xinyu Wang, Yuliang Liu, Chunhua Shen, Chun Chet Ng, Canjie Luo, Lianwen Jin, Chee Seng Chan, Anton van den Hengel, Liangwei Wang | Visual Question Answering (VQA) methods have made incredible progress, but suffer from a failure to generalize. This is visible in the fact that they are vulnerable to learning coincidental correlations in the data rather than deeper relations between image content and ideas expressed in language. We present a dataset that takes a step towards addressing this problem in that it contains questions expressed in two languages, and an evaluation process that co-opts a well understood image-based metric to reflect the method's ability to reason. Measuring reasoning directly encourages generalization by penalizing answers that are coincidentally correct. The dataset reflects the scene-text version of the VQA problem, and the reasoning evaluation can be seen as a text-based version of a referring expression challenge. Experiments and analyses are provided that show the value of the dataset. The dataset is available at www.est-vqa.org. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wang_On_the_General_Value_of_Evidence_and_Bilingual_Scene-Text_Visual_CVPR_2020_paper.pdf | http://arxiv.org/abs/2002.10215 | https://www.youtube.com/watch?v=86SdJC4fChI | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_On_the_General_Value_of_Evidence_and_Bilingual_Scene-Text_Visual_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_On_the_General_Value_of_Evidence_and_Bilingual_Scene-Text_Visual_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Designing Network Design Spaces | Ilija Radosavovic, Raj Prateek Kosaraju, Ross Girshick, Kaiming He, Piotr Dollar | In this work, we present a new network design paradigm. Our goal is to help advance the understanding of network design and discover design principles that generalize across settings. Instead of focusing on designing individual network instances, we design network design spaces that parametrize populations of networks. The overall process is analogous to classic manual design of networks, but elevated to the design space level. Using our methodology we explore the structure aspect of network design and arrive at a low-dimensional design space consisting of simple, regular networks that we call RegNet. The core insight of the RegNet parametrization is surprisingly simple: widths and depths of good networks can be explained by a quantized linear function. We analyze the RegNet design space and arrive at interesting findings that do not match the current practice of network design. The RegNet design space provides simple and fast networks that work well across a wide range of flop regimes. Under comparable training settings and flops, the RegNet models outperform the popular EfficientNet models while being up to 5x faster on GPUs. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Radosavovic_Designing_Network_Design_Spaces_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.13678 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Radosavovic_Designing_Network_Design_Spaces_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Radosavovic_Designing_Network_Design_Spaces_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Regularizing CNN Transfer Learning With Randomised Regression | Yang Zhong, Atsuto Maki | This paper is about regularizing deep convolutional networks (CNNs) based on an adaptive framework for transfer learning with limited training data in the target domain. Recent advances of CNN regularization in this context are commonly due to the use of additional regularization objectives. They guide the training away from the target task using some forms of concrete tasks. Unlike those related approaches, we suggest that an objective without a concrete goal can still serve well as a regularizer. In particular, we demonstrate Pseudo-task Regularization (PtR) which dynamically regularizes a network by simply attempting to regress image representations to pseudo-regression targets during fine-tuning. That is, a CNN is efficiently regularized without additional resources of data or prior domain expertise. In sum, the proposed PtR provides: a) an alternative for network regularization without dependence on the design of concrete regularization objectives or extra annotations; b) a dynamically adjusted and maintained strength of regularization effect by balancing the gradient norms between objectives on-line. Through numerous experiments, surprisingly, the improvements on classification accuracy by PtR are shown greater or on a par to the recent state-of-the-art methods. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zhong_Regularizing_CNN_Transfer_Learning_With_Randomised_Regression_CVPR_2020_paper.pdf | http://arxiv.org/abs/1908.05997 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhong_Regularizing_CNN_Transfer_Learning_With_Randomised_Regression_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhong_Regularizing_CNN_Transfer_Learning_With_Randomised_Regression_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Zhong_Regularizing_CNN_Transfer_CVPR_2020_supplemental.pdf | null | null |
PVN3D: A Deep Point-Wise 3D Keypoints Voting Network for 6DoF Pose Estimation | Yisheng He, Wei Sun, Haibin Huang, Jianran Liu, Haoqiang Fan, Jian Sun | In this work, we present a novel data-driven method for robust 6DoF object pose estimation from a single RGBD image. Unlike previous methods that directly regressing pose parameters, we tackle this challenging task with a keypoint-based approach. Specifically, we propose a deep Hough voting network to detect 3D keypoints of objects and then estimate the 6D pose parameters within a least-squares fitting manner. Our method is a natural extension of 2D-keypoint approaches that successfully work on RGB based 6DoF estimation. It allows us to fully utilize the geometric constraint of rigid objects with the extra depth information and is easy for a network to learn and optimize. Extensive experiments were conducted to demonstrate the effectiveness of 3D-keypoint detection in the 6D pose estimation task. Experimental results also show our method outperforms the state-of-the-art methods by large margins on several benchmarks. Code and video are available at https://github.com/ethnhe/PVN3D.git. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/He_PVN3D_A_Deep_Point-Wise_3D_Keypoints_Voting_Network_for_6DoF_CVPR_2020_paper.pdf | http://arxiv.org/abs/1911.04231 | https://www.youtube.com/watch?v=FhvhoGou9H8 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/He_PVN3D_A_Deep_Point-Wise_3D_Keypoints_Voting_Network_for_6DoF_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/He_PVN3D_A_Deep_Point-Wise_3D_Keypoints_Voting_Network_for_6DoF_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/He_PVN3D_A_Deep_CVPR_2020_supplemental.pdf | null | null |
Domain-Aware Visual Bias Eliminating for Generalized Zero-Shot Learning | Shaobo Min, Hantao Yao, Hongtao Xie, Chaoqun Wang, Zheng-Jun Zha, Yongdong Zhang | Generalized zero-shot learning aims to recognize images from seen and unseen domains. Recent methods focus on learning a unified semantic-aligned visual representation to transfer knowledge between two domains, while ignoring the effect of semantic-free visual representation in alleviating the biased recognition problem. In this paper, we propose a novel Domain-aware Visual Bias Eliminating (DVBE) network that constructs two complementary visual representations, i.e., semantic-free and semantic-aligned, to treat seen and unseen domains separately. Specifically, we explore cross-attentive second-order visual statistics to compact the semantic-free representation, and design an adaptive margin Softmax to maximize inter-class divergences. Thus, the semantic-free representation becomes discriminative enough to not only predict seen class accurately but also filter out unseen images, i.e., domain detection, based on the predicted class entropy. For unseen images, we automatically search an optimal semantic-visual alignment architecture, rather than manual designs, to predict unseen classes. With accurate domain detection, the biased recognition problem towards the seen domain is significantly reduced. Experiments on five benchmarks for classification and segmentation show that DVBE outperforms existing methods by averaged 5.7% improvement. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Min_Domain-Aware_Visual_Bias_Eliminating_for_Generalized_Zero-Shot_Learning_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.13261 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Min_Domain-Aware_Visual_Bias_Eliminating_for_Generalized_Zero-Shot_Learning_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Min_Domain-Aware_Visual_Bias_Eliminating_for_Generalized_Zero-Shot_Learning_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Min_Domain-Aware_Visual_Bias_CVPR_2020_supplemental.pdf | null | null |
VSGNet: Spatial Attention Network for Detecting Human Object Interactions Using Graph Convolutions | Oytun Ulutan, A S M Iftekhar, B. S. Manjunath | Comprehensive visual understanding requires detection frameworks that can effectively learn and utilize object interactions while analyzing objects individually. This is the main objective in Human-Object Interaction (HOI) detection task. In particular, relative spatial reasoning and structural connections between objects are essential cues for analyzing interactions, which is addressed by the proposed Visual-Spatial-Graph Network (VSGNet) architecture. VSGNet extracts visual features from the human-object pairs, refines the features with spatial configurations of the pair, and utilizes the structural connections between the pair via graph convolutions. The performance of VSGNet is thoroughly evaluated using the Verbs in COCO (V-COCO) dataset. Experimental results indicate that VSGNet outperforms state-of-the-art solutions by 8% or 4 mAP. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Ulutan_VSGNet_Spatial_Attention_Network_for_Detecting_Human_Object_Interactions_Using_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.05541 | https://www.youtube.com/watch?v=ALeqDlVq0z4 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Ulutan_VSGNet_Spatial_Attention_Network_for_Detecting_Human_Object_Interactions_Using_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Ulutan_VSGNet_Spatial_Attention_Network_for_Detecting_Human_Object_Interactions_Using_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Few-Shot Video Classification via Temporal Alignment | Kaidi Cao, Jingwei Ji, Zhangjie Cao, Chien-Yi Chang, Juan Carlos Niebles | Difficulty in collecting and annotating large-scale video data raises a growing interest in learning models which can recognize novel classes with only a few training examples. In this paper, we propose the Ordered Temporal Alignment Module (OTAM), a novel few-shot learning framework that can learn to classify a previously unseen video. While most previous work neglects long-term temporal ordering information, our proposed model explicitly leverages the temporal ordering information in video data through ordered temporal alignment. This leads to strong data-efficiency for few-shot learning. In concrete, our proposed pipeline learns a deep distance measurement of the query video with respect to novel class proxies over its alignment path. We adopt an episode-based training scheme and directly optimize the few-shot learning objective. We evaluate OTAM on two challenging real-world datasets, Kinetics and Something-Something-V2, and show that our model leads to significant improvement of few-shot video classification over a wide range of competitive baselines and outperforms state-of-the-art benchmarks by a large margin. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Cao_Few-Shot_Video_Classification_via_Temporal_Alignment_CVPR_2020_paper.pdf | http://arxiv.org/abs/1906.11415 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Cao_Few-Shot_Video_Classification_via_Temporal_Alignment_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Cao_Few-Shot_Video_Classification_via_Temporal_Alignment_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Density-Aware Graph for Deep Semi-Supervised Visual Recognition | Suichan Li, Bin Liu, Dongdong Chen, Qi Chu, Lu Yuan, Nenghai Yu | Semi-supervised learning (SSL) has been extensively studied to improve the generalization ability of deep neural networks for visual recognition. To involve the unlabelled data, most existing SSL methods are based on common density-based cluster assumption: samples lying in the same high-density region are likely to belong to the same class, including the methods performing consistency regularization or generating pseudo-labels for the unlabelled images. Despite their impressive performance, we argue three limitations exist: 1) Though the density information is demonstrated to be an important clue, they all use it in an implicit way and have not exploited it in depth. 2) For feature learning, they often learn the feature embedding based on the single data sample and ignore the neighborhood information. 3) For label-propagation based pseudo-label generation, it is often done offline and difficult to be end-to-end trained with feature learning. Motivated by these limitations, this paper proposes to solve the SSL problem by building a novel density-aware graph, based on which the neighborhood information can be easily leveraged and the feature learning and label propagation can also be trained in an end-to-end way. Specifically, we first propose a new Density-aware Neighborhood Aggregation(DNA) module to learn more discriminative features by incorporating the neighborhood information in a density-aware manner. Then a novel Density-ascending Path based Label Propagation(DPLP) module is proposed to generate the pseudo-labels for unlabeled samples more efficiently according to the feature distribution characterized by density. Finally, the DNA module and DPLP module evolve and improve each other end-to-end. Extensive experiments demonstrate the effectiveness of the newly proposed density-aware graph based SSL framework and our approach can outperform current state-of-the-art methods by a large margin. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Li_Density-Aware_Graph_for_Deep_Semi-Supervised_Visual_Recognition_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.13194 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Density-Aware_Graph_for_Deep_Semi-Supervised_Visual_Recognition_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Density-Aware_Graph_for_Deep_Semi-Supervised_Visual_Recognition_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Learning Deep Network for Detecting 3D Object Keypoints and 6D Poses | Wanqing Zhao, Shaobo Zhang, Ziyu Guan, Wei Zhao, Jinye Peng, Jianping Fan | The state-of-art 6D object pose detection methods use convolutional neural networks to estimate objects' 6D poses from RGB images. However, they require huge numbers of images with explicit 3D annotations such as 6D poses, 3D bounding boxes and 3D keypoints, either obtained by manual labeling or inferred from synthetic images generated by 3D CAD models. Manual labeling for a large number of images is a laborious task, and we usually do not have the corresponding 3D CAD models of objects in real environment. In this paper, we develop a keypoint-based 6D object pose detection method (and its deep network) called Object Keypoint based POSe Estimation (OK-POSE). OK-POSE employs relative transformation between viewpoints for training. Specifically, we use pairs of images with object annotation and relative transformation information between their viewpoints to automatically discover objects' 3D keypoints which are geometrically and visually consistent. Then, the 6D object pose can be estimated using a keypoint-based geometric reasoning method with a reference viewpoint. The relative transformation information can be easily obtained from any cheap binocular cameras or most smartphone devices, thus greatly lowering the labeling cost. Experiments have demonstrated that OK-POSE achieves acceptable performance compared to methods relying on the object's 3D CAD model or a great deal of 3D labeling. These results show that our method can be used as a suitable alternative when there are no 3D CAD models or a large number of 3D annotations. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zhao_Learning_Deep_Network_for_Detecting_3D_Object_Keypoints_and_6D_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhao_Learning_Deep_Network_for_Detecting_3D_Object_Keypoints_and_6D_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhao_Learning_Deep_Network_for_Detecting_3D_Object_Keypoints_and_6D_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Zhao_Learning_Deep_Network_CVPR_2020_supplemental.pdf | null | null |
REVERIE: Remote Embodied Visual Referring Expression in Real Indoor Environments | Yuankai Qi, Qi Wu, Peter Anderson, Xin Wang, William Yang Wang, Chunhua Shen, Anton van den Hengel | One of the long-term challenges of robotics is to enable robots to interact with humans in the visual world via natural language, as humans are visual animals that communicate through language. Overcoming this challenge requires the ability to perform a wide variety of complex tasks in response to multifarious instructions from humans. In the hope that it might drive progress towards more flexible and powerful human interactions with robots, we propose a dataset of varied and complex robot tasks, described in natural language, in terms of objects visible in a large set of real images. Given an instruction, success requires navigating through a previously-unseen environment to identify an object. This represents a practical challenge, but one that closely reflects one of the core visual problems in robotics. Several state-of-the-art vision-and-language navigation, and referring-expression models are tested to verify the difficulty of this new task, but none of them show promising results because there are many fundamental differences between our task and previous ones. A novel Interactive Navigator-Pointer model is also proposed that provides a strong baseline on the task. The proposed model especially achieves the best performance on the unseen test split, but still leaves substantial room for improvement compared to the human performance. Repository: https://github.com/YuankaiQi/REVERIE. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Qi_REVERIE_Remote_Embodied_Visual_Referring_Expression_in_Real_Indoor_Environments_CVPR_2020_paper.pdf | http://arxiv.org/abs/1904.10151 | https://www.youtube.com/watch?v=UWYTLZUgxjY | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Qi_REVERIE_Remote_Embodied_Visual_Referring_Expression_in_Real_Indoor_Environments_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Qi_REVERIE_Remote_Embodied_Visual_Referring_Expression_in_Real_Indoor_Environments_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Qi_REVERIE_Remote_Embodied_CVPR_2020_supplemental.pdf | https://cove.thecvf.com/datasets/335 | null |
Deep Iterative Surface Normal Estimation | Jan Eric Lenssen, Christian Osendorfer, Jonathan Masci | This paper presents an end-to-end differentiable algorithm for robust and detail-preserving surface normal estimation on unstructured point-clouds. We utilize graph neural networks to iteratively parameterize an adaptive anisotropic kernel that produces point weights for weighted least-squares plane fitting in local neighborhoods. The approach retains the interpretability and efficiency of traditional sequential plane fitting while benefiting from adaptation to data set statistics through deep learning. This results in a state-of-the-art surface normal estimator that is robust to noise, outliers and point density variation, preserves sharp features through anisotropic kernels and equivariance through a local quaternion-based spatial transformer. Contrary to previous deep learning methods, the proposed approach does not require any hand-crafted features or preprocessing. It improves on the state-of-the-art results while being more than two orders of magnitude faster and more parameter efficient. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Lenssen_Deep_Iterative_Surface_Normal_Estimation_CVPR_2020_paper.pdf | http://arxiv.org/abs/1904.07172 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Lenssen_Deep_Iterative_Surface_Normal_Estimation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Lenssen_Deep_Iterative_Surface_Normal_Estimation_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Lenssen_Deep_Iterative_Surface_CVPR_2020_supplemental.pdf | null | null |
Unified Dynamic Convolutional Network for Super-Resolution With Variational Degradations | Yu-Syuan Xu, Shou-Yao Roy Tseng, Yu Tseng, Hsien-Kai Kuo, Yi-Min Tsai | Deep Convolutional Neural Networks (CNNs) have achieved remarkable results on Single Image Super-Resolution (SISR). Despite considering only a single degradation, recent studies also include multiple degrading effects to better reflect real-world cases. However, most of the works assume a fixed combination of degrading effects, or even train an individual network for different combinations. Instead, a more practical approach is to train a single network for wide-ranging and variational degradations. To fulfill this requirement, this paper proposes a unified network to accommodate the variations from inter-image (cross-image variations) and intra-image (spatial variations). Different from the existing works, we incorporate dynamic convolution which is a far more flexible alternative to handle different variations. In SISR with non-blind setting, our Unified Dynamic Convolutional Network for Variational Degradations (UDVD) is evaluated on both synthetic and real images with an extensive set of variations. The qualitative results demonstrate the effectiveness of UDVD over various existing works. Extensive experiments show that our UDVD achieves favorable or comparable performance on both synthetic and real images. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Xu_Unified_Dynamic_Convolutional_Network_for_Super-Resolution_With_Variational_Degradations_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.06965 | https://www.youtube.com/watch?v=_B8CYhxWK38 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Xu_Unified_Dynamic_Convolutional_Network_for_Super-Resolution_With_Variational_Degradations_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Xu_Unified_Dynamic_Convolutional_Network_for_Super-Resolution_With_Variational_Degradations_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Noisier2Noise: Learning to Denoise From Unpaired Noisy Data | Nick Moran, Dan Schmidt, Yu Zhong, Patrick Coady | We present a method for training a neural network to perform image denoising without access to clean training examples or access to paired noisy training examples. Our method requires only a single noisy realization of each training example and a statistical model of the noise distribution, and is applicable to a wide variety of noise models, including spatially structured noise. Our model produces results which are competitive with other learned methods which require richer training data, and outperforms traditional non-learned denoising methods. We present derivations of our method for arbitrary additive noise, an improvement specific to Gaussian additive noise, and an extension to multiplicative Bernoulli noise. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Moran_Noisier2Noise_Learning_to_Denoise_From_Unpaired_Noisy_Data_CVPR_2020_paper.pdf | http://arxiv.org/abs/1910.11908 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Moran_Noisier2Noise_Learning_to_Denoise_From_Unpaired_Noisy_Data_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Moran_Noisier2Noise_Learning_to_Denoise_From_Unpaired_Noisy_Data_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Moran_Noisier2Noise_Learning_to_CVPR_2020_supplemental.pdf | null | null |
PhysGAN: Generating Physical-World-Resilient Adversarial Examples for Autonomous Driving | Zelun Kong, Junfeng Guo, Ang Li, Cong Liu | Although Deep neural networks (DNNs) are being pervasively used in vision-based autonomous driving systems, they are found vulnerable to adversarial attacks where small-magnitude perturbations into the inputs during test time cause dramatic changes to the outputs. While most of the recent attack methods target at digital-world adversarial scenarios, it is unclear how they perform in the physical world, and more importantly, the generated perturbations under such methods would cover a whole driving scene including those fixed background imagery such as the sky, making them inapplicable to physical world implementation. We present PhysGAN, which generates physical-world-resilient adversarial examples for misleading autonomous driving systems in a continuous manner. We show the effectiveness and robustness of PhysGAN via extensive digital- and real-world evaluations. We compare PhysGAN with a set of state-of-the-art baseline methods, which further demonstrate the robustness and efficacy of our approach. We also show that PhysGAN outperforms state-of-the-art baseline methods. To the best of our knowledge, PhysGAN is probably the first technique of generating realistic and physical-world-resilient adversarial examples for attacking common autonomous driving scenarios. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Kong_PhysGAN_Generating_Physical-World-Resilient_Adversarial_Examples_for_Autonomous_Driving_CVPR_2020_paper.pdf | http://arxiv.org/abs/1907.04449 | https://www.youtube.com/watch?v=3lnS02p4_7U | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Kong_PhysGAN_Generating_Physical-World-Resilient_Adversarial_Examples_for_Autonomous_Driving_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Kong_PhysGAN_Generating_Physical-World-Resilient_Adversarial_Examples_for_Autonomous_Driving_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Fast(er) Reconstruction of Shredded Text Documents via Self-Supervised Deep Asymmetric Metric Learning | Thiago M. Paixao, Rodrigo F. Berriel, Maria C. S. Boeres, Alessandro L. Koerich, Claudine Badue, Alberto F. De Souza, Thiago Oliveira-Santos | The reconstruction of shredded documents consists in arranging the pieces of paper (shreds) in order to reassemble the original aspect of such documents. This task is particularly relevant for supporting forensic investigation as documents may contain criminal evidence. As an alternative to the laborious and time-consuming manual process, several researchers have been investigating ways to perform automatic digital reconstruction. A central problem in automatic reconstruction of shredded documents is the pairwise compatibility evaluation of the shreds, notably for binary text documents. In this context, deep learning has enabled great progress for accurate reconstructions in the domain of mechanically-shredded documents. A sensitive issue, however, is that current deep model solutions require an inference whenever a pair of shreds has to be evaluated. This work proposes a scalable deep learning approach for measuring pairwise compatibility in which the number of inferences scales linearly (rather than quadratically) with the number of shreds. Instead of predicting compatibility directly, deep models are leveraged to asymmetrically project the raw shred content onto a common metric space in which distance is proportional to the compatibility. Experimental results show that our method has accuracy comparable to the state-of-the-art with a speed-up of about 22 times for a test instance with 505 shreds (20 mixed shredded-pages from different documents). | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Paixao_Faster_Reconstruction_of_Shredded_Text_Documents_via_Self-Supervised_Deep_Asymmetric_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.10063 | https://www.youtube.com/watch?v=NCRN6Zy_9AU | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Paixao_Faster_Reconstruction_of_Shredded_Text_Documents_via_Self-Supervised_Deep_Asymmetric_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Paixao_Faster_Reconstruction_of_Shredded_Text_Documents_via_Self-Supervised_Deep_Asymmetric_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Paixao_Faster_Reconstruction_of_CVPR_2020_supplemental.pdf | null | null |
MoreFusion: Multi-object Reasoning for 6D Pose Estimation from Volumetric Fusion | Kentaro Wada, Edgar Sucar, Stephen James, Daniel Lenton, Andrew J. Davison | Robots and other smart devices need efficient object-based scene representations from their on-board vision systems to reason about contact, physics and occlusion. Recognized precise object models will play an important role alongside non-parametric reconstructions of unrecognized structures. We present a system which can estimate the accurate poses of multiple known objects in contact and occlusion from real-time, embodied multi-view vision. Our approach makes 3D object pose proposals from single RGB-D views, accumulates pose estimates and non-parametric occupancy information from multiple views as the camera moves, and performs joint optimization to estimate consistent, non-intersecting poses for multiple objects in contact. We verify the accuracy and robustness of our approach experimentally on 2 object datasets: YCB-Video, and our own challenging Cluttered YCB-Video. We demonstrate a real-time robotics application where a robot arm precisely and orderly disassembles complicated piles of objects, using only on-board RGB-D vision. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wada_MoreFusion_Multi-object_Reasoning_for_6D_Pose_Estimation_from_Volumetric_Fusion_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.04336 | https://www.youtube.com/watch?v=pJq10WWXpec | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Wada_MoreFusion_Multi-object_Reasoning_for_6D_Pose_Estimation_from_Volumetric_Fusion_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Wada_MoreFusion_Multi-object_Reasoning_for_6D_Pose_Estimation_from_Volumetric_Fusion_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Wada_MoreFusion_Multi-object_Reasoning_CVPR_2020_supplemental.zip | null | null |
Filter Response Normalization Layer: Eliminating Batch Dependence in the Training of Deep Neural Networks | Saurabh Singh, Shankar Krishnan | Batch Normalization (BN) uses mini-batch statistics to normalize the activations during training, introducing dependence between mini-batch elements. This dependency can hurt the performance if the mini-batch size is too small, or if the elements are correlated. Several alternatives, such as Batch Renormalization and Group Normalization (GN), have been proposed to address this issue. However, they either do not match the performance of BN for large batches, or still exhibit degradation in performance for smaller batches, or introduce artificial constraints on the model architecture. In this paper we propose the Filter Response Normalization (FRN) layer, a novel combination of a normalization and an activation function, that can be used as a replacement for other normalizations and activations. Our method operates on each activation channel of each batch element independently, eliminating the dependency on other batch elements. Our method outperforms BN and other alternatives in a variety of settings for all batch sizes. FRN layer performs 0.7-1.0% better than BN on top-1 validation accuracy with large mini-batch sizes for Imagenet classification using InceptionV3 and ResnetV2-50 architectures. Further, it performs >1% better than GN on the same problem in the small mini-batch size regime. For object detection problem on COCO dataset, FRN layer outperforms all other methods by at least 0.3-0.5% in all batch size regimes. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Singh_Filter_Response_Normalization_Layer_Eliminating_Batch_Dependence_in_the_Training_CVPR_2020_paper.pdf | http://arxiv.org/abs/1911.09737 | https://www.youtube.com/watch?v=OcZD6oyX1eI | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Singh_Filter_Response_Normalization_Layer_Eliminating_Batch_Dependence_in_the_Training_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Singh_Filter_Response_Normalization_Layer_Eliminating_Batch_Dependence_in_the_Training_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Visual Reaction: Learning to Play Catch With Your Drone | Kuo-Hao Zeng, Roozbeh Mottaghi, Luca Weihs, Ali Farhadi | In this paper we address the problem of visual reaction: the task of interacting with dynamic environments where the changes in the environment are not necessarily caused by the agents itself. Visual reaction entails predicting the future changes in a visual environment and planning accordingly. We study the problem of visual reaction in the context of playing catch with a drone in visually rich synthetic environments. This is a challenging problem since the agent is required to learn (1) how objects with different physical properties and shapes move, (2) what sequence of actions should be taken according to the prediction, (3) how to adjust the actions based on the visual feedback from the dynamic environment (e.g., when objects bouncing off a wall), and (4) how to reason and act with an unexpected state change in a timely manner. We propose a new dataset for this task, which includes 30K throws of 20 types of objects in different directions with different forces. Our results show that our model that integrates a forecaster with a planner outperforms a set of strong baselines that are based on tracking as well as pure model-based and model-free RL baselines. The code and dataset are available at github.com/KuoHaoZeng/Visual_Reaction. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zeng_Visual_Reaction_Learning_to_Play_Catch_With_Your_Drone_CVPR_2020_paper.pdf | http://arxiv.org/abs/1912.02155 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zeng_Visual_Reaction_Learning_to_Play_Catch_With_Your_Drone_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zeng_Visual_Reaction_Learning_to_Play_Catch_With_Your_Drone_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Zeng_Visual_Reaction_Learning_CVPR_2020_supplemental.pdf | null | null |
Learning to See Through Obstructions | Yu-Lun Liu, Wei-Sheng Lai, Ming-Hsuan Yang, Yung-Yu Chuang, Jia-Bin Huang | We present a learning-based approach for removing unwanted obstructions, such as window reflections, fence occlusions or raindrops, from a short sequence of images captured by a moving camera. Our method leverages the motion differences between the background and the obstructing elements to recover both layers. Specifically, we alternate between estimating dense optical flow fields of the two layers and reconstructing each layer from the flow-warped images via a deep convolutional neural network. The learning-based layer reconstruction allows us to accommodate potential errors in the flow estimation and brittle assumptions such as brightness consistency. We show that training on synthetically generated data transfers well to real images. Our results on numerous challenging scenarios of reflection and fence removal demonstrate the effectiveness of the proposed method. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Liu_Learning_to_See_Through_Obstructions_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Liu_Learning_to_See_Through_Obstructions_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Liu_Learning_to_See_Through_Obstructions_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Liu_Learning_to_See_CVPR_2020_supplemental.pdf | null | null |
SpeedNet: Learning the Speediness in Videos | Sagie Benaim, Ariel Ephrat, Oran Lang, Inbar Mosseri, William T. Freeman, Michael Rubinstein, Michal Irani, Tali Dekel | We wish to automatically predict the "speediness" of moving objects in videos - whether they move faster, at, or slower than their "natural" speed. The core component in our approach is SpeedNet--a novel deep network trained to detect if a video is playing at normal rate, or if it is sped up. SpeedNet is trained on a large corpus of natural videos in a self-supervised manner, without requiring any manual annotations. We show how this single, binary classification network can be used to detect arbitrary rates of speediness of objects. We demonstrate prediction results by SpeedNet on a wide range of videos containing complex natural motions, and examine the visual cues it utilizes for making those predictions. Importantly, we show that through predicting the speed of videos, the model learns a powerful and meaningful space-time representation that goes beyond simple motion cues. We demonstrate how those learned features can boost the performance of self supervised action recognition, and can be used for video retrieval. Furthermore, we also apply SpeedNet for generating time-varying, adaptive video speedups, which can allow viewers to watch videos faster, but with less of the jittery, unnatural motions typical to videos that are sped up uniformly. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Benaim_SpeedNet_Learning_the_Speediness_in_Videos_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.06130 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Benaim_SpeedNet_Learning_the_Speediness_in_Videos_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Benaim_SpeedNet_Learning_the_Speediness_in_Videos_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Benaim_SpeedNet_Learning_the_CVPR_2020_supplemental.zip | null | null |
IMRAM: Iterative Matching With Recurrent Attention Memory for Cross-Modal Image-Text Retrieval | Hui Chen, Guiguang Ding, Xudong Liu, Zijia Lin, Ji Liu, Jungong Han | Enabling bi-directional retrieval of images and texts is important for understanding the correspondence between vision and language. Existing methods leverage the attention mechanism to explore such correspondence in a fine-grained manner. However, most of them consider all semantics equally and thus align them uniformly, regardless of their diverse complexities. In fact, semantics are diverse (i.e. involving different kinds of semantic concepts), and humans usually follow a latent structure to combine them into understandable languages. It may be difficult to optimally capture such sophisticated correspondences in existing methods. In this paper, to address such a deficiency, we propose an Iterative Matching with Recurrent Attention Memory (IMRAM) method, in which correspondences between images and texts are captured with multiple steps of alignments. Specifically, we introduce an iterative matching scheme to explore such fine-grained correspondence progressively. A memory distillation unit is used to refine alignment knowledge from early steps to later ones. Experiment results on three benchmark datasets, i.e. Flickr8K, Flickr30K, and MS COCO, show that our IMRAM achieves state-of-the-art performance, well demonstrating its effectiveness. Experiments on a practical business advertisement dataset, named KWAI-AD, further validates the applicability of our method in practical scenarios. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Chen_IMRAM_Iterative_Matching_With_Recurrent_Attention_Memory_for_Cross-Modal_Image-Text_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.03772 | https://www.youtube.com/watch?v=Fq7NRq1OQEo | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Chen_IMRAM_Iterative_Matching_With_Recurrent_Attention_Memory_for_Cross-Modal_Image-Text_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Chen_IMRAM_Iterative_Matching_With_Recurrent_Attention_Memory_for_Cross-Modal_Image-Text_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Satellite Image Time Series Classification With Pixel-Set Encoders and Temporal Self-Attention | Vivien Sainte Fare Garnot, Loic Landrieu, Sebastien Giordano, Nesrine Chehata | Satellite image time series, bolstered by their growing availability, are at the forefront of an extensive effort towards automated Earth monitoring by international institutions. In particular, large-scale control of agricultural parcels is an issue of major political and economic importance. In this regard, hybrid convolutional-recurrent neural architectures have shown promising results for the automated classification of satellite image time series. We propose an alternative approach in which the convolutional layers are advantageously replaced with encoders operating on unordered sets of pixels to exploit the typically coarse resolution of publicly available satellite images. We also propose to extract temporal features using a bespoke neural architecture based on self-attention instead of recurrent networks. We demonstrate experimentally that our method not only outperforms previous state-of-the-art approaches in terms of precision, but also significantly decreases processing time and memory requirements. Lastly, we release a large open-access annotated dataset as a benchmark for future work on satellite image time series. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Garnot_Satellite_Image_Time_Series_Classification_With_Pixel-Set_Encoders_and_Temporal_CVPR_2020_paper.pdf | http://arxiv.org/abs/1911.07757 | https://www.youtube.com/watch?v=9sVMZ6TP7Cs | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Garnot_Satellite_Image_Time_Series_Classification_With_Pixel-Set_Encoders_and_Temporal_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Garnot_Satellite_Image_Time_Series_Classification_With_Pixel-Set_Encoders_and_Temporal_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Garnot_Satellite_Image_Time_CVPR_2020_supplemental.pdf | null | null |
Train in Germany, Test in the USA: Making 3D Object Detectors Generalize | Yan Wang, Xiangyu Chen, Yurong You, Li Erran Li, Bharath Hariharan, Mark Campbell, Kilian Q. Weinberger, Wei-Lun Chao | In the domain of autonomous driving, deep learning has substantially improved the 3D object detection accuracy for LiDAR and stereo camera data alike. While deep networks are great at generalization, they are also notorious to overfit to all kinds of spurious artifacts, such as brightness, car sizes and models, that may appear consistently throughout the data. In fact, most datasets for autonomous driving are collected within a narrow subset of cities within one country, typically under similar weather conditions. In this paper we consider the task of adapting 3D object detectors from one dataset to another. We observe that naively, this appears to be a very challenging task, resulting in drastic drops in accuracy levels. We provide extensive experiments to investigate the true adaptation challenges and arrive at a surprising conclusion: the primary adaptation hurdle to overcome are differences in car sizes across geographic areas. A simple correction based on the average car size yields a strong correction of the adaptation gap. Our proposed method is simple and easily incorporated into most 3D object detection frameworks. It provides a first baseline for 3D object detection adaptation across countries, and gives hope that the underlying problem may be more within grasp than one may have hoped to believe. Our code is available at https://github. com/cxy1997/3D_adapt_auto_driving. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wang_Train_in_Germany_Test_in_the_USA_Making_3D_Object_CVPR_2020_paper.pdf | http://arxiv.org/abs/2005.08139 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Train_in_Germany_Test_in_the_USA_Making_3D_Object_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Train_in_Germany_Test_in_the_USA_Making_3D_Object_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Wang_Train_in_Germany_CVPR_2020_supplemental.pdf | null | null |
CARP: Compression Through Adaptive Recursive Partitioning for Multi-Dimensional Images | Rongjie Liu, Meng Li, Li Ma | Fast and effective image compression for multi-dimensional images has become increasingly important for efficient storage and transfer of massive amounts of high resolution images and videos. Desirable properties in compression methods include (1) high reconstruction quality at a wide range of compression rates while preserving key local details, (2) computational scalability, (3) applicability to a variety of different image/video types and of different dimensions, and (4) ease of tuning. We present such a method for multi-dimensional image compression called Compression via Adaptive Recursive Partitioning (CARP). CARP uses an optimal permutation of the image pixels inferred from a Bayesian probabilistic model on recursive partitions of the image to reduce its effective dimensionality, achieving a parsimonious representation that preserves information. CARP uses a multi-layer Bayesian hierarchical model to achieve self-tuning and regularization to avoid overfitting-- resulting in one single parameter to be specified by the user to achieve the desired compression rate. Extensive numerical experiments using a variety of datasets including 2D ImageNet, 3D medical image, and real-life YouTube and surveillance videos show that CARP dominates the state-of-the-art compression approaches-- including JPEG, JPEG2000, MPEG4, and a neural network-based method--for all of these different image types and often on nearly all of the individual images. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Liu_CARP_Compression_Through_Adaptive_Recursive_Partitioning_for_Multi-Dimensional_Images_CVPR_2020_paper.pdf | http://arxiv.org/abs/1912.05622 | https://www.youtube.com/watch?v=azcme-jLC8o | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Liu_CARP_Compression_Through_Adaptive_Recursive_Partitioning_for_Multi-Dimensional_Images_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Liu_CARP_Compression_Through_Adaptive_Recursive_Partitioning_for_Multi-Dimensional_Images_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Listen to Look: Action Recognition by Previewing Audio | Ruohan Gao, Tae-Hyun Oh, Kristen Grauman, Lorenzo Torresani | In the face of the video data deluge, today's expensive clip-level classifiers are increasingly impractical. We propose a framework for efficient action recognition in untrimmed video that uses audio as a preview mechanism to eliminate both short-term and long-term visual redundancies. First, we devise an ImgAud2Vid framework that hallucinates clip-level features by distilling from lighter modalities---a single frame and its accompanying audio---reducing short-term temporal redundancy for efficient clip-level recognition. Second, building on ImgAud2Vid, we further propose ImgAud-Skimming, an attention-based long short-term memory network that iteratively selects useful moments in untrimmed videos, reducing long-term temporal redundancy for efficient video-level recognition. Extensive experiments on four action recognition datasets demonstrate that our method achieves the state-of-the-art in terms of both recognition accuracy and speed. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Gao_Listen_to_Look_Action_Recognition_by_Previewing_Audio_CVPR_2020_paper.pdf | http://arxiv.org/abs/1912.04487 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Gao_Listen_to_Look_Action_Recognition_by_Previewing_Audio_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Gao_Listen_to_Look_Action_Recognition_by_Previewing_Audio_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Memory Enhanced Global-Local Aggregation for Video Object Detection | Yihong Chen, Yue Cao, Han Hu, Liwei Wang | How do humans recognize an object in a piece of video? Due to the deteriorated quality of single frame, it may be hard for people to identify an occluded object in this frame by just utilizing information within one image. We argue that there are two important cues for humans to recognize objects in videos: the global semantic information and the local localization information. Recently, plenty of methods adopt the self-attention mechanisms to enhance the features in key frame with either global semantic information or local localization information. In this paper we introduce memory enhanced global-local aggregation (MEGA) network, which is among the first trials that takes full consideration of both global and local information. Furthermore, empowered by a novel and carefully-designed Long Range Memory (LRM) module, our proposed MEGA could enable the key frame to get access to much more content than any previous methods. Enhanced by these two sources of information, our method achieves state-of-the-art performance on ImageNet VID dataset. Code is available at https://github.com/Scalsol/mega.pytorch. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Chen_Memory_Enhanced_Global-Local_Aggregation_for_Video_Object_Detection_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.12063 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Chen_Memory_Enhanced_Global-Local_Aggregation_for_Video_Object_Detection_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Chen_Memory_Enhanced_Global-Local_Aggregation_for_Video_Object_Detection_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Self-Training With Noisy Student Improves ImageNet Classification | Qizhe Xie, Minh-Thang Luong, Eduard Hovy, Quoc V. Le | We present a simple self-training method that achieves 88.4% top-1 accuracy on ImageNet, which is 2.0% better than the state-of-the-art model that requires 3.5B weakly labeled Instagram images. On robustness test sets, it improves ImageNet-A top-1 accuracy from 61.0% to 83.7%, reduces ImageNet-C mean corruption error from 45.7 to 28.3, and reduces ImageNet-P mean flip rate from 27.8 to 12.2. To achieve this result, we first train an EfficientNet model on labeled ImageNet images and use it as a teacher to generate pseudo labels on 300M unlabeled images. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. We iterate this process by putting back the student as the teacher. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. However, during the learning of the student, we inject noise such as dropout, stochastic depth and data augmentation via RandAugment to the student so that the student generalizes better than the teacher. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Xie_Self-Training_With_Noisy_Student_Improves_ImageNet_Classification_CVPR_2020_paper.pdf | http://arxiv.org/abs/1911.04252 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Xie_Self-Training_With_Noisy_Student_Improves_ImageNet_Classification_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Xie_Self-Training_With_Noisy_Student_Improves_ImageNet_Classification_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Xie_Self-Training_With_Noisy_CVPR_2020_supplemental.pdf | null | null |
Distilling Cross-Task Knowledge via Relationship Matching | Han-Jia Ye, Su Lu, De-Chuan Zhan | The discriminative knowledge from a high-capacity deep neural network (a.k.a. the "teacher") could be distilled to facilitate the learning efficacy of a shallow counterpart (a.k.a. the "student"). This paper deals with a general scenario reusing the knowledge from a cross-task teacher --- two models are targeting non-overlapping label spaces. We emphasize that the comparison ability between instances acts as an essential factor threading knowledge across domains, and propose the RElationship FacIlitated Local cLassifiEr Distillation (ReFilled) approach, which decomposes the knowledge distillation flow into branches for embedding and the top-layer classifier. In particular, different from reconciling the instance-label confidence between models, ReFilled requires the teacher to reweight the hard triplets push forwarded by the student so that the similarity comparison levels between instances are matched. A local embedding-induced classifier from the teacher further supervises the student's classification confidence. ReFilled demonstrates its effectiveness when reusing cross-task models, and also achieves state-of-the-art performance on the standard knowledge distillation benchmarks. The code of the paper can be accessed at https://github.com/njulus/ReFilled. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Ye_Distilling_Cross-Task_Knowledge_via_Relationship_Matching_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Ye_Distilling_Cross-Task_Knowledge_via_Relationship_Matching_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Ye_Distilling_Cross-Task_Knowledge_via_Relationship_Matching_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Ye_Distilling_Cross-Task_Knowledge_CVPR_2020_supplemental.pdf | null | null |
Multi-Modal Graph Neural Network for Joint Reasoning on Vision and Scene Text | Difei Gao, Ke Li, Ruiping Wang, Shiguang Shan, Xilin Chen | Answering questions that require reading texts in an image is challenging for current models. One key difficulty of this task is that rare, polysemous, and ambiguous words frequently appear in images, e.g., names of places, products, and sports teams. To overcome this difficulty, only resorting to pre-trained word embedding models is far from enough. A desired model should utilize the rich information in multiple modalities of the image to help understand the meaning of scene texts, e.g., the prominent text on a bottle is most likely to be the brand. Following this idea, we propose a novel VQA approach, Multi-Modal Graph Neural Network (MM-GNN). It first represents an image as a graph consisting of three sub-graphs, depicting visual, semantic, and numeric modalities respectively. Then, we introduce three aggregators which guide the message passing from one graph to another to utilize the contexts in various modalities, so as to refine the features of nodes. The updated nodes have better features for the downstream question answering module. Experimental evaluations show that our MM-GNN represents the scene texts better and obviously facilitates the performances on two VQA tasks that require reading scene texts. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Gao_Multi-Modal_Graph_Neural_Network_for_Joint_Reasoning_on_Vision_and_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.13962 | https://www.youtube.com/watch?v=Sw1s8LWg1ss | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Gao_Multi-Modal_Graph_Neural_Network_for_Joint_Reasoning_on_Vision_and_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Gao_Multi-Modal_Graph_Neural_Network_for_Joint_Reasoning_on_Vision_and_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Gao_Multi-Modal_Graph_Neural_CVPR_2020_supplemental.pdf | null | null |
Detecting Adversarial Samples Using Influence Functions and Nearest Neighbors | Gilad Cohen, Guillermo Sapiro, Raja Giryes | Deep neural networks (DNNs) are notorious for their vulnerability to adversarial attacks, which are small perturbations added to their input images to mislead their prediction. Detection of adversarial examples is, therefore, a fundamental requirement for robust classification frameworks. In this work, we present a method for detecting such adversarial attacks, which is suitable for any pre-trained neural network classifier. We use influence functions to measure the impact of every training sample on the validation set data. From the influence scores, we find the most supportive training samples for any given validation example. A k-nearest neighbor (k-NN) model fitted on the DNN's activation layers is employed to search for the ranking of these supporting training samples. We observe that these samples are highly correlated with the nearest neighbors of the normal inputs, while this correlation is much weaker for adversarial inputs. We train an adversarial detector using the k-NN ranks and distances and show that it successfully distinguishes adversarial examples, getting state-of-the-art results on six attack methods with three datasets. Code is available at https://github.com/giladcohen/NNIF_adv_defense. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Cohen_Detecting_Adversarial_Samples_Using_Influence_Functions_and_Nearest_Neighbors_CVPR_2020_paper.pdf | http://arxiv.org/abs/1909.06872 | https://www.youtube.com/watch?v=2zSDXllgr9c | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Cohen_Detecting_Adversarial_Samples_Using_Influence_Functions_and_Nearest_Neighbors_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Cohen_Detecting_Adversarial_Samples_Using_Influence_Functions_and_Nearest_Neighbors_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Cohen_Detecting_Adversarial_Samples_CVPR_2020_supplemental.pdf | null | null |
LiDAR-Based Online 3D Video Object Detection With Graph-Based Message Passing and Spatiotemporal Transformer Attention | Junbo Yin, Jianbing Shen, Chenye Guan, Dingfu Zhou, Ruigang Yang | Existing LiDAR-based 3D object detectors usually focus on the single-frame detection, while ignoring the spatiotemporal information in consecutive point cloud frames. In this paper, we propose an end-to-end online 3D video object detector that operates on point cloud sequences. The proposed model comprises a spatial feature encoding component and a spatiotemporal feature aggregation component. In the former component, a novel Pillar Message Passing Network (PMPNet) is proposed to encode each discrete point cloud frame. It adaptively collects information for a pillar node from its neighbors by iterative message passing, which effectively enlarges the receptive field of the pillar feature. In the latter component, we propose an Attentive Spatiotemporal Transformer GRU (AST-GRU) to aggregate the spatiotemporal information, which enhances the conventional ConvGRU with an attentive memory gating mechanism. AST-GRU contains a Spatial Transformer Attention (STA) module and a Temporal Transformer Attention (TTA) module, which can emphasize the foreground objects and align the dynamic objects, respectively. Experimental results demonstrate that the proposed 3D video object detector achieves state-of-the-art performance on the large-scale nuScenes benchmark. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Yin_LiDAR-Based_Online_3D_Video_Object_Detection_With_Graph-Based_Message_Passing_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.01389 | https://www.youtube.com/watch?v=dMm-mVKP7hg | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Yin_LiDAR-Based_Online_3D_Video_Object_Detection_With_Graph-Based_Message_Passing_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Yin_LiDAR-Based_Online_3D_Video_Object_Detection_With_Graph-Based_Message_Passing_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Iterative Context-Aware Graph Inference for Visual Dialog | Dan Guo, Hui Wang, Hanwang Zhang, Zheng-Jun Zha, Meng Wang | Visual dialog is a challenging task that requires the comprehension of the semantic dependencies among implicit visual and textual contexts. This task can refer to the relation inference in a graphical model with sparse contexts and unknown graph structure (relation descriptor), and how to model the underlying context-aware relation inference is critical. To this end, we propose a novel Context-Aware Graph (CAG) neural network. Each node in the graph corresponds to a joint semantic feature, including both object-based (visual) and history-related (textual) context representations. The graph structure (relations in dialog) is iteratively updated using an adaptive top-K message passing mechanism. Specifically, in every message passing step, each node selects the most K relevant nodes, and only receives messages from them. Then, after the update, we impose graph attention on all the nodes to get the final graph embedding and infer the answer. In CAG, each node has dynamic relations in the graph (different related K neighbor nodes), and only the most relevant nodes are attributive to the context-aware relational graph inference. Experimental results on VisDial v0.9 and v1.0 datasets show that CAG outperforms comparative methods. Visualization results further validate the interpretability of our method. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Guo_Iterative_Context-Aware_Graph_Inference_for_Visual_Dialog_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.02194 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Guo_Iterative_Context-Aware_Graph_Inference_for_Visual_Dialog_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Guo_Iterative_Context-Aware_Graph_Inference_for_Visual_Dialog_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Guo_Iterative_Context-Aware_Graph_CVPR_2020_supplemental.pdf | null | null |
Unsupervised Person Re-Identification via Multi-Label Classification | Dongkai Wang, Shiliang Zhang | The challenge of unsupervised person re-identification (ReID) lies in learning discriminative features without true labels. This paper formulates unsupervised person ReID as a multi-label classification task to progressively seek true labels. Our method starts by assigning each person image with a single-class label, then evolves to multi-label classification by leveraging the updated ReID model for label prediction. The label prediction comprises similarity computation and cycle consistency to ensure the quality of predicted labels. To boost the ReID model training efficiency in multi-label classification, we further propose the memory-based multi-label classification loss (MMCL). MMCL works with memory-based non-parametric classifier and integrates multi-label classification and single-label classification in an unified framework. Our label prediction and MMCL work iteratively and substantially boost the ReID performance. Experiments on several large-scale person ReID datasets demonstrate the superiority of our method in unsupervised person ReID. Our method also allows to use labeled person images in other domains. Under this transfer learning setting, our method also achieves state-of-the-art performance. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wang_Unsupervised_Person_Re-Identification_via_Multi-Label_Classification_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.09228 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Unsupervised_Person_Re-Identification_via_Multi-Label_Classification_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Unsupervised_Person_Re-Identification_via_Multi-Label_Classification_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Hit-Detector: Hierarchical Trinity Architecture Search for Object Detection | Jianyuan Guo, Kai Han, Yunhe Wang, Chao Zhang, Zhaohui Yang, Han Wu, Xinghao Chen, Chang Xu | Neural Architecture Search (NAS) has achieved great success in image classification task. Some recent works have managed to explore the automatic design of efficient backbone or feature fusion layer for object detection. However, these methods focus on searching only one certain component of object detector while leaving others manually designed. We identify the inconsistency between searched component and manually designed ones would withhold the detector of stronger performance. To this end, we propose a hierarchical trinity search framework to simultaneously discover efficient architectures for all components (i.e. backbone, neck, and head) of object detector in an end-to-end manner. In addition, we empirically reveal that different parts of the detector prefer different operators. Motivated by this, we employ a novel scheme to automatically screen different sub search spaces for different components so as to perform the end-to-end search for each component on the corresponding sub search space efficiently. Without bells and whistles, our searched architecture, namely Hit-Detector, achieves 41.4% mAP on COCO minival set with 27M parameters. Our implementation is available at \href https://github.com/ggjy/HitDet.pytorch https://github.com/ggjy/HitDet.pytorch . | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Guo_Hit-Detector_Hierarchical_Trinity_Architecture_Search_for_Object_Detection_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Guo_Hit-Detector_Hierarchical_Trinity_Architecture_Search_for_Object_Detection_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Guo_Hit-Detector_Hierarchical_Trinity_Architecture_Search_for_Object_Detection_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Guo_Hit-Detector_Hierarchical_Trinity_CVPR_2020_supplemental.pdf | null | null |
Visual-Semantic Matching by Exploring High-Order Attention and Distraction | Yongzhi Li, Duo Zhang, Yadong Mu | Cross-modality semantic matching is a vital task in computer vision and has attracted increasing attention in recent years. Existing methods mainly explore object-based alignment between image objects and text words. In this work, we address this task from two previously-ignored aspects: high-order semantic information (e.g., object-predicate-subject triplet, object-attribute pair) and visual distraction (i.e., despite the high relevance to textual query, images may also contain many prominent distracting objects or visual relations). Specifically, we build scene graphs for both visual and textual modalities. Our technical contributions are two-folds: firstly, we formulate the visual-semantic matching task as an attention-driven cross-modality scene graph matching problem. Graph convolutional networks (GCNs) are used to extract high-order information from two scene graphs. A novel cross-graph attention mechanism is proposed to contextually reweigh graph elements and calculate the inter-graph similarity; Secondly, some top-ranked samples are indeed false matching due to the co-occurrence of both highly-relevant and distracting information. We devise an information-theoretic measure for estimating semantic distraction and re-ranking the initial retrieval results. Comprehensive experiments and ablation studies on two large public datasets (MS-COCO and Flickr30K) demonstrate the superiority of the proposed method and the effectiveness of both high-order attention and distraction. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Li_Visual-Semantic_Matching_by_Exploring_High-Order_Attention_and_Distraction_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Visual-Semantic_Matching_by_Exploring_High-Order_Attention_and_Distraction_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Visual-Semantic_Matching_by_Exploring_High-Order_Attention_and_Distraction_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Disparity-Aware Domain Adaptation in Stereo Image Restoration | Bo Yan, Chenxi Ma, Bahetiyaer Bare, Weimin Tan, Steven C. H. Hoi | Under stereo settings, the problems of disparity estimation, stereo magnification and stereo-view synthesis have gathered wide attention. However, the limited image quality brings non-negligible difficulties in developing related applications and becomes the main bottleneck of stereo images. To the best of our knowledge, stereo image restoration is rarely studied. Towards this end, this paper analyses how to effectively explore disparity information, and proposes a unified stereo image restoration framework. The proposed framework explicitly learn the inherent pixel correspondence between stereo views and restores stereo image with the cross-view information at image and feature level. A Feature Modulation Dense Block (FMDB) is introduced to insert disparity prior throughout the whole network. The experiments in terms of efficiency, objective and perceptual quality, and the accuracy of depth estimation demonstrates the superiority of the proposed framework on various stereo image restoration tasks. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Yan_Disparity-Aware_Domain_Adaptation_in_Stereo_Image_Restoration_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Yan_Disparity-Aware_Domain_Adaptation_in_Stereo_Image_Restoration_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Yan_Disparity-Aware_Domain_Adaptation_in_Stereo_Image_Restoration_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Yan_Disparity-Aware_Domain_Adaptation_CVPR_2020_supplemental.pdf | null | null |
Assessing Eye Aesthetics for Automatic Multi-Reference Eye In-Painting | Bo Yan, Qing Lin, Weimin Tan, Shili Zhou | With the wide use of artistic images, aesthetic quality assessment has been widely concerned. How to integrate aesthetics into image editing is still a problem worthy of discussion. In this paper, aesthetic assessment is introduced into eye in-painting task for the first time. We construct an eye aesthetic dataset, and train the eye aesthetic assessment network on this basis. Then we propose a novel eye aesthetic and face semantic guided multi-reference eye inpainting GAN approach (AesGAN), which automatically selects the best reference under the guidance of eye aesthetics. A new aesthetic loss has also been introduced into the network to learn the eye aesthetic features and generate highquality eyes. We prove the effectiveness of eye aesthetic assessment in our experiments, which may inspire more applications of aesthetics assessment. Both qualitative and quantitative experimental results show that the proposed AesGAN can produce more natural and visually attractive eyes compared with state-of-the-art methods. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Yan_Assessing_Eye_Aesthetics_for_Automatic_Multi-Reference_Eye_In-Painting_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Yan_Assessing_Eye_Aesthetics_for_Automatic_Multi-Reference_Eye_In-Painting_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Yan_Assessing_Eye_Aesthetics_for_Automatic_Multi-Reference_Eye_In-Painting_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Yan_Assessing_Eye_Aesthetics_CVPR_2020_supplemental.pdf | null | null |
Equalization Loss for Long-Tailed Object Recognition | Jingru Tan, Changbao Wang, Buyu Li, Quanquan Li, Wanli Ouyang, Changqing Yin, Junjie Yan | Object recognition techniques using convolutional neural networks (CNN) have achieved great success. However, state-of-the-art object detection methods still perform poorly on large vocabulary and long-tailed datasets, e.g. LVIS. In this work, we analyze this problem from a novel perspective: each positive sample of one category can be seen as a negative sample for other categories, making the tail categories receive more discouraging gradients. Based on it, we propose a simple but effective loss, named equalization loss, to tackle the problem of long-tailed rare categories by simply ignoring those gradients for rare categories. The equalization loss protects the learning of rare categories from being at a disadvantage during the network parameter updating. Thus the model is capable of learning better discriminative features for objects of rare classes. Without any bells and whistles, our method achieves AP gains of 4.1% and 4.8% for the rare and common categories on the challenging LVIS benchmark, compared to the Mask R-CNN baseline. With the utilization of the effective equalization loss, we finally won the 1st place in the LVIS Challenge 2019. Code has been made available at: https://github.com/tztztztztz/eql.detectron2 | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Tan_Equalization_Loss_for_Long-Tailed_Object_Recognition_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.05176 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Tan_Equalization_Loss_for_Long-Tailed_Object_Recognition_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Tan_Equalization_Loss_for_Long-Tailed_Object_Recognition_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Tan_Equalization_Loss_for_CVPR_2020_supplemental.pdf | null | null |
Sideways: Depth-Parallel Training of Video Models | Mateusz Malinowski, Grzegorz Swirszcz, Joao Carreira, Viorica Patraucean | We propose Sideways, an approximate backpropagation scheme for training video models. In standard backpropagation, the gradients and activations at every computation step through the model are temporally synchronized. The forward activations need to be stored until the backward pass is executed, preventing inter-layer (depth) parallelization. However, can we leverage smooth, redundant input streams such as videos to develop a more efficient training scheme? Here, we explore an alternative to backpropagation; we overwrite network activations whenever new ones, i.e., from new frames, become available. Such a more gradual accumulation of information from both passes breaks the precise correspondence between gradients and activations, leading to theoretically more noisy weight updates. Counter-intuitively, we show that Sideways training of deep convolutional video networks not only still converges, but can also potentially exhibit better generalization compared to standard synchronized backpropagation. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Malinowski_Sideways_Depth-Parallel_Training_of_Video_Models_CVPR_2020_paper.pdf | http://arxiv.org/abs/2001.06232 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Malinowski_Sideways_Depth-Parallel_Training_of_Video_Models_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Malinowski_Sideways_Depth-Parallel_Training_of_Video_Models_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Malinowski_Sideways_Depth-Parallel_Training_CVPR_2020_supplemental.zip | null | null |
Hierarchical Conditional Relation Networks for Video Question Answering | Thao Minh Le, Vuong Le, Svetha Venkatesh, Truyen Tran | Video question answering (VideoQA) is challenging as it requires modeling capacity to distill dynamic visual artifacts and distant relations and to associate them with linguistic concepts. We introduce a general-purpose reusable neural unit called Conditional Relation Network (CRN) that serves as a building block to construct more sophisticated structures for representation and reasoning over video. CRN takes as input an array of tensorial objects and a conditioning feature, and computes an array of encoded output objects. Model building becomes a simple exercise of replication, rearrangement and stacking of these reusable units for diverse modalities and contextual information. This design thus supports high-order relational and multi-step reasoning. The resulting architecture for VideoQA is a CRN hierarchy whose branches represent sub-videos or clips, all sharing the same question as the contextual condition. Our evaluations on well-known datasets achieved new SoTA results, demonstrating the impact of building a general-purpose reasoning unit on complex domains such as VideoQA. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Le_Hierarchical_Conditional_Relation_Networks_for_Video_Question_Answering_CVPR_2020_paper.pdf | http://arxiv.org/abs/2002.10698 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Le_Hierarchical_Conditional_Relation_Networks_for_Video_Question_Answering_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Le_Hierarchical_Conditional_Relation_Networks_for_Video_Question_Answering_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Le_Hierarchical_Conditional_Relation_CVPR_2020_supplemental.pdf | null | null |
RankMI: A Mutual Information Maximizing Ranking Loss | Mete Kemertas, Leila Pishdad, Konstantinos G. Derpanis, Afsaneh Fazly | We introduce an information-theoretic loss function, RankMI, and an associated training algorithm for deep representation learning for image retrieval. Our proposed framework consists of alternating updates to a network that estimates the divergence between distance distributions of matching and non-matching pairs of learned embeddings, and an embedding network that maximizes this estimate via sampled negatives. In addition, under this information-theoretic lens we draw connections between RankMI and commonly-used ranking losses, e.g., triplet loss. We extensively evaluate RankMI on several standard image retrieval datasets, namely, CUB-200-2011, CARS-196, and Stanford Online Products. Our method achieves competitive results or significant improvements over previous reported results on all datasets. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Kemertas_RankMI_A_Mutual_Information_Maximizing_Ranking_Loss_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Kemertas_RankMI_A_Mutual_Information_Maximizing_Ranking_Loss_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Kemertas_RankMI_A_Mutual_Information_Maximizing_Ranking_Loss_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Kemertas_RankMI_A_Mutual_CVPR_2020_supplemental.pdf | null | null |
HAMBox: Delving Into Mining High-Quality Anchors on Face Detection | Yang Liu, Xu Tang, Junyu Han, Jingtuo Liu, Dinger Rui, Xiang Wu | Current face detectors utilize anchors to frame a multi-task learning problem which combines classification and bounding box regression. Effective anchor design and anchor matching strategy enable face detectors to localize faces under large pose and scale variations. However, we observe that, more than 80% correctly predicted bounding boxes are regressed from the unmatched anchors (the IoUs between anchors and target faces are lower than a threshold) in the inference phase. It indicates that these unmatched anchors perform excellent regression ability, but the existing methods neglect to learn from them. In this paper, we propose an Online High-quality Anchor Mining Strategy (HAMBox), which explicitly helps outer faces compensate with high-quality anchors. Our proposed HAMBox method could be a general strategy for anchor-based single-stage face detection. Experiments on various datasets, including WIDER FACE, FDDB, AFW and PASCAL Face, demonstrate the superiority of the proposed method. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Liu_HAMBox_Delving_Into_Mining_High-Quality_Anchors_on_Face_Detection_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Liu_HAMBox_Delving_Into_Mining_High-Quality_Anchors_on_Face_Detection_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Liu_HAMBox_Delving_Into_Mining_High-Quality_Anchors_on_Face_Detection_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
McFlow: Monte Carlo Flow Models for Data Imputation | Trevor W. Richardson, Wencheng Wu, Lei Lin, Beilei Xu, Edgar A. Bernal | We consider the topic of data imputation, a foundational task in machine learning that addresses issues with missing data. To that end, we propose MCFlow, a deep framework for imputation that leverages normalizing flow generative models and Monte Carlo sampling. We address the causality dilemma that arises when training models with incomplete data by introducing an iterative learning scheme which alternately updates the density estimate and the values of the missing entries in the training data. We provide extensive empirical validation of the effectiveness of the proposed method on standard multivariate and image datasets, and benchmark its performance against state-of-the-art alternatives. We demonstrate that MCFlow is superior to competing methods in terms of the quality of the imputed data, as well as with regards to its ability to preserve the semantic structure of the data. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Richardson_McFlow_Monte_Carlo_Flow_Models_for_Data_Imputation_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.12628 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Richardson_McFlow_Monte_Carlo_Flow_Models_for_Data_Imputation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Richardson_McFlow_Monte_Carlo_Flow_Models_for_Data_Imputation_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Richardson_McFlow_Monte_Carlo_CVPR_2020_supplemental.pdf | null | null |
MonoPair: Monocular 3D Object Detection Using Pairwise Spatial Relationships | Yongjian Chen, Lei Tai, Kai Sun, Mingyang Li | Monocular 3D object detection is an essential component in autonomous driving while challenging to solve, especially for those occluded samples which are only partially visible. Most detectors consider each 3D object as an independent training target, inevitably resulting in a lack of useful information for occluded samples. To this end, we propose a novel method to improve the monocular 3D object detection by considering the relationship of paired samples. This allows us to encode spatial constraints for partially-occluded objects from their adjacent neighbors. Specifically, the proposed detector computes uncertainty-aware predictions for object locations and 3D distances for the adjacent object pairs, which are subsequently jointly optimized by nonlinear least squares. Finally, the one-stage uncertainty-aware prediction structure and the post-optimization module are dedicatedly integrated for ensuring the run-time efficiency. Experiments demonstrate that our method yields the best performance on KITTI 3D detection benchmark, by outperforming state-of-the-art competitors by wide margins, especially for the hard samples. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Chen_MonoPair_Monocular_3D_Object_Detection_Using_Pairwise_Spatial_Relationships_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.00504 | https://www.youtube.com/watch?v=o0RnEtkCFi0 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Chen_MonoPair_Monocular_3D_Object_Detection_Using_Pairwise_Spatial_Relationships_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Chen_MonoPair_Monocular_3D_Object_Detection_Using_Pairwise_Spatial_Relationships_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Chen_MonoPair_Monocular_3D_CVPR_2020_supplemental.pdf | null | null |
KeyPose: Multi-View 3D Labeling and Keypoint Estimation for Transparent Objects | Xingyu Liu, Rico Jonschkowski, Anelia Angelova, Kurt Konolige | Estimating the 3D pose of desktop objects is crucial for applications such as robotic manipulation. Many existing approaches to this problem require a depth map of the object for both training and prediction, which restricts them to opaque, lambertian objects that produce good returns in an RGBD sensor. In this paper we forgo using a depth sensor in favor of raw stereo input. We address two problems: first, we establish an easy method for capturing and labeling 3D keypoints on desktop objects with an RGB camera; and second, we develop a deep neural network, called KeyPose, that learns to accurately predict object poses using 3D keypoints, from stereo input, and works even for transparent objects. To evaluate the performance of our method, we create a dataset of 15 clear objects in five classes, with 48K 3D-keypoint labeled images. We train both instance and category models, and show generalization to new textures, poses, and objects. KeyPose surpasses state-of-the-art performance in 3D pose estimation on this dataset by factors of 1.5 to 3.5, even in cases where the competing method is provided with ground-truth depth. Stereo input is essential for this performance as it improves results compared to using monocular input by a factor of 2. We will release a public version of the data capture and labeling pipeline, the transparent object database, and the KeyPose models and evaluation code. Project website: https://sites.google.com/corp/view/keypose. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Liu_KeyPose_Multi-View_3D_Labeling_and_Keypoint_Estimation_for_Transparent_Objects_CVPR_2020_paper.pdf | http://arxiv.org/abs/1912.02805 | https://www.youtube.com/watch?v=DBY4gycGzXM | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Liu_KeyPose_Multi-View_3D_Labeling_and_Keypoint_Estimation_for_Transparent_Objects_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Liu_KeyPose_Multi-View_3D_Labeling_and_Keypoint_Estimation_for_Transparent_Objects_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Liu_KeyPose_Multi-View_3D_CVPR_2020_supplemental.zip | null | null |
Putting Visual Object Recognition in Context | Mengmi Zhang, Claire Tseng, Gabriel Kreiman | Context plays an important role in visual recognition. Recent studies have shown that visual recognition networks can be fooled by placing objects in inconsistent contexts (e.g., a cow in the ocean). To model the role of contextual information in visual recognition, we systematically investigated ten critical properties of where, when, and how context modulates recognition, including the amount of context, context and object resolution, geometrical structure of context, context congruence, and temporal dynamics of contextual modulation. The tasks involved recognizing a target object surrounded with context in a natural image. As an essential benchmark, we conducted a series of psychophysics experiments where we altered one aspect of context at a time, and quantified recognition accuracy. We propose a biologically-inspired context-aware object recognition model consisting of a two-stream architecture. The model processes visual information at the fovea and periphery in parallel, dynamically incorporates object and contextual information, and sequentially reasons about the class label for the target object. Across a wide range of behavioral tasks, the model approximates human level performance without retraining for each task, captures the dependence of context enhancement on image properties, and provides initial steps towards integrating scene and object information for visual recognition. All source code and data are publicly available: https://github.com/kreimanlab/Put-In-Context. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zhang_Putting_Visual_Object_Recognition_in_Context_CVPR_2020_paper.pdf | http://arxiv.org/abs/1911.07349 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_Putting_Visual_Object_Recognition_in_Context_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_Putting_Visual_Object_Recognition_in_Context_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Zhang_Putting_Visual_Object_CVPR_2020_supplemental.pdf | https://cove.thecvf.com/datasets/324 | null |
Multi-Path Learning for Object Pose Estimation Across Domains | Martin Sundermeyer, Maximilian Durner, En Yen Puang, Zoltan-Csaba Marton, Narunas Vaskevicius, Kai O. Arras, Rudolph Triebel | We introduce a scalable approach for object pose estimation trained on simulated RGB views of multiple 3D models together. We learn an encoding of object views that does not only describe an implicit orientation of all objects seen during training, but can also relate views of untrained objects. Our single-encoder-multi-decoder network is trained using a technique we denote "multi-path learning": While the encoder is shared by all objects, each decoder only reconstructs views of a single object. Consequently, views of different instances do not have to be separated in the latent space and can share common features. The resulting encoder generalizes well from synthetic to real data and across various instances, categories, model types and datasets. We systematically investigate the learned encodings, their generalization, and iterative refinement strategies on the ModelNet40 and T-LESS dataset. Despite training jointly on multiple objects, our 6D Object Detection pipeline achieves state-of-the-art results on T-LESS at much lower runtimes than competing approaches. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Sundermeyer_Multi-Path_Learning_for_Object_Pose_Estimation_Across_Domains_CVPR_2020_paper.pdf | http://arxiv.org/abs/1908.00151 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Sundermeyer_Multi-Path_Learning_for_Object_Pose_Estimation_Across_Domains_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Sundermeyer_Multi-Path_Learning_for_Object_Pose_Estimation_Across_Domains_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Sundermeyer_Multi-Path_Learning_for_CVPR_2020_supplemental.zip | null | null |
Instance Credibility Inference for Few-Shot Learning | Yikai Wang, Chengming Xu, Chen Liu, Li Zhang, Yanwei Fu | Few-shot learning (FSL) aims to recognize new objects with extremely limited training data for each category. Previous efforts are made by either leveraging meta-learning paradigm or novel principles in data augmentation to alleviate this extremely data-scarce problem. In contrast, this paper presents a simple statistical approach, dubbed Instance Credibility Inference (ICI) to exploit the distribution support of unlabeled instances for few-shot learning. Specifically, we first train a linear classifier with the labeled few-shot examples and use it to infer the pseudo-labels for the unlabeled data. To measure the credibility of each pseudo-labeled instance, we then propose to solve another linear regression hypothesis by increasing the sparsity of the incidental parameters and rank the pseudo-labeled instances with their sparsity degree. We select the most trustworthy pseudo-labeled instances alongside the labeled examples to re-train the linear classifier. This process is iterated until all the unlabeled samples are included in the expanded training set, i.e. the pseudo-label is converged for unlabeled data pool. Extensive experiments under two few-shot settings show that our simple approach can establish new state-of-the-arts on four widely used few-shot learning benchmark datasets including miniImageNet, tieredImageNet, CIFAR-FS, and CUB. Our code is available at: https://github.com/Yikai-Wang/ICI-FSL | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wang_Instance_Credibility_Inference_for_Few-Shot_Learning_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.11853 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Instance_Credibility_Inference_for_Few-Shot_Learning_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Instance_Credibility_Inference_for_Few-Shot_Learning_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
From Paris to Berlin: Discovering Fashion Style Influences Around the World | Ziad Al-Halah, Kristen Grauman | The evolution of clothing styles and their migration across the world is intriguing, yet difficult to describe quantitatively. We propose to discover and quantify fashion influences from everyday images of people wearing clothes. We introduce an approach that detects which cities influence which other cities in terms of propagating their styles. We then leverage the discovered influence patterns to inform a forecasting model that predicts the popularity of any given style at any given city into the future. Demonstrating our idea with GeoStyle--a large-scale dataset of 7.7M images covering 44 major world cities, we present the discovered influence relationships, revealing how cities exert and receive fashion influence for an array of 50 observed visual styles. Furthermore, the proposed forecasting model achieves state-of-the-art results for a challenging style forecasting task, showing the advantage of grounding visual style evolution both spatially and temporally. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Al-Halah_From_Paris_to_Berlin_Discovering_Fashion_Style_Influences_Around_the_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=cy_Bx82P-b0 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Al-Halah_From_Paris_to_Berlin_Discovering_Fashion_Style_Influences_Around_the_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Al-Halah_From_Paris_to_Berlin_Discovering_Fashion_Style_Influences_Around_the_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Al-Halah_From_Paris_to_CVPR_2020_supplemental.pdf | null | null |
Severity-Aware Semantic Segmentation With Reinforced Wasserstein Training | Xiaofeng Liu, Wenxuan Ji, Jane You, Georges El Fakhri, Jonghye Woo | Semantic segmentation is a class of methods to classify each pixel in an image into semantic classes, which is critical for autonomous vehicles and surgery systems. Cross-entropy (CE) loss-based deep neural networks (DNN) achieved great success w.r.t. the accuracy-based metrics, e.g., mean Intersection-over Union. However, the CE loss has a limitation in that it ignores varying degrees of severity of pair-wise misclassified results. For instance, classifying a car into the road is much more terrible than recognizing it as a bus. To sidestep this, in this work, we propose to incorporate the severity-aware inter-class correlation into our Wasserstein training framework by configuring its ground distance matrix. In addition, our method can adaptively learn the ground metric in a high-fidelity simulator, following a reinforcement alternative optimization scheme. We evaluate our method using the CARLA simulator with the Deeplab backbone, demonstraing that our method significantly improves the survival time in the CARLA simulator. In addition, our method can be readily applied to existing DNN architectures and algorithms while yielding superior performance. We report results from experiments carried out with the CamVid and Cityscapes datasets. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Liu_Severity-Aware_Semantic_Segmentation_With_Reinforced_Wasserstein_Training_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Liu_Severity-Aware_Semantic_Segmentation_With_Reinforced_Wasserstein_Training_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Liu_Severity-Aware_Semantic_Segmentation_With_Reinforced_Wasserstein_Training_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Sketchformer: Transformer-Based Representation for Sketched Structure | Leo Sampaio Ferraz Ribeiro, Tu Bui, John Collomosse, Moacir Ponti | Sketchformer is a novel transformer-based representation for encoding free-hand sketches input in a vector form, i.e. as a sequence of strokes. Sketchformer effectively addresses multiple tasks: sketch classification, sketch based image retrieval (SBIR), and the reconstruction and interpolation of sketches. We report several variants exploring continuous and tokenized input representations, and contrast their performance. Our learned embedding, driven by a dictionary learning tokenization scheme, yields state of the art performance in classification and image retrieval tasks, when compared against baseline representations driven by LSTM sequence to sequence architectures: SketchRNN and derivatives. We show that sketch reconstruction and interpolation are improved significantly by the Sketchformer embedding for complex sketches with longer stroke sequences. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Ribeiro_Sketchformer_Transformer-Based_Representation_for_Sketched_Structure_CVPR_2020_paper.pdf | http://arxiv.org/abs/2002.10381 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Ribeiro_Sketchformer_Transformer-Based_Representation_for_Sketched_Structure_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Ribeiro_Sketchformer_Transformer-Based_Representation_for_Sketched_Structure_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Detail-recovery Image Deraining via Context Aggregation Networks | Sen Deng, Mingqiang Wei, Jun Wang, Yidan Feng, Luming Liang, Haoran Xie, Fu Lee Wang, Meng Wang | This paper looks at this intriguing question: are single images with their details lost during deraining, reversible to their artifact-free status? We propose an end-to-end detail-recovery image deraining network (termed a DRDNet) to solve the problem. Unlike existing image deraining approaches that attempt to meet the conflicting goal of simultaneously deraining and preserving details in a unified framework, we propose to view rain removal and detail recovery as two seperate tasks, so that each part could specialize rather than trade-off between two conflicting goals. Specifically, we introduce two parallel sub-networks with a comprehensive loss function which synergize to derain and recover the lost details caused by deraining. For complete rain removal, we present a rain residual network with the squeeze-and-excitation (SE) operation to remove rain streaks from the rainy images. For detail recovery, we construct a specialized detail repair network consisting of welldesigned blocks, named structure detail context aggregation block (SDCAB), to encourage the lost details to return for eliminating image degradations. Moreover, the detail recovery branch of our proposed detail repair framework is detachable and can be incorporated into existing deraining methods to boost their performances. DRD-Net has been validated on several well-known benchmark datasets in terms of deraining robustness and detail accuracy. Comparisons show clear visual and numerical improvements of our method over the state-of-the-arts. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Deng_Detail-recovery_Image_Deraining_via_Context_Aggregation_Networks_CVPR_2020_paper.pdf | http://arxiv.org/abs/1908.10267 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Deng_Detail-recovery_Image_Deraining_via_Context_Aggregation_Networks_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Deng_Detail-recovery_Image_Deraining_via_Context_Aggregation_Networks_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Deng_Detail-recovery_Image_Deraining_CVPR_2020_supplemental.pdf | null | null |
Dynamic Refinement Network for Oriented and Densely Packed Object Detection | Xingjia Pan, Yuqiang Ren, Kekai Sheng, Weiming Dong, Haolei Yuan, Xiaowei Guo, Chongyang Ma, Changsheng Xu | Object detection has achieved remarkable progress in the past decade. However, the detection of oriented and densely packed objects remains challenging because of following inherent reasons: (1) receptive fields of neurons are all axis-aligned and of the same shape, whereas objects are usually of diverse shapes and align along various directions; (2) detection models are typically trained with generic knowledge and may not generalize well to handle specific objects at test time; (3) the limited dataset hinders the development on this task. To resolve the first two issues, we present a dynamic refinement network that consists of two novel components, i.e., a feature selection module (FSM) and a dynamic refinement head (DRH). Our FSM enables neurons to adjust receptive fields in accordance with the shapes and orientations of target objects, whereas the DRH empowers our model to refine the prediction dynamically in an object-aware manner. To address the limited availability of related benchmarks, we collect an extensive and fully annotated dataset, namely, SKU110K-R, which is relabeled with oriented bounding boxes based on SKU110K. We perform quantitative evaluations on several publicly available benchmarks including DOTA, HRSC2016, SKU110K, and our own SKU110K-R dataset. Experimental results show that our method achieves consistent and substantial gains compared with baseline approaches. Our source code and dataset will be released to encourage follow-up research. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Pan_Dynamic_Refinement_Network_for_Oriented_and_Densely_Packed_Object_Detection_CVPR_2020_paper.pdf | http://arxiv.org/abs/2005.09973 | https://www.youtube.com/watch?v=HxV3ZxSjvwc | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Pan_Dynamic_Refinement_Network_for_Oriented_and_Densely_Packed_Object_Detection_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Pan_Dynamic_Refinement_Network_for_Oriented_and_Densely_Packed_Object_Detection_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Pan_Dynamic_Refinement_Network_CVPR_2020_supplemental.pdf | null | null |
Self-Trained Deep Ordinal Regression for End-to-End Video Anomaly Detection | Guansong Pang, Cheng Yan, Chunhua Shen, Anton van den Hengel, Xiao Bai | Video anomaly detection is of critical practical importance to a variety of real applications because it allows human attention to be focused on events that are likely to be of interest, in spite of an otherwise overwhelming volume of video. We show that applying self-trained deep ordinal regression to video anomaly detection overcomes two key limitations of existing methods, namely, 1) being highly dependent on manually labeled normal training data; and 2) sub-optimal feature learning. By formulating a surrogate two-class ordinal regression task we devise an end-to-end trainable video anomaly detection approach that enables joint representation learning and anomaly scoring without manually labeled normal/abnormal data. Experiments on eight real-world video scenes show that our proposed method outperforms state-of-the-art methods that require no labeled training data by a substantial margin, and enables easy and accurate localization of the identified anomalies. Furthermore, we demonstrate that our method offers effective human-in-the-loop anomaly detection which can be critical in applications where anomalies are rare and the false-negative cost is high. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Pang_Self-Trained_Deep_Ordinal_Regression_for_End-to-End_Video_Anomaly_Detection_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.06780 | https://www.youtube.com/watch?v=JISkqZ1IIYU | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Pang_Self-Trained_Deep_Ordinal_Regression_for_End-to-End_Video_Anomaly_Detection_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Pang_Self-Trained_Deep_Ordinal_Regression_for_End-to-End_Video_Anomaly_Detection_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Smoothing Adversarial Domain Attack and P-Memory Reconsolidation for Cross-Domain Person Re-Identification | Guangcong Wang, Jian-Huang Lai, Wenqi Liang, Guangrun Wang | Most of the existing person re-identification (re-ID) methods achieve promising accuracy in a supervised manner, but they assume the identity labels of the target domain is available. This greatly limits the scalability of person re-ID in real-world scenarios. Therefore, the current person re-ID community focuses on the cross-domain person re-ID that aims to transfer the knowledge from a labeled source domain to an unlabeled target domain and exploits the specific knowledge from the data distribution of the target domain to further improve the performance. To reduce the gap between the source and target domains, we propose a Smoothing Adversarial Domain Attack (SADA) approach that guides the source domain images to align the target domain images by using a trained camera classifier. To stabilize a memory trace of cross-domain knowledge transfer after its initial acquisition from the source domain, we propose a p-Memory Reconsolidation (pMR) method that reconsolidates the source knowledge with a small probability p during the self-training of the target domain. With both SADA and pMR, the proposed method significantly improves the cross-domain person re-ID. Extensive experiments on Market-1501 and DukeMTMC-reID benchmarks show that our pMR-SADA outperforms all of the state-of-the-arts by a large margin. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wang_Smoothing_Adversarial_Domain_Attack_and_P-Memory_Reconsolidation_for_Cross-Domain_Person_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=DF8_ico4L8w | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Smoothing_Adversarial_Domain_Attack_and_P-Memory_Reconsolidation_for_Cross-Domain_Person_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Smoothing_Adversarial_Domain_Attack_and_P-Memory_Reconsolidation_for_Cross-Domain_Person_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Predicting Sharp and Accurate Occlusion Boundaries in Monocular Depth Estimation Using Displacement Fields | Michael Ramamonjisoa, Yuming Du, Vincent Lepetit | Current methods for depth map prediction from monocular images tend to predict smooth, poorly localized contours for the occlusion boundaries in the input image. This is unfortunate as occlusion boundaries are important cues to recognize objects, and as we show, may lead to a way to discover new objects from scene reconstruction. To improve predicted depth maps, recent methods rely on various forms of filtering or predict an additive residual depth map to refine a first estimate. We instead learn to predict, given a depth map predicted by some reconstruction method, a 2D displacement field able to re-sample pixels around the occlusion boundaries into sharper reconstructions. Our method can be applied to the output of any depth estimation method, in an end-to-end trainable fashion. For evaluation, we manually annotated the occlusion boundaries in all the images in the test split of popular NYUv2-Depth dataset. We show that our approach improves the localization of occlusion boundaries for all state-of-the-art monocular depth estimation methods that we could evaluate, without degrading the depth accuracy for the rest of the images. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Ramamonjisoa_Predicting_Sharp_and_Accurate_Occlusion_Boundaries_in_Monocular_Depth_Estimation_CVPR_2020_paper.pdf | http://arxiv.org/abs/2002.12730 | https://www.youtube.com/watch?v=A1_5RXBP_No | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Ramamonjisoa_Predicting_Sharp_and_Accurate_Occlusion_Boundaries_in_Monocular_Depth_Estimation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Ramamonjisoa_Predicting_Sharp_and_Accurate_Occlusion_Boundaries_in_Monocular_Depth_Estimation_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Ramamonjisoa_Predicting_Sharp_and_CVPR_2020_supplemental.pdf | https://cove.thecvf.com/datasets/344 | null |
Spatiotemporal Fusion in 3D CNNs: A Probabilistic View | Yizhou Zhou, Xiaoyan Sun, Chong Luo, Zheng-Jun Zha, Wenjun Zeng | Despite the success in still image recognition, deep neural networks for spatiotemporal signal tasks (such as human action recognition in videos) still suffers from low efficacy and inefficiency over the past years. Recently, human experts have put more efforts into analyzing the importance of different components in 3D convolutional neural networks (3D CNNs) to design more powerful spatiotemporal learning backbones. Among many others, spatiotemporal fusion is one of the essentials. It controls how spatial and temporal signals are extracted at each layer during inference. Previous attempts usually start by ad-hoc designs that empirically combine certain convolutions and then draw conclusions based on the performance obtained by training the corresponding networks. These methods only support network-level analysis on limited number of fusion strategies. In this paper, we propose to convert the spatiotemporal fusion strategies into a probability space, which allows us to perform network-level evaluations of various fusion strategies without having to train them separately. Besides, we can also obtain fine-grained numerical information such as layer-level preference on spatiotemporal fusion within the probability space. Our approach greatly boosts the efficiency of analyzing spatiotemporal fusion. Based on the probability space, we further generate new fusion strategies which achieve the state-of-the-art performance on four well-known action recognition datasets. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zhou_Spatiotemporal_Fusion_in_3D_CNNs_A_Probabilistic_View_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.04981 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhou_Spatiotemporal_Fusion_in_3D_CNNs_A_Probabilistic_View_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhou_Spatiotemporal_Fusion_in_3D_CNNs_A_Probabilistic_View_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Zhou_Spatiotemporal_Fusion_in_CVPR_2020_supplemental.pdf | null | null |
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