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Bipartite Graph Network With Adaptive Message Passing for Unbiased Scene Graph Generation | Rongjie Li, Songyang Zhang, Bo Wan, Xuming He | Scene graph generation is an important visual understanding task with a broad range of vision applications. Despite recent tremendous progress, it remains challenging due to the intrinsic long-tailed class distribution and large intra-class variation. To address these issues, we introduce a novel confidence-aware bipartite graph neural network with adaptive message propagation mechanism for unbiased scene graph generation. In addition, we propose an efficient bi-level data resampling strategy to alleviate the imbalanced data distribution problem in training our graph network. Our approach achieves superior or competitive performance over previous methods on several challenging datasets, including Visual Genome, Open Images V4/V6, demonstrating its effectiveness and generality. | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_Bipartite_Graph_Network_With_Adaptive_Message_Passing_for_Unbiased_Scene_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.00308 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Bipartite_Graph_Network_With_Adaptive_Message_Passing_for_Unbiased_Scene_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Bipartite_Graph_Network_With_Adaptive_Message_Passing_for_Unbiased_Scene_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Li_Bipartite_Graph_Network_CVPR_2021_supplemental.pdf | null |
Guided Interactive Video Object Segmentation Using Reliability-Based Attention Maps | Yuk Heo, Yeong Jun Koh, Chang-Su Kim | We propose a novel guided interactive segmentation (GIS) algorithm for video objects to improve the segmentation accuracy and reduce the interaction time. First, we design the reliability-based attention module to analyze the reliability of multiple annotated frames. Second, we develop the intersection-aware propagation module to propagate segmentation results to neighboring frames. Third, we introduce the GIS mechanism for a user to select unsatisfactory frames quickly with less effort. Experimental results demonstrate that the proposed algorithm provides more accurate segmentation results at a faster speed than conventional algorithms. Codes are available at https://github.com/yuk6heo/GIS-RAmap. | https://openaccess.thecvf.com/content/CVPR2021/papers/Heo_Guided_Interactive_Video_Object_Segmentation_Using_Reliability-Based_Attention_Maps_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.10386 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Heo_Guided_Interactive_Video_Object_Segmentation_Using_Reliability-Based_Attention_Maps_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Heo_Guided_Interactive_Video_Object_Segmentation_Using_Reliability-Based_Attention_Maps_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Heo_Guided_Interactive_Video_CVPR_2021_supplemental.zip | null |
Learning Spatial-Semantic Relationship for Facial Attribute Recognition With Limited Labeled Data | Ying Shu, Yan Yan, Si Chen, Jing-Hao Xue, Chunhua Shen, Hanzi Wang | Recent advances in deep learning have demonstrated excellent results for Facial Attribute Recognition (FAR), typically trained with large-scale labeled data. However, in many real-world FAR applications, only limited labeled data are available, leading to remarkable deterioration in performance for most existing deep learning-based FAR methods. To address this problem, here we propose a method termed Spatial-Semantic Patch Learning (SSPL). The training of SSPL involves two stages. First, three auxiliary tasks, consisting of a Patch Rotation Task (PRT), a Patch Segmentation Task (PST), and a Patch Classification Task (PCT), are jointly developed to learn the spatial-semantic relationship from large-scale unlabeled facial data. We thus obtain a powerful pre-trained model. In particular, PRT exploits the spatial information of facial images in a self-supervised learning manner. PST and PCT respectively capture the pixel-level and image-level semantic information of facial images based on a facial parsing model. Second, the spatial-semantic knowledge learned from auxiliary tasks is transferred to the FAR task. By doing so, it enables that only a limited number of labeled data are required to fine-tune the pre-trained model. We achieve superior performance compared with state-of-the-art methods, as substantiated by extensive experiments and studies. | https://openaccess.thecvf.com/content/CVPR2021/papers/Shu_Learning_Spatial-Semantic_Relationship_for_Facial_Attribute_Recognition_With_Limited_Labeled_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Shu_Learning_Spatial-Semantic_Relationship_for_Facial_Attribute_Recognition_With_Limited_Labeled_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Shu_Learning_Spatial-Semantic_Relationship_for_Facial_Attribute_Recognition_With_Limited_Labeled_CVPR_2021_paper.html | CVPR 2021 | null | null |
Decoupled Dynamic Filter Networks | Jingkai Zhou, Varun Jampani, Zhixiong Pi, Qiong Liu, Ming-Hsuan Yang | Convolution is one of the basic building blocks of CNN architectures. Despite its common use, standard convolution has two main shortcomings: Content-agnostic and Computation-heavy. Dynamic filters are content-adaptive, while further increasing the computational overhead. Depth-wise convolution is a lightweight variant, but it usually leads to a drop in CNN performance or requires a larger number of channels. In this work, we propose the Decoupled Dynamic Filter (DDF) that can simultaneously tackle both of these shortcomings. Inspired by recent advances in attention, DDF decouples a depth-wise dynamic filter into spatial and channel dynamic filters. This decomposition considerably reduces the number of parameters and limits computational costs to the same level as depth-wise convolution. Meanwhile, we observe a significant boost in performance when replacing standard convolution with DDF in classification networks. ResNet50 / 101 get improved by 1.9% and 1.3% on the top-1 accuracy, while their computational costs are reduced by nearly half. Experiments on the detection and joint upsampling networks also demonstrate the superior performance of the DDF upsampling variant (DDF-Up) in comparison with standard convolution and specialized content-adaptive layers. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhou_Decoupled_Dynamic_Filter_Networks_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.14107 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhou_Decoupled_Dynamic_Filter_Networks_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhou_Decoupled_Dynamic_Filter_Networks_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhou_Decoupled_Dynamic_Filter_CVPR_2021_supplemental.pdf | null |
Motion Representations for Articulated Animation | Aliaksandr Siarohin, Oliver J. Woodford, Jian Ren, Menglei Chai, Sergey Tulyakov | We propose novel motion representations for animating articulated objects consisting of distinct parts. In a completely unsupervised manner, our method identifies object parts, tracks them in a driving video, and infers their motions by considering their principal axes. In contrast to the previous keypoint-based works, our method extracts meaningful and consistent regions, describing locations, shape, and pose. The regions correspond to semantically relevant and distinct object parts, that are more easily detected in frames of the driving video. To force decoupling of foreground from background, we model non-object related global motion with an additional affine transformation. To facilitate animation and prevent the leakage of the shape of the driving object, we disentangle shape and pose of objects in the region space. Our model can animate a variety of objects, surpassing previous methods by a large margin on existing benchmarks. We present a challenging new benchmark with high-resolution videos and show that the improvement is particularly pronounced when articulated objects are considered, reaching 96.6% user preference vs. the state of the art. | https://openaccess.thecvf.com/content/CVPR2021/papers/Siarohin_Motion_Representations_for_Articulated_Animation_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.11280 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Siarohin_Motion_Representations_for_Articulated_Animation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Siarohin_Motion_Representations_for_Articulated_Animation_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Siarohin_Motion_Representations_for_CVPR_2021_supplemental.pdf | null |
General Multi-Label Image Classification With Transformers | Jack Lanchantin, Tianlu Wang, Vicente Ordonez, Yanjun Qi | Multi-label image classification is the task of predicting a set of labels corresponding to objects, attributes or other entities present in an image. In this work we propose the Classification Transformer (C-Tran), a general framework for multi-label image classification that leverages Transformers to exploit the complex dependencies among visual features and labels. Our approach consists of a Transformer encoder trained to predict a set of target labels given an input set of masked labels, and visual features from a convolutional neural network. A key ingredient of our method is a label mask training objective that uses a ternary encoding scheme to represent the state of the labels as positive, negative, or unknown during training. Our model shows state-of-the-art performance on challenging datasets such as COCO and Visual Genome. Moreover, because our model explicitly represents the label state during training, it is more general by allowing us to produce improved results for images with partial or extra label annotations during inference. We demonstrate this additional capability in the COCO, Visual Genome, News-500, and CUB image datasets. | https://openaccess.thecvf.com/content/CVPR2021/papers/Lanchantin_General_Multi-Label_Image_Classification_With_Transformers_CVPR_2021_paper.pdf | http://arxiv.org/abs/2011.14027 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Lanchantin_General_Multi-Label_Image_Classification_With_Transformers_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Lanchantin_General_Multi-Label_Image_Classification_With_Transformers_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Lanchantin_General_Multi-Label_Image_CVPR_2021_supplemental.pdf | null |
On Self-Contact and Human Pose | Lea Muller, Ahmed A. A. Osman, Siyu Tang, Chun-Hao P. Huang, Michael J. Black | People touch their face 23 times an hour, they cross their arms and legs, put their hands on their hips, etc. While many images of people contain some form of self-contact, current 3D human pose and shape (HPS) regression methods typically fail to estimate this contact. To address this, we develop new datasets and methods that significantly improve human pose estimation with self-contact. First, we create a dataset of 3D Contact Poses (3DCP) containing SMPL-X bodies fit to 3D scans as well as poses from AMASS, which we refine to ensure good contact. Second, we leverage this to create the Mimic-The-Pose (MTP) dataset of images, collected via Amazon Mechanical Turk, containing people mimicking the 3DCP poses with self-contact. Third, we develop a novel HPS optimization method, SMPLify-XMC, that includes contact constraints and uses the known 3DCP body pose during fitting to create near ground-truth poses for MTP images. Fourth, for more image variety, we label a dataset of in-the-wild images with Discrete Self-Contact (DSC) information and use another new optimization method, SMPLify-DC, that exploits discrete contacts during pose optimization. Finally, we use our datasets during SPIN training to learn a new 3D human pose regressor, called TUCH (Towards Understanding Contact in Humans). We show that the new self-contact training data significantly improves 3D human pose estimates on withheld test data and existing datasets like 3DPW. Not only does our method improve results for self-contact poses, but it also improves accuracy for non-contact poses. The code and data are available for research purposes at https://tuch.is.tue.mpg.de. | https://openaccess.thecvf.com/content/CVPR2021/papers/Muller_On_Self-Contact_and_Human_Pose_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.03176 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Muller_On_Self-Contact_and_Human_Pose_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Muller_On_Self-Contact_and_Human_Pose_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Muller_On_Self-Contact_and_CVPR_2021_supplemental.pdf | null |
Center-Based 3D Object Detection and Tracking | Tianwei Yin, Xingyi Zhou, Philipp Krahenbuhl | Three-dimensional objects are commonly represented as 3D boxes in a point-cloud. This representation mimics the well-studied image-based 2D bounding-box detection but comes with additional challenges. Objects in a 3D world do not follow any particular orientation, and box-based detectors have difficulties enumerating all orientations or fitting an axis-aligned bounding box to rotated objects. In this paper, we instead propose to represent, detect, and track 3D objects as points. Our framework, CenterPoint, first detects centers of objects using a keypoint detector and regresses to other attributes, including 3D size, 3D orientation, and velocity. In a second stage, it refines these estimates using additional point features on the object. In CenterPoint, 3D object tracking simplifies to greedy closest-point matching. The resulting detection and tracking algorithm is simple, efficient, and effective. On the nuScenes and Waymo datasets, CenterPoint surpasses prior methods by a large margin. On the Waymo Open Dataset, CenterPoint improves previous state-of-the-art by 10-20% while running at 13FPS. The code and pretrained models are available at https://github.com/tianweiy/CenterPoint. | https://openaccess.thecvf.com/content/CVPR2021/papers/Yin_Center-Based_3D_Object_Detection_and_Tracking_CVPR_2021_paper.pdf | http://arxiv.org/abs/2006.11275 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Yin_Center-Based_3D_Object_Detection_and_Tracking_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Yin_Center-Based_3D_Object_Detection_and_Tracking_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yin_Center-Based_3D_Object_CVPR_2021_supplemental.pdf | null |
Prototype Augmentation and Self-Supervision for Incremental Learning | Fei Zhu, Xu-Yao Zhang, Chuang Wang, Fei Yin, Cheng-Lin Liu | Despite the impressive performance in many individual tasks, deep neural networks suffer from catastrophic forgetting when learning new tasks incrementally. Recently, various incremental learning methods have been proposed, and some approaches achieved acceptable performance relying on stored data or complex generative models. However, storing data from previous tasks is limited by memory or privacy issues, and generative models are usually unstable and inefficient in training. In this paper, we propose a simple non-exemplar based method named PASS, to address the catastrophic forgetting problem in incremental learning. On the one hand, we propose to memorize one class-representative prototype for each old class and adopt prototype augmentation (protoAug) in the deep feature space to maintain the decision boundary of previous tasks. On the other hand, we employ self-supervised learning (SSL) to learn more generalizable and transferable features for other tasks, which demonstrates the effectiveness of SSL in incremental learning. Experimental results on benchmark datasets show that our approach significantly outperforms non-exemplar based methods, and achieves comparable performance compared to exemplar based approaches. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhu_Prototype_Augmentation_and_Self-Supervision_for_Incremental_Learning_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhu_Prototype_Augmentation_and_Self-Supervision_for_Incremental_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhu_Prototype_Augmentation_and_Self-Supervision_for_Incremental_Learning_CVPR_2021_paper.html | CVPR 2021 | null | null |
CompositeTasking: Understanding Images by Spatial Composition of Tasks | Nikola Popovic, Danda Pani Paudel, Thomas Probst, Guolei Sun, Luc Van Gool | We define the concept of CompositeTasking as the fusion of multiple, spatially distributed tasks, for various aspects of image understanding. Learning to perform spatially distributed tasks is motivated by the frequent availability of only sparse labels across tasks, and the desire for a compact multi-tasking network. To facilitate CompositeTasking, we introduce a novel task conditioning model -- a single encoder-decoder network that performs multiple, spatially varying tasks at once. The proposed network takes an image and a set of pixel-wise dense task requests as inputs, and performs the requested prediction task for each pixel. Moreover, we also learn the composition of tasks that needs to be performed according to some CompositeTasking rules, which includes the decision of where to apply which task. It not only offers us a compact network for multi-tasking, but also allows for task-editing. Another strength of the proposed method is demonstrated by only having to supply sparse supervision per task. The obtained results are on par with our baselines that use dense supervision and a multi-headed multi-tasking design. The source code will be made publicly available at www.github.com/nikola3794/composite-tasking. | https://openaccess.thecvf.com/content/CVPR2021/papers/Popovic_CompositeTasking_Understanding_Images_by_Spatial_Composition_of_Tasks_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.09030 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Popovic_CompositeTasking_Understanding_Images_by_Spatial_Composition_of_Tasks_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Popovic_CompositeTasking_Understanding_Images_by_Spatial_Composition_of_Tasks_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Popovic_CompositeTasking_Understanding_Images_CVPR_2021_supplemental.pdf | null |
Searching for Fast Model Families on Datacenter Accelerators | Sheng Li, Mingxing Tan, Ruoming Pang, Andrew Li, Liqun Cheng, Quoc V. Le, Norman P. Jouppi | Neural Architecture Search (NAS), together with model scaling, has shown remarkable progress in designing high accuracy and fast convolutional architecture families. However, as neither NAS nor model scaling considers sufficient hardware architecture details, they do not take full advantage of the emerging datacenter (DC) accelerators. In this paper, we search for fast and accurate CNN model families for efficient inference on DC accelerators. We first analyze DC accelerators and find that existing CNNs suffer from insufficient operational intensity, parallelism, and execution efficiency and exhibit FLOPs-latency nonproportionality. These insights let us create a DC-accelerator-optimized search space, with space-to-depth, space-to-batch, hybrid fused convolution structures with vanilla and depthwise convolutions, and block-wise activation functions. We further propose a latency-aware compound scaling (LACS), the first multi-objective compound scaling method optimizing both accuracy and latency. Our LACS discovers that network depth should grow much faster than image size and network width, which is quite different from the observations from previous compound scaling. With the new search space and LACS, our search and scaling on datacenter accelerators results in a new model series named EfficientNet-X. EfficientNet-X is up to more than 2X faster than EfficientNet (a model series with state-of-the-art trade-off on FLOPs and accuracy) on TPUv3 and GPUv100, with comparable accuracy. EfficientNet-X is also up to 7X faster than recent RegNet and ResNeSt on TPUv3 and GPUv100. Source code is at https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet/tpu | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_Searching_for_Fast_Model_Families_on_Datacenter_Accelerators_CVPR_2021_paper.pdf | http://arxiv.org/abs/2102.05610 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Searching_for_Fast_Model_Families_on_Datacenter_Accelerators_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Searching_for_Fast_Model_Families_on_Datacenter_Accelerators_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Li_Searching_for_Fast_CVPR_2021_supplemental.pdf | null |
Task-Aware Variational Adversarial Active Learning | Kwanyoung Kim, Dongwon Park, Kwang In Kim, Se Young Chun | Often, labeling large amount of data is challenging due to high labeling cost limiting the application domain of deep learning techniques. Active learning (AL) tackles this by querying the most informative samples to be annotated among unlabeled pool. Two promising directions for AL that have been recently explored are task-agnostic approach to select data points that are far from the current labeled pool and task-aware approach that relies on the perspective of task model. Unfortunately, the former does not exploit structures from tasks and the latter does not seem to well-utilize overall data distribution. Here, we propose task-aware variational adversarial AL (TA-VAAL) that modifies task-agnostic VAAL, that considered data distribution of both label and unlabeled pools, by relaxing task learning loss prediction to ranking loss prediction and by using ranking conditional generative adversarial network to embed normalized ranking loss information on VAAL. Our proposed TA-VAAL outperforms state-of-the-arts on various benchmark datasets for classifications with balanced / imbalanced labels as well as semantic segmentation and its task-aware and task-agnostic AL properties were confirmed with our in-depth analyses. | https://openaccess.thecvf.com/content/CVPR2021/papers/Kim_Task-Aware_Variational_Adversarial_Active_Learning_CVPR_2021_paper.pdf | http://arxiv.org/abs/2002.04709 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Kim_Task-Aware_Variational_Adversarial_Active_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Kim_Task-Aware_Variational_Adversarial_Active_Learning_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Kim_Task-Aware_Variational_Adversarial_CVPR_2021_supplemental.pdf | null |
Understanding and Simplifying Perceptual Distances | Dan Amir, Yair Weiss | Perceptual metrics based on features of deep Convolutional Neural Networks (CNNs) have shown remarkable success when used as loss functions in a range of computer vision problems and significantly outperform classical losses such as L1 or L2 in pixel space. The source of this success remains somewhat mysterious, especially since a good loss does not require a particular CNN architecture nor a particular training method. In this paper we show that similar success can be achieved even with losses based on features of a deep CNN with random filters. We use the tool of infinite CNNs to derive an analytical form for perceptual similarity in such CNNs, and prove that the perceptual distance between two images is equivalent to the maximum mean discrepancy (MMD) distance between local distributions of small patches in the two images. We use this equivalence to propose a simple metric for comparing two images which directly computes the MMD between local distributions of patches in the two images. Our proposed metric is simple to understand, requires no deep networks, and gives comparable performance to perceptual metrics in a range of computer vision tasks. | https://openaccess.thecvf.com/content/CVPR2021/papers/Amir_Understanding_and_Simplifying_Perceptual_Distances_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Amir_Understanding_and_Simplifying_Perceptual_Distances_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Amir_Understanding_and_Simplifying_Perceptual_Distances_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Amir_Understanding_and_Simplifying_CVPR_2021_supplemental.pdf | null |
Class-Aware Robust Adversarial Training for Object Detection | Pin-Chun Chen, Bo-Han Kung, Jun-Cheng Chen | Object detection is an important computer vision task with plenty of real-world applications; therefore, how to enhance its robustness against adversarial attacks has emerged as a crucial issue. However, most of the previous defense methods focused on the classification task and had few analysis in the context of the object detection task. In this work, to address the issue, we present a novel class-aware robust adversarial training paradigm for the object detection task. For a given image, the proposed approach generates a universal adversarial perturbation to simultaneously attack all the occurred objects in the image through jointly maximizing the respective loss for each object. Meanwhile, instead of normalizing the total loss with the number of objects, the proposed approach decomposes the total loss into class-wise losses and normalizes each class loss using the number of objects for the class. The adversarial training based on the class weighted loss can not only balances the influence of each class but also effectively and evenly improves the adversarial robustness of trained models for all the object classes as compared with the previous defense methods. Furthermore, with the recent development of fast adversarial training, we provide a fast version of the proposed algorithm which can be trained faster than the traditional adversarial training while keeping comparable performance. With extensive experiments on the challenging PASCAL-VOC and MS-COCO datasets, the evaluation results demonstrate that the proposed defense methods can effectively enhance the robustness of the object detection models. | https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_Class-Aware_Robust_Adversarial_Training_for_Object_Detection_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.16148 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Class-Aware_Robust_Adversarial_Training_for_Object_Detection_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Class-Aware_Robust_Adversarial_Training_for_Object_Detection_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Chen_Class-Aware_Robust_Adversarial_CVPR_2021_supplemental.pdf | null |
Bayesian Nested Neural Networks for Uncertainty Calibration and Adaptive Compression | Yufei Cui, Ziquan Liu, Qiao Li, Antoni B. Chan, Chun Jason Xue | Nested networks or slimmable networks are neural networks whose architectures can be adjusted instantly during testing time, e.g., based on computational constraints. Recent studies have focused on a "nested dropout" layer, which is able to order the nodes of a layer by importance during training, thus generating a nested set of sub-networks that are optimal for different configurations of resources. However, the dropout rate is fixed as a hyper-parameter over different layers during the whole training process. Therefore, when nodes are removed, the performance decays in a human-specified trajectory rather than in a trajectory learned from data. Another drawback is the generated sub-networks are deterministic networks without well-calibrated uncertainty. To address these two problems, we develop a Bayesian approach to nested neural networks. We propose a variational ordering unit that draws samples for nested dropout at a low cost, from a proposed Downhill distribution, which provides useful gradients to the parameters of nested dropout. Based on this approach, we design a Bayesian nested neural network that learns the order knowledge of the node distributions. In experiments, we show that the proposed approach outperforms the nested network in terms of accuracy, calibration, and out-of-domain detection in classification tasks. It also outperforms the related approach on uncertainty-critical tasks in computer vision. | https://openaccess.thecvf.com/content/CVPR2021/papers/Cui_Bayesian_Nested_Neural_Networks_for_Uncertainty_Calibration_and_Adaptive_Compression_CVPR_2021_paper.pdf | http://arxiv.org/abs/2101.11353 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Cui_Bayesian_Nested_Neural_Networks_for_Uncertainty_Calibration_and_Adaptive_Compression_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Cui_Bayesian_Nested_Neural_Networks_for_Uncertainty_Calibration_and_Adaptive_Compression_CVPR_2021_paper.html | CVPR 2021 | null | null |
Fast Bayesian Uncertainty Estimation and Reduction of Batch Normalized Single Image Super-Resolution Network | Aupendu Kar, Prabir Kumar Biswas | Convolutional neural network (CNN) has achieved unprecedented success in image super-resolution tasks in recent years. However, the network's performance depends on the distribution of the training sets and degrades on out-of-distribution samples. This paper adopts a Bayesian approach for estimating uncertainty associated with output and applies it in a deep image super-resolution model to address the concern mentioned above. We use the uncertainty estimation technique using the batch-normalization layer, where stochasticity of the batch mean and variance generate Monte-Carlo (MC) samples. The MC samples, which are nothing but different super-resolved images using different stochastic parameters, reconstruct the image, and provide a confidence or uncertainty map of the reconstruction. We propose a faster approach for MC sample generation, and it allows the variable image size during testing. Therefore, it will be useful for image reconstruction domain. Our experimental findings show that this uncertainty map strongly relates to the quality of reconstruction generated by the deep CNN model and explains its limitation. Furthermore, this paper proposes an approach to reduce the model's uncertainty for an input image, and it helps to defend the adversarial attacks on the image super-resolution model. The proposed uncertainty reduction technique also improves the performance of the model for out-of-distribution test images. To the best of our knowledge, we are the first to propose an adversarial defense mechanism in any image reconstruction domain. | https://openaccess.thecvf.com/content/CVPR2021/papers/Kar_Fast_Bayesian_Uncertainty_Estimation_and_Reduction_of_Batch_Normalized_Single_CVPR_2021_paper.pdf | http://arxiv.org/abs/1903.09410 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Kar_Fast_Bayesian_Uncertainty_Estimation_and_Reduction_of_Batch_Normalized_Single_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Kar_Fast_Bayesian_Uncertainty_Estimation_and_Reduction_of_Batch_Normalized_Single_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Kar_Fast_Bayesian_Uncertainty_CVPR_2021_supplemental.pdf | null |
Euro-PVI: Pedestrian Vehicle Interactions in Dense Urban Centers | Apratim Bhattacharyya, Daniel Olmeda Reino, Mario Fritz, Bernt Schiele | Accurate prediction of pedestrian and bicyclist paths is integral to the development of reliable autonomous vehicles in dense urban environments. The interactions between vehicle and pedestrian or bicyclist have a significant impact on the trajectories of traffic participants e.g. stopping or turning to avoid collisions. Although recent datasets and trajectory prediction approaches have fostered the development of autonomous vehicles yet the amount of vehicle-pedestrian (bicyclist) interactions modeled are sparse. In this work, we propose Euro-PVI, a dataset of pedestrian and bicyclist trajectories. In particular, our dataset caters more diverse and complex interactions in dense urban scenarios compared to the existing datasets. To address the challenges in predicting future trajectories with dense interactions, we develop a joint inference model that learns an expressive multi-modal shared latent space across agents in the urban scene. This enables our Joint-b-cVAE approach to better model the distribution of future trajectories. We achieve state of the art results on the nuScenes and Euro-PVI datasets demonstrating the importance of capturing interactions between ego-vehicle and pedestrians (bicyclists) for accurate predictions. | https://openaccess.thecvf.com/content/CVPR2021/papers/Bhattacharyya_Euro-PVI_Pedestrian_Vehicle_Interactions_in_Dense_Urban_Centers_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Bhattacharyya_Euro-PVI_Pedestrian_Vehicle_Interactions_in_Dense_Urban_Centers_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Bhattacharyya_Euro-PVI_Pedestrian_Vehicle_Interactions_in_Dense_Urban_Centers_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Bhattacharyya_Euro-PVI_Pedestrian_Vehicle_CVPR_2021_supplemental.pdf | null |
RepVGG: Making VGG-Style ConvNets Great Again | Xiaohan Ding, Xiangyu Zhang, Ningning Ma, Jungong Han, Guiguang Ding, Jian Sun | We present a simple but powerful architecture of convolutional neural network, which has a VGG-like inference-time body composed of nothing but a stack of 3x3 convolution and ReLU, while the training-time model has a multi-branch topology. Such decoupling of the training-time and inference-time architecture is realized by a structural re-parameterization technique so that the model is named RepVGG. On ImageNet, RepVGG reaches over 80% top-1 accuracy, which is the first time for a plain model, to the best of our knowledge. On NVIDIA 1080Ti GPU, RepVGG models run 83% faster than ResNet-50 or 101% faster than ResNet-101 with higher accuracy and show favorable accuracy-speed trade-off compared to the state-of-the-art models like EfficientNet and RegNet. The code and trained models are available at https://github.com/megvii-model/RepVGG. | https://openaccess.thecvf.com/content/CVPR2021/papers/Ding_RepVGG_Making_VGG-Style_ConvNets_Great_Again_CVPR_2021_paper.pdf | http://arxiv.org/abs/2101.03697 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Ding_RepVGG_Making_VGG-Style_ConvNets_Great_Again_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Ding_RepVGG_Making_VGG-Style_ConvNets_Great_Again_CVPR_2021_paper.html | CVPR 2021 | null | null |
Partial Feature Selection and Alignment for Multi-Source Domain Adaptation | Yangye Fu, Ming Zhang, Xing Xu, Zuo Cao, Chao Ma, Yanli Ji, Kai Zuo, Huimin Lu | Multi-Source Domain Adaptation (MSDA), which dedicates to transfer the knowledge learned from multiple source domains to an unlabeled target domain, has drawn increasing attention in the research community. By assuming that the source and target domains share consistent key feature representations and identical label space, existing studies on MSDA typically utilize the entire union set of features from both the source and target domains to obtain the feature map and align the map for each category and domain. However, the default setting of MSDA may neglect the issue of "partialness", i.e., 1) a part of the features contained in the union set of multiple source domains may not present in the target domain; 2) the label space of the target domain may not completely overlap with the multiple source domains. In this paper, we unify the above two cases to a more generalized MSDA task as Multi-Source Partial Domain Adaptation (MSPDA). We propose a novel model termed Partial Feature Selection and Alignment (PFSA) to jointly cope with both MSDA and MSPDA tasks. Specifically, we firstly employ a feature selection vector based on the correlation among the features of multiple sources and target domains. We then design three effective feature alignment losses to jointly align the selected features by preserving the domain information of the data sample clusters in the same category and the discrimination between different classes. Extensive experiments on various benchmark datasets for both MSDA and MSPDA tasks demonstrate that our proposed PFSA approach remarkably outperforms the state-of-the-art MSDA and unimodal PDA methods. | https://openaccess.thecvf.com/content/CVPR2021/papers/Fu_Partial_Feature_Selection_and_Alignment_for_Multi-Source_Domain_Adaptation_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Fu_Partial_Feature_Selection_and_Alignment_for_Multi-Source_Domain_Adaptation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Fu_Partial_Feature_Selection_and_Alignment_for_Multi-Source_Domain_Adaptation_CVPR_2021_paper.html | CVPR 2021 | null | null |
Multi-Institutional Collaborations for Improving Deep Learning-Based Magnetic Resonance Image Reconstruction Using Federated Learning | Pengfei Guo, Puyang Wang, Jinyuan Zhou, Shanshan Jiang, Vishal M. Patel | Fast and accurate reconstruction of magnetic resonance (MR) images from under-sampled data is important in many clinical applications. In recent years, deep learning-based methods have been shown to produce superior performance on MR image reconstruction. However, these methods require large amounts of data which is difficult to collect and share due to the high cost of acquisition and medical data privacy regulations. In order to overcome this challenge, we propose a federated learning (FL) based solution in which we take advantage of the MR data available at different institutions while preserving patients' privacy. However, the generalizability of models trained with the FL setting can still be suboptimal due to domain shift, which results from the data collected at multiple institutions with different sensors, disease types, and acquisition protocols, etc. With the motivation of circumventing this challenge, we propose a cross-site modeling for MR image reconstruction in which the learned intermediate latent features among different source sites are aligned with the distribution of the latent features at the target site. Extensive experiments are conducted to provide various insights about FL for MR image reconstruction. Experimental results demonstrate that the proposed framework is a promising direction to utilize multi-institutional data without compromising patients' privacy for achieving improved MR image reconstruction. Our code is available at https://github.com/guopengf/FL-MRCM | https://openaccess.thecvf.com/content/CVPR2021/papers/Guo_Multi-Institutional_Collaborations_for_Improving_Deep_Learning-Based_Magnetic_Resonance_Image_Reconstruction_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.02148 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Guo_Multi-Institutional_Collaborations_for_Improving_Deep_Learning-Based_Magnetic_Resonance_Image_Reconstruction_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Guo_Multi-Institutional_Collaborations_for_Improving_Deep_Learning-Based_Magnetic_Resonance_Image_Reconstruction_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Guo_Multi-Institutional_Collaborations_for_CVPR_2021_supplemental.pdf | null |
UAV-Human: A Large Benchmark for Human Behavior Understanding With Unmanned Aerial Vehicles | Tianjiao Li, Jun Liu, Wei Zhang, Yun Ni, Wenqian Wang, Zhiheng Li | Human behavior understanding with unmanned aerial vehicles (UAVs) is of great significance for a wide range of applications, which simultaneously brings an urgent demand of large, challenging, and comprehensive benchmarks for the development and evaluation of UAV-based models. However, existing benchmarks have limitations in terms of the amount of captured data, types of data modalities, categories of provided tasks, and diversities of subjects and environments. Here we propose a new benchmark - UAV-Human - for human behavior understanding with UAVs, which contains 67,428 multi-modal video sequences and 119 subjects for action recognition, 22,476 frames for pose estimation, 41,290 frames and 1,144 identities for person re-identification, and 22,263 frames for attribute recognition. Our dataset was collected by a flying UAV in multiple urban and rural districts in both daytime and nighttime over three months, hence covering extensive diversities w.r.t subjects, backgrounds, illuminations, weathers, occlusions, camera motions, and UAV flying attitudes. Such a comprehensive and challenging benchmark shall be able to promote the research of UAV-based human behavior understanding, including action recognition, pose estimation, re-identification, and attribute recognition. Furthermore, we propose a fisheye-based action recognition method that mitigates the distortions in fisheye videos via learning unbounded transformations guided by flat RGB videos. Experiments show the efficacy of our method on the UAV-Human dataset. | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_UAV-Human_A_Large_Benchmark_for_Human_Behavior_Understanding_With_Unmanned_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Li_UAV-Human_A_Large_Benchmark_for_Human_Behavior_Understanding_With_Unmanned_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Li_UAV-Human_A_Large_Benchmark_for_Human_Behavior_Understanding_With_Unmanned_CVPR_2021_paper.html | CVPR 2021 | null | null |
An Alternative Probabilistic Interpretation of the Huber Loss | Gregory P. Meyer | The Huber loss is a robust loss function used for a wide range of regression tasks. To utilize the Huber loss, a parameter that controls the transitions from a quadratic function to an absolute value function needs to be selected. We believe the standard probabilistic interpretation that relates the Huber loss to the Huber density fails to provide adequate intuition for identifying the transition point. As a result, a hyper-parameter search is often necessary to determine an appropriate value. In this work, we propose an alternative probabilistic interpretation of the Huber loss, which relates minimizing the loss to minimizing an upper-bound on the Kullback-Leibler divergence between Laplace distributions, where one distribution represents the noise in the ground-truth and the other represents the noise in the prediction. In addition, we show that the parameters of the Laplace distributions are directly related to the transition point of the Huber loss. We demonstrate, through a toy problem, that the optimal transition point of the Huber loss is closely related to the distribution of the noise in the ground-truth data. As a result, our interpretation provides an intuitive way to identify well-suited hyper-parameters by approximating the amount of noise in the data, which we demonstrate through a case study and experimentation on the Faster R-CNN and RetinaNet object detectors. | https://openaccess.thecvf.com/content/CVPR2021/papers/Meyer_An_Alternative_Probabilistic_Interpretation_of_the_Huber_Loss_CVPR_2021_paper.pdf | http://arxiv.org/abs/1911.02088 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Meyer_An_Alternative_Probabilistic_Interpretation_of_the_Huber_Loss_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Meyer_An_Alternative_Probabilistic_Interpretation_of_the_Huber_Loss_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Meyer_An_Alternative_Probabilistic_CVPR_2021_supplemental.pdf | null |
Siamese Natural Language Tracker: Tracking by Natural Language Descriptions With Siamese Trackers | Qi Feng, Vitaly Ablavsky, Qinxun Bai, Stan Sclaroff | We propose a novel Siamese Natural Language Tracker (SNLT), which brings the advancements in visual tracking to the tracking by natural language (NL) specification task. The proposed SNLT is applicable to a wide range of Siamese trackers, providing a new class of baselines for the tracking by NL task and promising future improvements from the advancements of Siamese trackers. The carefully designed architecture of the Siamese Natural Language Region Proposal Network (SNL-RPN), together with the Dynamic Aggregation of vision and language modalities, is introduced to perform the tracking by NL task. Empirical results over tracking benchmarks with NL annotations show that the proposed SNLT improves Siamese trackers by 3 to 7 percentage points with a slight tradeoff of speed. The proposed SNLT outperforms all NL trackers to-date and is competitive among state-of-the-art real-time trackers on LaSOT benchmarks while running at 50 frames per second on a single GPU. | https://openaccess.thecvf.com/content/CVPR2021/papers/Feng_Siamese_Natural_Language_Tracker_Tracking_by_Natural_Language_Descriptions_With_CVPR_2021_paper.pdf | http://arxiv.org/abs/1912.02048 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Feng_Siamese_Natural_Language_Tracker_Tracking_by_Natural_Language_Descriptions_With_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Feng_Siamese_Natural_Language_Tracker_Tracking_by_Natural_Language_Descriptions_With_CVPR_2021_paper.html | CVPR 2021 | null | null |
Discrimination-Aware Mechanism for Fine-Grained Representation Learning | Furong Xu, Meng Wang, Wei Zhang, Yuan Cheng, Wei Chu | Recently, with the emergence of retrieval requirements for certain individual in the same superclass, e.g., birds, persons, cars, fine-grained recognition task has attracted a significant amount of attention from academia and industry. In fine-grained recognition scenario, the inter-class differences are quite diverse and subtle, which makes it challenging to extract all the discriminative cues. Traditional training mechanism optimizes the overall discriminativeness of the whole feature. It may stop early when some feature elements has been trained to distinguish training samples well, leaving other elements insufficiently trained for a feature. This would result in a less generalizable feature extractor that only captures major discriminative cues and ignores subtle ones. Therefore, there is a need for a training mechanism that enforces the discriminativeness of all the elements in the feature to capture more the subtle visual cues. In this paper, we propose a Discrimination-Aware Mechanism (DAM) that iteratively identifies insufficiently trained elements and improves them. DAM is able to increase the number of well learned elements, which captures more visual cues by the feature extractor. In this way, a more informative representation is learned, which brings better generalization performance. We show that DAM can be easily applied to both proxy-based and pair-based loss functions, and thus can be used in most existing fine-grained recognition paradigms. Comprehensive experiments on CUB-200-2011, Cars196, Market-1501, and MSMT17 datasets demonstrate the advantages of our DAM based loss over the related state-of-the-art approaches. | https://openaccess.thecvf.com/content/CVPR2021/papers/Xu_Discrimination-Aware_Mechanism_for_Fine-Grained_Representation_Learning_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Xu_Discrimination-Aware_Mechanism_for_Fine-Grained_Representation_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Xu_Discrimination-Aware_Mechanism_for_Fine-Grained_Representation_Learning_CVPR_2021_paper.html | CVPR 2021 | null | null |
Rainbow Memory: Continual Learning With a Memory of Diverse Samples | Jihwan Bang, Heesu Kim, YoungJoon Yoo, Jung-Woo Ha, Jonghyun Choi | Continual learning is a realistic learning scenario for AI models. Prevalent scenario of continual learning, however, assumes disjoint sets of classes as tasks and is less realistic rather artificial. Instead, we focus on 'blurry' task boundary; where tasks shares classes and is more realistic and practical. To address such task, we argue the importance of diversity of samples in an episodic memory. To enhance the sample diversity in the memory, we propose a novel memory management strategy based on per-sample classification uncertainty and data augmentation, named Rainbow Memory (RM). With extensive empirical validations on MNIST, CIFAR10, CIFAR100, and ImageNet datasets, we show that the proposed method significantly improves the accuracy in blurry continual learning setups, outperforming state of the arts by large margins despite its simplicity. Code and data splits will be available in https://github.com/clovaai/rainbow-memory. | https://openaccess.thecvf.com/content/CVPR2021/papers/Bang_Rainbow_Memory_Continual_Learning_With_a_Memory_of_Diverse_Samples_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.17230 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Bang_Rainbow_Memory_Continual_Learning_With_a_Memory_of_Diverse_Samples_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Bang_Rainbow_Memory_Continual_Learning_With_a_Memory_of_Diverse_Samples_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Bang_Rainbow_Memory_Continual_CVPR_2021_supplemental.pdf | null |
Learning Discriminative Prototypes With Dynamic Time Warping | Xiaobin Chang, Frederick Tung, Greg Mori | Dynamic Time Warping (DTW) is widely used for temporal data processing. However, existing methods can neither learn the discriminative prototypes of different classes nor exploit such prototypes for further analysis. We propose Discriminative Prototype DTW (DP-DTW), a novel method to learn class-specific discriminative prototypes for temporal recognition tasks. DP-DTW shows superior performance compared to conventional DTWs on time series classification benchmarks. Combined with end-to-end deep learning, DP-DTW can handle challenging weakly supervised action segmentation problems and achieves state of the art results on standard benchmarks. Moreover, detailed reasoning on the input video is enabled by the learned action prototypes. Specifically, an action-based video summarization can be obtained by aligning the input sequence with action prototypes. | https://openaccess.thecvf.com/content/CVPR2021/papers/Chang_Learning_Discriminative_Prototypes_With_Dynamic_Time_Warping_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.09458 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Chang_Learning_Discriminative_Prototypes_With_Dynamic_Time_Warping_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Chang_Learning_Discriminative_Prototypes_With_Dynamic_Time_Warping_CVPR_2021_paper.html | CVPR 2021 | null | null |
Deep Implicit Moving Least-Squares Functions for 3D Reconstruction | Shi-Lin Liu, Hao-Xiang Guo, Hao Pan, Peng-Shuai Wang, Xin Tong, Yang Liu | Point set is a flexible and lightweight representation widely used for 3D deep learning. However, their discrete nature prevents them from representing continuous and fine geometry, posing a major issue for learning-based shape generation. In this work, we turn the discrete point sets into smooth surfaces by introducing the well-known implicit moving least-squares (IMLS) surface formulation, which naturally defines locally implicit functions on point sets. We incorporate IMLS surface generation into deep neural networks for inheriting both the flexibility of point sets and the high quality of implicit surfaces. Our IMLSNet predicts an octree structure as a scaffold for generating MLS points where needed and characterizes shape geometry with learned local priors. Furthermore, our implicit function evaluation is independent of the neural network once the MLS points are predicted, thus enabling fast runtime evaluation. Our experiments on 3D object reconstruction demonstrate that IMLSNets outperform state-of-the-art learning-based methods in terms of reconstruction quality and computational efficiency. Extensive ablation tests also validate our network design and loss functions. | https://openaccess.thecvf.com/content/CVPR2021/papers/Liu_Deep_Implicit_Moving_Least-Squares_Functions_for_3D_Reconstruction_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.12266 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_Deep_Implicit_Moving_Least-Squares_Functions_for_3D_Reconstruction_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_Deep_Implicit_Moving_Least-Squares_Functions_for_3D_Reconstruction_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Liu_Deep_Implicit_Moving_CVPR_2021_supplemental.pdf | null |
Video Prediction Recalling Long-Term Motion Context via Memory Alignment Learning | Sangmin Lee, Hak Gu Kim, Dae Hwi Choi, Hyung-Il Kim, Yong Man Ro | Our work addresses long-term motion context issues for predicting future frames. To predict the future precisely, it is required to capture which long-term motion context (e.g., walking or running) the input motion (e.g., leg movement) belongs to. The bottlenecks arising when dealing with the long-term motion context are: (i) how to predict the long-term motion context naturally matching input sequences with limited dynamics, (ii) how to predict the long-term motion context with high-dimensionality (e.g., complex motion). To address the issues, we propose novel motion context-aware video prediction. To solve the bottleneck (i), we introduce a long-term motion context memory (LMC-Memory) with memory alignment learning. The proposed memory alignment learning enables to store long-term motion contexts into the memory and to match them with sequences including limited dynamics. As a result, the long-term context can be recalled from the limited input sequence. In addition, to resolve the bottleneck (ii), we propose memory query decomposition to store local motion context (i.e., low-dimensional dynamics) and recall the suitable local context for each local part of the input individually. It enables to boost the alignment effects of the memory. Experimental results show that the proposed method outperforms other sophisticated RNN-based methods, especially in long-term condition. Further, we validate the effectiveness of the proposed network designs by conducting ablation studies and memory feature analysis. The source code of this work is available. | https://openaccess.thecvf.com/content/CVPR2021/papers/Lee_Video_Prediction_Recalling_Long-Term_Motion_Context_via_Memory_Alignment_Learning_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.00924 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Lee_Video_Prediction_Recalling_Long-Term_Motion_Context_via_Memory_Alignment_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Lee_Video_Prediction_Recalling_Long-Term_Motion_Context_via_Memory_Alignment_Learning_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Lee_Video_Prediction_Recalling_CVPR_2021_supplemental.pdf | null |
Automatic Vertebra Localization and Identification in CT by Spine Rectification and Anatomically-Constrained Optimization | Fakai Wang, Kang Zheng, Le Lu, Jing Xiao, Min Wu, Shun Miao | Accurate vertebra localization and identification are required in many clinical applications of spine disorder diagnosis and surgery planning. However, significant challenges are posed in this task by highly varying pathologies (such as vertebral compression fracture, scoliosis, and vertebral fixation) and imaging conditions (such as limited field of view and metal streak artifacts). This paper proposes a robust and accurate method that effectively exploits the anatomical knowledge of the spine to facilitate vertebra localization and identification. A key point localization model is trained to produce activation maps of vertebra centers. They are then re-sampled along the spine centerline to produce spine-rectified activation maps, which are further aggregated into 1-D activation signals. Following this, an anatomically-constrained optimization module is introduced to jointly search for the optimal vertebra centers under a soft constraint that regulates the distance between vertebrae and a hard constraint on the consecutive vertebra indices. When being evaluated on a major public benchmark of 302 highly pathological CT images, the proposed method reports the state of the art identification (id.) rate of 97.4%, and outperforms the best competing method of 94.7% id. rate by reducing the relative id. error rate by half. | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_Automatic_Vertebra_Localization_and_Identification_in_CT_by_Spine_Rectification_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.07947 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Automatic_Vertebra_Localization_and_Identification_in_CT_by_Spine_Rectification_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Automatic_Vertebra_Localization_and_Identification_in_CT_by_Spine_Rectification_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wang_Automatic_Vertebra_Localization_CVPR_2021_supplemental.pdf | null |
MotionRNN: A Flexible Model for Video Prediction With Spacetime-Varying Motions | Haixu Wu, Zhiyu Yao, Jianmin Wang, Mingsheng Long | This paper tackles video prediction from a new dimension of predicting spacetime-varying motions that are incessantly changing across both space and time. Prior methods mainly capture the temporal state transitions but overlook the complex spatiotemporal variations of the motion itself, making them difficult to adapt to ever-changing motions. We observe that physical world motions can be decomposed into transient variation and motion trend, while the latter can be regarded as the accumulation of previous motions. Thus, simultaneously capturing the transient variation and the motion trend is the key to make spacetime-varying motions more predictable. Based on these observations, we propose the MotionRNN framework, which can capture the complex variations within motions and adapt to spacetime-varying scenarios. MotionRNN has two main contributions. The first is that we design the MotionGRU unit, which can model the transient variation and motion trend in a unified way. The second is that we apply the MotionGRU to RNN-based predictive models and indicate a new flexible video prediction architecture with a Motion Highway that can significantly improve the ability to predict changeable motions and avoid motion vanishing for stacked multiple-layer predictive models. With high flexibility, this framework can adapt to a series of models for deterministic spatiotemporal prediction. Our MotionRNN can yield significant improvements on three challenging benchmarks for video prediction with spacetime-varying motions. | https://openaccess.thecvf.com/content/CVPR2021/papers/Wu_MotionRNN_A_Flexible_Model_for_Video_Prediction_With_Spacetime-Varying_Motions_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.02243 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wu_MotionRNN_A_Flexible_Model_for_Video_Prediction_With_Spacetime-Varying_Motions_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wu_MotionRNN_A_Flexible_Model_for_Video_Prediction_With_Spacetime-Varying_Motions_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wu_MotionRNN_A_Flexible_CVPR_2021_supplemental.pdf | null |
MOS: Towards Scaling Out-of-Distribution Detection for Large Semantic Space | Rui Huang, Yixuan Li | Detecting out-of-distribution (OOD) inputs is a central challenge for safely deploying machine learning models in the real world. Existing solutions are mainly driven by small datasets, with low resolution and very few class labels (e.g., CIFAR). As a result, OOD detection for large-scale image classification tasks remains largely unexplored. In this paper, we bridge this critical gap by proposing a group-based OOD detection framework, along with a novel OOD scoring function termed MOS. Our key idea is to decompose the large semantic space into smaller groups with similar concepts, which allows simplifying the decision boundaries between in- vs. out-of-distribution data for effective OOD detection. Our method scales substantially better for high-dimensional class space than previous approaches. We evaluate models trained on ImageNet against four carefully curated OOD datasets, spanning diverse semantics. MOS establishes state-of-the-art performance, reducing the average FPR95 by 14.33% while achieving 6x speedup in inference compared to the previous best method. | https://openaccess.thecvf.com/content/CVPR2021/papers/Huang_MOS_Towards_Scaling_Out-of-Distribution_Detection_for_Large_Semantic_Space_CVPR_2021_paper.pdf | http://arxiv.org/abs/2105.01879 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Huang_MOS_Towards_Scaling_Out-of-Distribution_Detection_for_Large_Semantic_Space_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Huang_MOS_Towards_Scaling_Out-of-Distribution_Detection_for_Large_Semantic_Space_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Huang_MOS_Towards_Scaling_CVPR_2021_supplemental.pdf | null |
Visual Semantic Role Labeling for Video Understanding | Arka Sadhu, Tanmay Gupta, Mark Yatskar, Ram Nevatia, Aniruddha Kembhavi | We propose a new framework for understanding and representing related salient events in a video using visual semantic role labeling. We represent videos as a set of related events, wherein each event consists of a verb and multiple entities that fulfill various roles relevant to that event. To study the challenging task of semantic role labeling in videos or VidSRL, we introduce the VidSitu benchmark, a large scale video understanding data source with 27K 10-second movie clips richly annotated with a verb and semantic-roles every 2 seconds. Entities are co-referenced across events within a movie clip and events are connected to each other via event-event relations. Clips in VidSitu are drawn from a large collection of movies ( 3K) and have been chosen to be both complex ( 4.2 unique verbs within a video) as well as diverse ( 200 verbs have more than 100 annotations each). We provide a comprehensive analysis of the dataset in comparison to other publicly available video understanding benchmarks, several illustrative baselines and evaluate a range of standard video recognition models. Our code and dataset will be released publicly. | https://openaccess.thecvf.com/content/CVPR2021/papers/Sadhu_Visual_Semantic_Role_Labeling_for_Video_Understanding_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.00990 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Sadhu_Visual_Semantic_Role_Labeling_for_Video_Understanding_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Sadhu_Visual_Semantic_Role_Labeling_for_Video_Understanding_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Sadhu_Visual_Semantic_Role_CVPR_2021_supplemental.pdf | null |
SwiftNet: Real-Time Video Object Segmentation | Haochen Wang, Xiaolong Jiang, Haibing Ren, Yao Hu, Song Bai | In this work we present SwiftNet for real-time semi-supervised video object segmentation (one-shot VOS), which reports 77.8% J&F and 70 FPS on DAVIS 2017 validation dataset, leading all present solutions in overall accuracy and speed performance. We achieve this by elaborately compressing spatiotemporal redundancy in matching-based VOS via Pixel-Adaptive Memory (PAM). Temporally, PAM adaptively triggers memory updates on frames where objects display noteworthy inter-frame variations. Spatially, PAM selectively performs memory update and match on dynamic pixels while ignoring the static ones, significantly reducing redundant computations wasted on segmentation-irrelevant pixels. To promote efficient reference encoding, light-aggregation encoder is also introduced in SwiftNet deploying reversed sub-pixel. We hope SwiftNet could set a strong and efficient baseline for real-time VOS and facilitate its application in mobile vision. The source code of SwiftNet can be found at https://github.com/haochenheheda/SwiftNet. | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_SwiftNet_Real-Time_Video_Object_Segmentation_CVPR_2021_paper.pdf | http://arxiv.org/abs/2102.04604 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_SwiftNet_Real-Time_Video_Object_Segmentation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_SwiftNet_Real-Time_Video_Object_Segmentation_CVPR_2021_paper.html | CVPR 2021 | null | null |
Contrastive Embedding for Generalized Zero-Shot Learning | Zongyan Han, Zhenyong Fu, Shuo Chen, Jian Yang | Generalized zero-shot learning (GZSL) aims to recognize objects from both seen and unseen classes, when only the labeled examples from seen classes are provided. Recent feature generation methods learn a generative model that can synthesize the missing visual features of unseen classes to mitigate the data-imbalance problem in GZSL. However, the original visual feature space is suboptimal for GZSL classification since it lacks discriminative information. To tackle this issue, we propose to integrate the generation model with the embedding model, yielding a hybrid GZSL framework. The hybrid GZSL approach maps both the real and the synthetic samples produced by the generation model into an embedding space, where we perform the final GZSL classification. Specifically, we propose a contrastive embedding (CE) for our hybrid GZSL framework. The proposed contrastive embedding can leverage not only the class-wise supervision but also the instance-wise supervision, where the latter is usually neglected by existing GZSL researches. We evaluate our proposed hybrid GZSL framework with contrastive embedding, named CE-GZSL, on five benchmark datasets. The results show that our CEGZSL method can outperform the state-of-the-arts by a significant margin on three datasets. Our codes are available on https://github.com/Hanzy1996/CE-GZSL. | https://openaccess.thecvf.com/content/CVPR2021/papers/Han_Contrastive_Embedding_for_Generalized_Zero-Shot_Learning_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.16173 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Han_Contrastive_Embedding_for_Generalized_Zero-Shot_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Han_Contrastive_Embedding_for_Generalized_Zero-Shot_Learning_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Han_Contrastive_Embedding_for_CVPR_2021_supplemental.pdf | null |
Scale-Localized Abstract Reasoning | Yaniv Benny, Niv Pekar, Lior Wolf | We consider the abstract relational reasoning task, which is commonly used as an intelligence test. Since some patterns have spatial rationales, while others are only semantic, we propose a multi-scale architecture that processes each query in multiple resolutions. We show that indeed different rules are solved by different resolutions and a combined multi-scale approach outperforms the existing state of the art in this task on all benchmarks by 5-54%. The success of our method is shown to arise from multiple novelties. First, it searches for relational patterns in multiple resolutions, which allows it to readily detect visual relations, such as location, in higher resolution, while allowing the lower resolution module to focus on semantic relations, such as shape type. Second, we optimize the reasoning network of each resolution proportionally to its performance, hereby we motivate each resolution to specialize on the rules for which it performs better than the others and ignore cases that are already solved by the other resolutions. Third, we propose a new way to pool information along the rows and the columns of the illustration-grid of the query. Our work also analyses the existing benchmarks, demonstrating that the RAVEN dataset selects the negative examples in a way that is easily exploited. We, therefore, propose a modified version of the RAVEN dataset, named RAVEN-FAIR. Our code and pretrained models are available at https://github.com/yanivbenny/MRNet. | https://openaccess.thecvf.com/content/CVPR2021/papers/Benny_Scale-Localized_Abstract_Reasoning_CVPR_2021_paper.pdf | http://arxiv.org/abs/2009.09405 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Benny_Scale-Localized_Abstract_Reasoning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Benny_Scale-Localized_Abstract_Reasoning_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Benny_Scale-Localized_Abstract_Reasoning_CVPR_2021_supplemental.pdf | null |
Transferable Query Selection for Active Domain Adaptation | Bo Fu, Zhangjie Cao, Jianmin Wang, Mingsheng Long | Unsupervised domain adaptation (UDA) enables transferring knowledge from a related source domain to a fully unlabeled target domain. Despite the significant advances in UDA, the performance gap remains quite large between UDA and supervised learning with fully labeled target data. Active domain adaptation (ADA) mitigates the gap under minimal annotation cost by selecting a small quota of target samples to annotate and incorporating them into training. Due to the domain shift, the query selection criteria of prior active learning methods may be ineffective to select the most informative target samples for annotation. In this paper, we propose Transferable Query Selection (TQS), which selects the most informative samples under domain shift by an ensemble of three new criteria: transferable committee, transferable uncertainty, and transferable domainness. We further develop a randomized selection algorithm to enhance the diversity of the selected samples. Experiments show that TQS remarkably outperforms previous UDA and ADA methods on several domain adaptation datasets. Deeper analyses demonstrate that TQS can select the most informative target samples under the domain shift. | https://openaccess.thecvf.com/content/CVPR2021/papers/Fu_Transferable_Query_Selection_for_Active_Domain_Adaptation_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Fu_Transferable_Query_Selection_for_Active_Domain_Adaptation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Fu_Transferable_Query_Selection_for_Active_Domain_Adaptation_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Fu_Transferable_Query_Selection_CVPR_2021_supplemental.pdf | null |
CLCC: Contrastive Learning for Color Constancy | Yi-Chen Lo, Chia-Che Chang, Hsuan-Chao Chiu, Yu-Hao Huang, Chia-Ping Chen, Yu-Lin Chang, Kevin Jou | In this paper, we present CLCC, a novel contrastive learning framework for color constancy. Contrastive learning has been applied for learning high-quality visual representations for image classification. One key aspect to yield useful representations for image classification is to design illuminant invariant augmentations. However, the illuminant invariant assumption conflicts with the nature of the color constancy task, which aims to estimate the illuminant given a raw image. Therefore, we construct effective contrastive pairs for learning better illuminant-dependent features via a novel raw-domain color augmentation. On the NUS-8 dataset, our method provides 17.5% relative improvements over a strong baseline, reaching state-of-the-art performance without increasing model complexity. Furthermore, our method achieves competitive performance on the Gehler dataset with 3x fewer parameters compared to top-ranking deep learning methods. More importantly, we show that our model is more robust to different scenes under close proximity of illuminants, significantly reducing 28.7% worst-case error in data-sparse regions. Our code is available at https://github.com/howardyclo/clcc-cvpr21. | https://openaccess.thecvf.com/content/CVPR2021/papers/Lo_CLCC_Contrastive_Learning_for_Color_Constancy_CVPR_2021_paper.pdf | http://arxiv.org/abs/2106.04989 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Lo_CLCC_Contrastive_Learning_for_Color_Constancy_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Lo_CLCC_Contrastive_Learning_for_Color_Constancy_CVPR_2021_paper.html | CVPR 2021 | null | null |
Dual Attention Suppression Attack: Generate Adversarial Camouflage in Physical World | Jiakai Wang, Aishan Liu, Zixin Yin, Shunchang Liu, Shiyu Tang, Xianglong Liu | Deep learning models are vulnerable to adversarial examples. As a more threatening type for practical deep learning systems, physical adversarial examples have received extensive research attention in recent years. However, without exploiting the intrinsic characteristics such as model-agnostic and human-specific patterns, existing works generate weak adversarial perturbations in the physical world, which fall short of attacking across different models and show visually suspicious appearance. Motivated by the viewpoint that attention reflects the intrinsic characteristics of the recognition process, this paper proposes the Dual Attention Suppression (DAS) attack to generate visually-natural physical adversarial camouflage with strong transferability by suppressing both model and human attention. As for attacking, we generate transferable adversarial camouflages by distracting the model-shared similar attention patterns from the target to non-target regions. Meanwhile, based on the fact that human visual attention always focuses on salient items (e.g., suspicious distortions), we evade the human-specific bottom-up attention to generate visually-natural camouflage which is correlated to the scenario context. We conduct extensive experiments in both the digital and physical world for classification and detection tasks on up to date models (e.g., Yolo-V5) and significantly demonstrate that our method outperforms state-of-the-art methods. | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_Dual_Attention_Suppression_Attack_Generate_Adversarial_Camouflage_in_Physical_World_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.01050 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Dual_Attention_Suppression_Attack_Generate_Adversarial_Camouflage_in_Physical_World_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Dual_Attention_Suppression_Attack_Generate_Adversarial_Camouflage_in_Physical_World_CVPR_2021_paper.html | CVPR 2021 | null | null |
Long-Tailed Multi-Label Visual Recognition by Collaborative Training on Uniform and Re-Balanced Samplings | Hao Guo, Song Wang | Long-tailed data distribution is common in many multi-label visual recognition tasks and the direct use of these data for training usually leads to relatively low performance on tail classes. While re-balanced data sampling can improve the performance on tail classes, it may also hurt the performance on head classes in training due to label co-occurrence. In this paper, we propose a new approach to train on both uniform and re-balanced samplings in a collaborative way, resulting in performance improvement on both head and tail classes. More specifically, we design a visual recognition network with two branches: one takes the uniform sampling as input while the other takes the re-balanced sampling as the input. For each branch, we conduct visual recognition using a binary-cross-entropy-based classification loss with learnable logit compensation. We further define a new cross-branch loss to enforce the consistency when the same input image goes through the two branches. We conduct extensive experiments on VOC-LT and COCO-LT datasets. The results show that the proposed method significantly outperforms previous state-of-the-art methods on long-tailed multi-label visual recognition. | https://openaccess.thecvf.com/content/CVPR2021/papers/Guo_Long-Tailed_Multi-Label_Visual_Recognition_by_Collaborative_Training_on_Uniform_and_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Guo_Long-Tailed_Multi-Label_Visual_Recognition_by_Collaborative_Training_on_Uniform_and_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Guo_Long-Tailed_Multi-Label_Visual_Recognition_by_Collaborative_Training_on_Uniform_and_CVPR_2021_paper.html | CVPR 2021 | null | null |
3D Object Detection With Pointformer | Xuran Pan, Zhuofan Xia, Shiji Song, Li Erran Li, Gao Huang | Feature learning for 3D object detection from point clouds is very challenging due to the irregularity of 3D point cloud data. In this paper, we propose Pointformer, a Transformer backbone designed for 3D point clouds to learn features effectively. Specifically, a Local Transformer module is employed to model interactions among points in a local region, which learns context-dependent region features at an object level. A Global Transformer is designed to learn context-aware representations at the scene level. To further capture the dependencies among multi-scale representations, we propose Local-Global Transformer to integrate local features with global features from higher resolution. In addition, we introduce an efficient coordinate refinement module to shift down-sampled points closer to object centroids, which improves object proposal generation. We use Pointformer as the backbone for state-of-the-art object detection models and demonstrate significant improvements over original models on both indoor and outdoor datasets. | https://openaccess.thecvf.com/content/CVPR2021/papers/Pan_3D_Object_Detection_With_Pointformer_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.11409 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Pan_3D_Object_Detection_With_Pointformer_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Pan_3D_Object_Detection_With_Pointformer_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Pan_3D_Object_Detection_CVPR_2021_supplemental.pdf | null |
Fair Feature Distillation for Visual Recognition | Sangwon Jung, Donggyu Lee, Taeeon Park, Taesup Moon | Fairness is becoming an increasingly crucial issue for computer vision, especially in the human-related decision systems. However, achieving algorithmic fairness, which makes a model produce indiscriminative outcomes against protected groups, is still an unresolved problem. In this paper, we devise a systematic approach which reduces algorithmic biases via feature distillation for visual recognition tasks, dubbed as MMD-based Fair Distillation (MFD). While the distillation technique has been widely used in general to improve the prediction accuracy, to the best of our knowledge, there has been no explicit work that also tries to improve fairness via distillation. Furthermore, We give a theoretical justification of our MFD on the effect of knowledge distillation and fairness. Throughout the extensive experiments, we show our MFD significantly mitigates the bias against specific minorities without any loss of the accuracy on both synthetic and real-world face datasets. | https://openaccess.thecvf.com/content/CVPR2021/papers/Jung_Fair_Feature_Distillation_for_Visual_Recognition_CVPR_2021_paper.pdf | http://arxiv.org/abs/2106.04411 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Jung_Fair_Feature_Distillation_for_Visual_Recognition_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Jung_Fair_Feature_Distillation_for_Visual_Recognition_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Jung_Fair_Feature_Distillation_CVPR_2021_supplemental.pdf | null |
Diversifying Sample Generation for Accurate Data-Free Quantization | Xiangguo Zhang, Haotong Qin, Yifu Ding, Ruihao Gong, Qinghua Yan, Renshuai Tao, Yuhang Li, Fengwei Yu, Xianglong Liu | Quantization has emerged as one of the most prevalent approaches to compress and accelerate neural networks. Recently, data-free quantization has been widely studied as a practical and promising solution. It synthesizes data for calibrating the quantized model according to the batch normalization (BN) statistics of FP32 ones and significantly relieves the heavy dependency on real training data in traditional quantization methods. Unfortunately, we find that in practice, the synthetic data identically constrained by BN statistics suffers serious homogenization at both distribution level and sample level and further causes a significant performance drop of the quantized model. We propose Diverse Sample Generation (DSG) scheme to mitigate the adverse effects caused by homogenization. Specifically, we slack the alignment of feature statistics in the BN layer to relax the constraint at the distribution level and design a layerwise enhancement to reinforce specific layers for different data samples. Our DSG scheme is versatile and even able to be applied to the state-of-the-art post-training quantization method like AdaRound. We evaluate the DSG scheme on the large-scale image classification task and consistently obtain significant improvements over various network architectures and quantization methods, especially when quantized to lower bits (e.g., up to 22% improvement on W4A4). Moreover, benefiting from the enhanced diversity, models calibrated by synthetic data perform close to those calibrated by real data and even outperform them on W4A4. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_Diversifying_Sample_Generation_for_Accurate_Data-Free_Quantization_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.01049 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Diversifying_Sample_Generation_for_Accurate_Data-Free_Quantization_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Diversifying_Sample_Generation_for_Accurate_Data-Free_Quantization_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhang_Diversifying_Sample_Generation_CVPR_2021_supplemental.pdf | null |
SSTVOS: Sparse Spatiotemporal Transformers for Video Object Segmentation | Brendan Duke, Abdalla Ahmed, Christian Wolf, Parham Aarabi, Graham W. Taylor | In this paper we introduce a Transformer-based approach to video object segmentation (VOS). To address compounding error and scalability issues of prior work, we propose a scalable, end-to-end method for VOS called Sparse Spatiotemporal Transformers (SST). SST extracts per-pixel representations for each object in a video using sparse attention over spatiotemporal features. Our attention-based formulation for VOS allows a model to learn to attend over a history of multiple frames and provides suitable inductive bias for performing correspondence-like computations necessary for solving motion segmentation. We demonstrate the effectiveness of attention-based over recurrent networks in the spatiotemporal domain. Our method achieves competitive results on YouTube-VOS and DAVIS 2017 with improved scalability and robustness to occlusions compared with the state of the art. Code is available at https://github.com/dukebw/SSTVOS. | https://openaccess.thecvf.com/content/CVPR2021/papers/Duke_SSTVOS_Sparse_Spatiotemporal_Transformers_for_Video_Object_Segmentation_CVPR_2021_paper.pdf | http://arxiv.org/abs/2101.08833 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Duke_SSTVOS_Sparse_Spatiotemporal_Transformers_for_Video_Object_Segmentation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Duke_SSTVOS_Sparse_Spatiotemporal_Transformers_for_Video_Object_Segmentation_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Duke_SSTVOS_Sparse_Spatiotemporal_CVPR_2021_supplemental.pdf | null |
Inferring CAD Modeling Sequences Using Zone Graphs | Xianghao Xu, Wenzhe Peng, Chin-Yi Cheng, Karl D.D. Willis, Daniel Ritchie | In computer-aided design (CAD), the ability to "reverse engineer" the modeling steps used to create 3D shapes is a long-sought-after goal. This process can be decomposed into two sub-problems: converting an input mesh or point cloud into a boundary representation (or B-rep), and then inferring modeling operations which construct this B-rep. In this paper, we present a new system for solving the second sub-problem. Central to our approach is a new geometric representation: the zone graph. Zones are the set of solid regions formed by extending all B-Rep faces and partitioning space with them; a zone graph has these zones as its nodes, with edges denoting geometric adjacencies between them. Zone graphs allow us to tractably work with industry-standard CAD operations, unlike prior work using CSG with parametric primitives. We focus on CAD programs consisting of sketch + extrude + Boolean operations, which are common in CAD practice. We phrase our problem as search in the space of such extrusions permitted by the zone graph, and we train a graph neural network to score potential extrusions in order to accelerate the search. We show that our approach outperforms an existing CSG inference baseline in terms of geometric reconstruction accuracy and reconstruction time, while also creating more plausible modeling sequences. | https://openaccess.thecvf.com/content/CVPR2021/papers/Xu_Inferring_CAD_Modeling_Sequences_Using_Zone_Graphs_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.03900 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Xu_Inferring_CAD_Modeling_Sequences_Using_Zone_Graphs_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Xu_Inferring_CAD_Modeling_Sequences_Using_Zone_Graphs_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Xu_Inferring_CAD_Modeling_CVPR_2021_supplemental.pdf | null |
Closed-Form Factorization of Latent Semantics in GANs | Yujun Shen, Bolei Zhou | A rich set of interpretable dimensions has been shown to emerge in the latent space of the Generative Adversarial Networks (GANs) trained for synthesizing images. In order to identify such latent dimensions for image editing, previous methods typically annotate a collection of synthesized samples and train linear classifiers in the latent space. However, they require a clear definition of the target attribute as well as the corresponding manual annotations, limiting their applications in practice. In this work, we examine the internal representation learned by GANs to reveal the underlying variation factors in an unsupervised manner. In particular, we take a closer look into the generation mechanism of GANs and further propose a closed-form factorization algorithm for latent semantic discovery by directly decomposing the pre-trained weights. With a lightning-fast implementation, our approach is capable of not only finding semantically meaningful dimensions comparably to the state-of-the-art supervised methods, but also resulting in far more versatile concepts across multiple GAN models trained on a wide range of datasets. | https://openaccess.thecvf.com/content/CVPR2021/papers/Shen_Closed-Form_Factorization_of_Latent_Semantics_in_GANs_CVPR_2021_paper.pdf | http://arxiv.org/abs/2007.06600 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Shen_Closed-Form_Factorization_of_Latent_Semantics_in_GANs_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Shen_Closed-Form_Factorization_of_Latent_Semantics_in_GANs_CVPR_2021_paper.html | CVPR 2021 | null | null |
Weakly-Supervised Physically Unconstrained Gaze Estimation | Rakshit Kothari, Shalini De Mello, Umar Iqbal, Wonmin Byeon, Seonwook Park, Jan Kautz | A major challenge for physically unconstrained gaze estimation is acquiring training data with 3D gaze annotations for in-the-wild and outdoor scenarios. In contrast, videos of human interactions in unconstrained environments are abundantly available and can be much more easily annotated with frame-level activity labels. In this work, we tackle the previously unexplored problem of weakly-supervised gaze estimation from videos of human interactions. We leverage the insight that strong gaze-related geometric constraints exist when people perform the activity of "looking at each other" (LAEO). To acquire viable 3D gaze supervision from LAEO labels, we propose a training algorithm along with several novel loss functions especially designed for the task. With weak supervision from two large scale CMU-Panoptic and AVA-LAEO activity datasets, we show significant improvements in (a) the accuracy of semi-supervised gaze estimation and (b) cross-domain generalization on the state-of-the-art physically unconstrained in-the-wild Gaze360 gaze estimation benchmark. We open source our code at https://github.com/NVlabs/weakly-supervised-gaze. | https://openaccess.thecvf.com/content/CVPR2021/papers/Kothari_Weakly-Supervised_Physically_Unconstrained_Gaze_Estimation_CVPR_2021_paper.pdf | http://arxiv.org/abs/2105.09803 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Kothari_Weakly-Supervised_Physically_Unconstrained_Gaze_Estimation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Kothari_Weakly-Supervised_Physically_Unconstrained_Gaze_Estimation_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Kothari_Weakly-Supervised_Physically_Unconstrained_CVPR_2021_supplemental.pdf | null |
A Circular-Structured Representation for Visual Emotion Distribution Learning | Jingyuan Yang, Jie Li, Leida Li, Xiumei Wang, Xinbo Gao | Visual Emotion Analysis (VEA) has attracted increasing attention recently with the prevalence of sharing images on social networks. Since human emotions are ambiguous and subjective, it is more reasonable to address VEA in a label distribution learning (LDL) paradigm rather than a single-label classification task. Different from other LDL tasks, there exist intrinsic relationships between emotions and unique characteristics within them, as demonstrated in psychological theories. Inspired by this, we propose a well-grounded circular-structured representation to utilize the prior knowledge for visual emotion distribution learning. To be specific, we first construct an Emotion Circle to unify any emotional state within it. On the proposed Emotion Circle, each emotion distribution is represented with an emotion vector, which is defined with three attributes (i.e., emotion polarity, emotion type, emotion intensity) as well as two properties (i.e., similarity, additivity). Besides, we design a novel Progressive Circular (PC) loss to penalize the dissimilarities between predicted emotion vector and labeled one in a coarse-to-fine manner, which further boosts the learning process in an emotion-specific way. Extensive experiments and comparisons are conducted on public visual emotion distribution datasets, and the results demonstrate that the proposed method outperforms the state-of-the-art methods. | https://openaccess.thecvf.com/content/CVPR2021/papers/Yang_A_Circular-Structured_Representation_for_Visual_Emotion_Distribution_Learning_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Yang_A_Circular-Structured_Representation_for_Visual_Emotion_Distribution_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Yang_A_Circular-Structured_Representation_for_Visual_Emotion_Distribution_Learning_CVPR_2021_paper.html | CVPR 2021 | null | null |
VirTex: Learning Visual Representations From Textual Annotations | Karan Desai, Justin Johnson | The de-facto approach to many vision tasks is to start from pretrained visual representations, typically learned via supervised training on ImageNet. Recent methods have explored unsupervised pretraining to scale to vast quantities of unlabeled images. In contrast, we aim to learn high-quality visual representations from fewer images. To this end we revisit supervised pretraining, and seek data-efficient alternatives to classification-based pretraining. We propose VirTex -- a pretraining approach using semantically dense captions to learn visual representations. We train convolutional networks from scratch on COCO Captions, and transfer them to downstream recognition tasks including image classification, object detection, and instance segmentation. On all tasks, VirTex yields features that match or exceed those learned on ImageNet -- supervised or unsupervised -- despite using up to ten times fewer images. | https://openaccess.thecvf.com/content/CVPR2021/papers/Desai_VirTex_Learning_Visual_Representations_From_Textual_Annotations_CVPR_2021_paper.pdf | http://arxiv.org/abs/2006.06666 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Desai_VirTex_Learning_Visual_Representations_From_Textual_Annotations_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Desai_VirTex_Learning_Visual_Representations_From_Textual_Annotations_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Desai_VirTex_Learning_Visual_CVPR_2021_supplemental.pdf | null |
MASA-SR: Matching Acceleration and Spatial Adaptation for Reference-Based Image Super-Resolution | Liying Lu, Wenbo Li, Xin Tao, Jiangbo Lu, Jiaya Jia | Reference-based image super-resolution (RefSR) has shown promising success in recovering high-frequency details by utilizing an external reference image (Ref). In this task, texture details are transferred from the Ref image to the low-resolution (LR) image according to their point- or patch-wise correspondence. Therefore, high-quality correspondence matching is critical. It is also desired to be computationally efficient. Besides, existing RefSR methods tend to ignore the potential large disparity in distributions between the LR and Ref images, which hurts the effectiveness of the information utilization. In this paper, we propose the MASA network for RefSR, where two novel modules are designed to address these problems. The proposed Match & Extraction Module significantly reduces the computational cost by a coarse-to-fine correspondence matching scheme. The Spatial Adaptation Module learns the difference of distribution between the LR and Ref images, and remaps the distribution of Ref features to that of LR features in a spatially adaptive way. This scheme makes the network robust to handle different reference images. Extensive quantitative and qualitative experiments validate the effectiveness of our proposed model. | https://openaccess.thecvf.com/content/CVPR2021/papers/Lu_MASA-SR_Matching_Acceleration_and_Spatial_Adaptation_for_Reference-Based_Image_Super-Resolution_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Lu_MASA-SR_Matching_Acceleration_and_Spatial_Adaptation_for_Reference-Based_Image_Super-Resolution_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Lu_MASA-SR_Matching_Acceleration_and_Spatial_Adaptation_for_Reference-Based_Image_Super-Resolution_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Lu_MASA-SR_Matching_Acceleration_CVPR_2021_supplemental.pdf | null |
Spatiotemporal Contrastive Video Representation Learning | Rui Qian, Tianjian Meng, Boqing Gong, Ming-Hsuan Yang, Huisheng Wang, Serge Belongie, Yin Cui | We present a self-supervised Contrastive Video Representation Learning (CVRL) method to learn spatiotemporal visual representations from unlabeled videos. Our representations are learned using a contrastive loss, where two augmented clips from the same short video are pulled together in the embedding space, while clips from different videos are pushed away. We study what makes for good data augmentations for video self-supervised learning and find that both spatial and temporal information are crucial. We carefully design data augmentations involving spatial and temporal cues. Concretely, we propose a temporally consistent spatial augmentation method to impose strong spatial augmentations on each frame of the video while maintaining the temporal consistency across frames. We also propose a sampling-based temporal augmentation method to avoid overly enforcing invariance on clips that are distant in time. On Kinetics-600, a linear classifier trained on the representations learned by CVRL achieves 70.4% top-1 accuracy with a 3D-ResNet-50 (R3D-50) backbone, outperforming ImageNet supervised pre-training by 15.7% and SimCLR unsupervised pre-training by 18.8% using the same inflated R3D-50. The performance of CVRL can be further improved to 72.9% with a larger R3D-152 (2x filters) backbone, significantly closing the gap between unsupervised and supervised video representation learning. Our code and models will be available at https://github.com/tensorflow/models/tree/master/official/. | https://openaccess.thecvf.com/content/CVPR2021/papers/Qian_Spatiotemporal_Contrastive_Video_Representation_Learning_CVPR_2021_paper.pdf | http://arxiv.org/abs/2008.03800 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Qian_Spatiotemporal_Contrastive_Video_Representation_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Qian_Spatiotemporal_Contrastive_Video_Representation_Learning_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Qian_Spatiotemporal_Contrastive_Video_CVPR_2021_supplemental.pdf | null |
Scaled-YOLOv4: Scaling Cross Stage Partial Network | Chien-Yao Wang, Alexey Bochkovskiy, Hong-Yuan Mark Liao | We show that the YOLOv4 object detection neural network based on the CSP approach, scales both up and down and is applicable to small and large networks while maintaining optimal speed and accuracy. We propose a network scaling approach that modifies not only the depth, width, resolution, but also structure of the network. YOLOv4-large model achieves state-of-the-art results: 55.5% AP (73.4% AP50) for the MS COCO dataset at a speed of 16 FPS on Tesla V100, while with the test time augmentation, YOLOv4-large achieves 56.0% AP (73.3 AP50). To the best of our knowledge, this is currently the highest accuracy on the COCO dataset among any published work. The YOLOv4-tiny model achieves 22.0% AP (42.0% AP50) at a speed of 443 FPS on RTX 2080Ti, while by using TensorRT, batch size = 4 and FP16-precision the YOLOv4-tiny achieves 1774 FPS. | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_Scaled-YOLOv4_Scaling_Cross_Stage_Partial_Network_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Scaled-YOLOv4_Scaling_Cross_Stage_Partial_Network_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Scaled-YOLOv4_Scaling_Cross_Stage_Partial_Network_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wang_Scaled-YOLOv4_Scaling_Cross_CVPR_2021_supplemental.pdf | null |
Quantifying Explainers of Graph Neural Networks in Computational Pathology | Guillaume Jaume, Pushpak Pati, Behzad Bozorgtabar, Antonio Foncubierta, Anna Maria Anniciello, Florinda Feroce, Tilman Rau, Jean-Philippe Thiran, Maria Gabrani, Orcun Goksel | Explainability of deep learning methods is imperative to facilitate their clinical adoption in digital pathology. However, popular deep learning methods and explainability techniques (explainers) based on pixel-wise processing disregard biological entities' notion, thus complicating comprehension by pathologists. In this work, we address this by adopting biological entity-based graph processing and graph explainers enabling explanations accessible to pathologists. In this context, a major challenge becomes to discern meaningful explainers, particularly in a standardized and quantifiable fashion. To this end, we propose herein a set of novel quantitative metrics based on statistics of class separability using pathologically measurable concepts to characterize graph explainers. We employ the proposed metrics to evaluate three types of graph explainers, namely the layer-wise relevance propagation, gradient-based saliency, and graph pruning approaches, to explain Cell-Graph representations for Breast Cancer Subtyping. The proposed metrics are also applicable in other domains by using domain-specific intuitive concepts. We validate the qualitative and quantitative findings on the BRACS dataset, a large cohort of breast cancer RoIs, by expert pathologists. The code and models will be released upon acceptance. | https://openaccess.thecvf.com/content/CVPR2021/papers/Jaume_Quantifying_Explainers_of_Graph_Neural_Networks_in_Computational_Pathology_CVPR_2021_paper.pdf | http://arxiv.org/abs/2011.12646 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Jaume_Quantifying_Explainers_of_Graph_Neural_Networks_in_Computational_Pathology_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Jaume_Quantifying_Explainers_of_Graph_Neural_Networks_in_Computational_Pathology_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Jaume_Quantifying_Explainers_of_CVPR_2021_supplemental.pdf | null |
Knowledge Evolution in Neural Networks | Ahmed Taha, Abhinav Shrivastava, Larry S. Davis | Deep learning relies on the availability of a large corpus of data (labeled or unlabeled). Thus, one challenging unsettled question is: how to train a deep network on a relatively small dataset? To tackle this question, we propose an evolution-inspired training approach to boost performance on relatively small datasets. The knowledge evolution (KE) approach splits a deep network into two hypotheses: the fit-hypothesis and the reset-hypothesis. We iteratively evolve the knowledge inside the fit-hypothesis by perturbing the reset-hypothesis for multiple generations. This approach not only boosts performance, but also learns a slim network with a smaller inference cost. KE integrates seamlessly with both vanilla and residual convolutional networks. KE reduces both overfitting and the burden for data collection. We evaluate KE on various network architectures and loss functions. We evaluate KE using relatively small datasets (e.g., CUB-200) and randomly initialized deep networks. KE achieves an absolute 21% improvement margin on a state-of-the-art baseline. This performance improvement is accompanied by a relative 73% reduction in inference cost. KE achieves state-of-the-art results on classification and metric learning benchmarks. | https://openaccess.thecvf.com/content/CVPR2021/papers/Taha_Knowledge_Evolution_in_Neural_Networks_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.05152 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Taha_Knowledge_Evolution_in_Neural_Networks_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Taha_Knowledge_Evolution_in_Neural_Networks_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Taha_Knowledge_Evolution_in_CVPR_2021_supplemental.pdf | null |
Revisiting Knowledge Distillation: An Inheritance and Exploration Framework | Zhen Huang, Xu Shen, Jun Xing, Tongliang Liu, Xinmei Tian, Houqiang Li, Bing Deng, Jianqiang Huang, Xian-Sheng Hua | Knowledge Distillation (KD) is a popular technique to transfer knowledge from a teacher model or ensemble to a student model. Its success is generally attributed to the privileged information on similarities/consistency between the class distributions or intermediate feature representations of the teacher model and the student model. However, directly pushing the student model to mimic the probabilities/features of the teacher model to a large extent limits the student model in learning undiscovered knowledge/features. In this paper, we propose a novel inheritance and exploration knowledge distillation framework (IE-KD), in which a student model is split into two parts -- inheritance and exploration. The inheritance part is learned with a similarity loss to transfer the existing learned knowledge from the teacher model to the student model, while the exploration part is encouraged to learn representations different from the inherited ones with a dis-similarity loss. Our IE-KD framework is generic and can be easily combined with existing distillation or mutual learning methods for training deep neural networks. Extensive experiments demonstrate that these two parts can jointly push the student model to learn more diversified and effective representations, and our IE-KD can be a general technique to improve the student network to achieve SOTA performance. Furthermore, by applying our IE-KD to the training of two networks, the performance of both can be improved w.r.t. deep mutual learning. | https://openaccess.thecvf.com/content/CVPR2021/papers/Huang_Revisiting_Knowledge_Distillation_An_Inheritance_and_Exploration_Framework_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Huang_Revisiting_Knowledge_Distillation_An_Inheritance_and_Exploration_Framework_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Huang_Revisiting_Knowledge_Distillation_An_Inheritance_and_Exploration_Framework_CVPR_2021_paper.html | CVPR 2021 | null | null |
Temporally-Weighted Hierarchical Clustering for Unsupervised Action Segmentation | Saquib Sarfraz, Naila Murray, Vivek Sharma, Ali Diba, Luc Van Gool, Rainer Stiefelhagen | Action segmentation refers to inferring boundaries of semantically consistent visual concepts in videos and is an important requirement for many video understanding tasks. For this and other video understanding tasks, supervised approaches have achieved encouraging performance but require a high volume of detailed, frame-level, annotations. We present a fully automatic and unsupervised approach for segmenting actions in a video that does not require any training. Our proposal is an effective temporally-weighted hierarchical clustering algorithm that can group semantically consistent frames of the video. The main finding is that representing a video with a 1-nearest neighbor graph by taking into account the time progression is sufficient to form semantically and temporally consistent clusters of frames where each cluster may represent some action in the video. Additionally, we establish strong unsupervised baselines for action segmentation and show significant performance improvements over published unsupervised methods on five challenging action segmentation datasets. Our code is available at https://github.com/ssarfraz/FINCH-Clustering/tree/master/TW-FINCH | https://openaccess.thecvf.com/content/CVPR2021/papers/Sarfraz_Temporally-Weighted_Hierarchical_Clustering_for_Unsupervised_Action_Segmentation_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.11264 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Sarfraz_Temporally-Weighted_Hierarchical_Clustering_for_Unsupervised_Action_Segmentation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Sarfraz_Temporally-Weighted_Hierarchical_Clustering_for_Unsupervised_Action_Segmentation_CVPR_2021_paper.html | CVPR 2021 | null | null |
SMURF: Self-Teaching Multi-Frame Unsupervised RAFT With Full-Image Warping | Austin Stone, Daniel Maurer, Alper Ayvaci, Anelia Angelova, Rico Jonschkowski | We present SMURF, a method for unsupervised learning of optical flow that improves state of the art on all benchmarks by 36% to 40% and even outperforms several supervised approaches such as PWC-Net and FlowNet2. Our method integrates architecture improvements from supervised optical flow, i.e. the RAFT model, with new ideas for unsupervised learning that include a novel unsupervised sequence loss and self-supervision loss, a technique for handling out-of-frame motion, and an approach for learning effectively from multi-frame video data while still only requiring two frames for inference. | https://openaccess.thecvf.com/content/CVPR2021/papers/Stone_SMURF_Self-Teaching_Multi-Frame_Unsupervised_RAFT_With_Full-Image_Warping_CVPR_2021_paper.pdf | http://arxiv.org/abs/2105.07014 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Stone_SMURF_Self-Teaching_Multi-Frame_Unsupervised_RAFT_With_Full-Image_Warping_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Stone_SMURF_Self-Teaching_Multi-Frame_Unsupervised_RAFT_With_Full-Image_Warping_CVPR_2021_paper.html | CVPR 2021 | null | null |
Glancing at the Patch: Anomaly Localization With Global and Local Feature Comparison | Shenzhi Wang, Liwei Wu, Lei Cui, Yujun Shen | Anomaly localization, with the purpose to segment the anomalous regions within images, is challenging due to the large variety of anomaly types. Existing methods typically train deep models by treating the entire image as a whole yet put little effort into learning the local distribution, which is vital for this pixel-precise task. In this work, we propose an unsupervised patch-based approach that gives due consideration to both the global and local information. More concretely, we employ a Local-Net and Global-Net to extract features from any individual patch and its surrounding respectively. Global-Net is trained with the purpose to mimic the local feature such that we can easily detect an abnormal patch when its feature mismatches that from the context. We further introduce an Inconsistency Anomaly Detection (IAD) head and a Distortion Anomaly Detection (DAD) head to sufficiently spot the discrepancy between global and local features. A scoring function derived from the multi-head design facilitates high-precision anomaly localization. Extensive experiments on a couple of real-world datasets suggest that our approach outperforms state-of-the-art competitors by a sufficiently large margin. | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_Glancing_at_the_Patch_Anomaly_Localization_With_Global_and_Local_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Glancing_at_the_Patch_Anomaly_Localization_With_Global_and_Local_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Glancing_at_the_Patch_Anomaly_Localization_With_Global_and_Local_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wang_Glancing_at_the_CVPR_2021_supplemental.pdf | null |
Single-View 3D Object Reconstruction From Shape Priors in Memory | Shuo Yang, Min Xu, Haozhe Xie, Stuart Perry, Jiahao Xia | Existing methods for single-view 3D object reconstruction directly learn to transform image features into 3D representations. However, these methods are vulnerable to images containing noisy backgrounds and heavy occlusions because the extracted image features do not contain enough information to reconstruct high-quality 3D shapes. Humans routinely use incomplete or noisy visual cues from an image to retrieve similar 3D shapes from their memory and reconstruct the 3D shape of an object. Inspired by this, we propose a novel method, named Mem3D, that explicitly constructs shape priors to supplement the missing information in the image. Specifically, the shape priors are in the forms of "image-voxel" pairs in the memory network, which is stored by a well-designed writing strategy during training. We also propose a voxel triplet loss function that helps to retrieve the precise 3D shapes that are highly related to the input image from shape priors. The LSTM-based shape encoder is introduced to extract information from the retrieved 3D shapes, which are useful in recovering the 3D shape of an object that is heavily occluded or in complex environments. Experimental results demonstrate that Mem3D significantly improves reconstruction quality and performs favorably against state-of-the-art methods on the ShapeNet and Pix3D datasets. | https://openaccess.thecvf.com/content/CVPR2021/papers/Yang_Single-View_3D_Object_Reconstruction_From_Shape_Priors_in_Memory_CVPR_2021_paper.pdf | http://arxiv.org/abs/2003.03711 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Yang_Single-View_3D_Object_Reconstruction_From_Shape_Priors_in_Memory_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Yang_Single-View_3D_Object_Reconstruction_From_Shape_Priors_in_Memory_CVPR_2021_paper.html | CVPR 2021 | null | null |
Recognizing Actions in Videos From Unseen Viewpoints | AJ Piergiovanni, Michael S. Ryoo | Standard methods for video recognition use large CNNs designed to capture spatio-temporal data. However, training these models requires a large amount of labeled training data, containing a wide variety of actions, scenes, settings and camera viewpoints. In this paper, we show that current convolutional neural network models are unable to recognize actions from camera viewpoints not present in their training data (i.e., unseen view action recognition). To address this, we develop approaches based on 3D pose and introduce a new geometric convolutional layer that can learn viewpoint invariant representations. Further, we introduce a new, challenging dataset for unseen view recognition and show the approaches ability to learn viewpoint invariant representations. | https://openaccess.thecvf.com/content/CVPR2021/papers/Piergiovanni_Recognizing_Actions_in_Videos_From_Unseen_Viewpoints_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.16516 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Piergiovanni_Recognizing_Actions_in_Videos_From_Unseen_Viewpoints_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Piergiovanni_Recognizing_Actions_in_Videos_From_Unseen_Viewpoints_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Piergiovanni_Recognizing_Actions_in_CVPR_2021_supplemental.pdf | null |
Perceptual Indistinguishability-Net (PI-Net): Facial Image Obfuscation With Manipulable Semantics | Jia-Wei Chen, Li-Ju Chen, Chia-Mu Yu, Chun-Shien Lu | With the growing use of camera devices, the industry has many image datasets that provide more opportunities for collaboration between the machine learning community and industry. However, the sensitive information in the datasets discourages data owners from releasing these datasets. Despite recent research devoted to removing sensitive information from images, they provide neither meaningful privacy-utility trade-off nor provable privacy guarantees. In this study, with the consideration of the perceptual similarity, we propose perceptual indistinguishability (PI) as a formal privacy notion particularly for images. We also propose PI-Net, a privacy-preserving mechanism that achieves image obfuscation with PI guarantee. Our study shows that PI-Net achieves significantly better privacy utility trade-off through public image data. | https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_Perceptual_Indistinguishability-Net_PI-Net_Facial_Image_Obfuscation_With_Manipulable_Semantics_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.01753 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Perceptual_Indistinguishability-Net_PI-Net_Facial_Image_Obfuscation_With_Manipulable_Semantics_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Perceptual_Indistinguishability-Net_PI-Net_Facial_Image_Obfuscation_With_Manipulable_Semantics_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Chen_Perceptual_Indistinguishability-Net_PI-Net_CVPR_2021_supplemental.pdf | null |
To the Point: Efficient 3D Object Detection in the Range Image With Graph Convolution Kernels | Yuning Chai, Pei Sun, Jiquan Ngiam, Weiyue Wang, Benjamin Caine, Vijay Vasudevan, Xiao Zhang, Dragomir Anguelov | 3D object detection is vital for many robotics applications. For tasks where a 2D perspective range image exists, we propose to learn a 3D representation directly from this range image view. To this end, we designed a 2D convolutional network architecture that carries the 3D spherical coordinates of each pixel throughout the network. Its layers can consume any arbitrary convolution kernel in place of the default inner product kernel and exploit the underlying local geometry around each pixel. We outline four such kernels: a dense kernel according to the bag-of-words paradigm, and three graph kernels inspired by recent graph neural network advances: the Transformer, the PointNet, and the Edge Convolution. We also explore cross-modality fusion with the camera image, facilitated by operating in the perspective range image view. Our method performs competitively on the Waymo Open Dataset and improves the state-of-the-art AP for pedestrian detection from 69.7% to 75.5%. It is also efficient in that our smallest model, which still outperforms the popular PointPillars in quality, requires 180 times fewer FLOPS and model parameters. | https://openaccess.thecvf.com/content/CVPR2021/papers/Chai_To_the_Point_Efficient_3D_Object_Detection_in_the_Range_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Chai_To_the_Point_Efficient_3D_Object_Detection_in_the_Range_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Chai_To_the_Point_Efficient_3D_Object_Detection_in_the_Range_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Chai_To_the_Point_CVPR_2021_supplemental.pdf | null |
Coarse-To-Fine Domain Adaptive Semantic Segmentation With Photometric Alignment and Category-Center Regularization | Haoyu Ma, Xiangru Lin, Zifeng Wu, Yizhou Yu | Unsupervised domain adaptation (UDA) in semantic segmentation is a fundamental yet promising task relieving the need for laborious annotation works. However, the domain shifts/discrepancies problem in this task compromise the final segmentation performance. Based on our observation, the main causes of the domain shifts are differences in imaging conditions, called image-level domain shifts, and differences in object category configurations called category-level domain shifts. In this paper, we propose a novel UDA pipeline that unifies image-level alignment and category-level feature distribution regularization in a coarse-to-fine manner. Specifically, on the coarse side, we propose a photometric alignment module that aligns an image in the source domain with a reference image from the target domain using a set of image-level operators; on the fine side, we propose a category-oriented triplet loss that imposes a soft constraint to regularize category centers in the source domain and a self-supervised consistency regularization method in the target domain. Experimental results show that our proposed pipeline improves the generalization capability of the final segmentation model and significantly outperforms all previous state-of-the-arts. | https://openaccess.thecvf.com/content/CVPR2021/papers/Ma_Coarse-To-Fine_Domain_Adaptive_Semantic_Segmentation_With_Photometric_Alignment_and_Category-Center_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.13041 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Ma_Coarse-To-Fine_Domain_Adaptive_Semantic_Segmentation_With_Photometric_Alignment_and_Category-Center_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Ma_Coarse-To-Fine_Domain_Adaptive_Semantic_Segmentation_With_Photometric_Alignment_and_Category-Center_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Ma_Coarse-To-Fine_Domain_Adaptive_CVPR_2021_supplemental.pdf | null |
Self-Supervised Wasserstein Pseudo-Labeling for Semi-Supervised Image Classification | Fariborz Taherkhani, Ali Dabouei, Sobhan Soleymani, Jeremy Dawson, Nasser M. Nasrabadi | The goal is to use Wasserstein metric to provide pseudo labels for the unlabeled images to train a Convolutional Neural Networks (CNN) in a Semi-Supervised Learning (SSL) manner for the classification task. The basic premise in our method is that the discrepancy between two discrete empirical measures (e.g., clusters) which come from the same or similar distribution is expected to be less than the case where these measures come from completely two different distributions. In our proposed method, we first pre-train our CNN using a self-supervised learning method to make a cluster assumption on the unlabeled images. Next, inspired by the Wasserstein metric which considers the geometry of the metric space to provide a natural notion of similarity between discrete empirical measures, we leverage it to cluster the unlabeled images and then match the clusters to their similar class of labeled images to provide a pseudo label for the data within each cluster. We have evaluated and compared our method with state-of-the-art SSL methods on the standard datasets to demonstrate its effectiveness. | https://openaccess.thecvf.com/content/CVPR2021/papers/Taherkhani_Self-Supervised_Wasserstein_Pseudo-Labeling_for_Semi-Supervised_Image_Classification_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Taherkhani_Self-Supervised_Wasserstein_Pseudo-Labeling_for_Semi-Supervised_Image_Classification_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Taherkhani_Self-Supervised_Wasserstein_Pseudo-Labeling_for_Semi-Supervised_Image_Classification_CVPR_2021_paper.html | CVPR 2021 | null | null |
MeanShift++: Extremely Fast Mode-Seeking With Applications to Segmentation and Object Tracking | Jennifer Jang, Heinrich Jiang | MeanShift is a popular mode-seeking clustering algorithm used in a wide range of applications in machine learning. However, it is known to be prohibitively slow, with quadratic runtime per iteration. We propose MeanShift++, an extremely fast mode-seeking algorithm based on MeanShift that uses a grid-based approach to speed up the mean shift step, replacing the computationally expensive neighbors search with a density-weighted mean of adjacent grid cells. In addition, we show that this grid-based technique for density estimation comes with theoretical guarantees. The runtime is linear in the number of points and exponential in dimension, which makes MeanShift++ ideal on low-dimensional applications such as image segmentation and object tracking. We provide extensive experimental analysis showing that MeanShift++ can be more than 10,000x faster than MeanShift with competitive clustering results on benchmark datasets and nearly identical image segmentations as MeanShift. Finally, we show promising results for object tracking. | https://openaccess.thecvf.com/content/CVPR2021/papers/Jang_MeanShift_Extremely_Fast_Mode-Seeking_With_Applications_to_Segmentation_and_Object_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Jang_MeanShift_Extremely_Fast_Mode-Seeking_With_Applications_to_Segmentation_and_Object_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Jang_MeanShift_Extremely_Fast_Mode-Seeking_With_Applications_to_Segmentation_and_Object_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Jang_MeanShift_Extremely_Fast_CVPR_2021_supplemental.pdf | null |
PCLs: Geometry-Aware Neural Reconstruction of 3D Pose With Perspective Crop Layers | Frank Yu, Mathieu Salzmann, Pascal Fua, Helge Rhodin | Local processing is an essential feature of CNNs and other neural network architectures -- it is one of the reasons why they work so well on images where relevant information is, to a large extent, local. However, perspective effects stemming from the projection in a conventional camera vary for different global positions in the image. We introduce Perspective Crop Layers (PCLs) -- a form of perspective crop of the region of interest based on the camera geometry -- and show that accounting for the perspective consistently improves the accuracy of state-of-the-art 3D pose reconstruction methods. PCLs are modular neural network layers, which, when inserted into existing CNN and MLP architectures, deterministically remove the location-dependent perspective effects while leaving end-to-end training and the number of parameters of the underlying neural network unchanged. We demonstrate that PCL leads to improved 3D human pose reconstruction accuracy for CNN architectures that use cropping operations, such as spatial transformer networks (STN), and, somewhat surprisingly, MLPs used for 2D-to-3D keypoint lifting. Our conclusion is that it is important to utilize camera calibration information when available, for classical and deep-learning-based computer vision alike. PCL offers an easy way to improve the accuracy of existing 3D reconstruction networks by making them geometry aware. Our code is publicly available at github.com/yu-frank/PerspectiveCropLayers. | https://openaccess.thecvf.com/content/CVPR2021/papers/Yu_PCLs_Geometry-Aware_Neural_Reconstruction_of_3D_Pose_With_Perspective_Crop_CVPR_2021_paper.pdf | http://arxiv.org/abs/2011.13607 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Yu_PCLs_Geometry-Aware_Neural_Reconstruction_of_3D_Pose_With_Perspective_Crop_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Yu_PCLs_Geometry-Aware_Neural_Reconstruction_of_3D_Pose_With_Perspective_Crop_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yu_PCLs_Geometry-Aware_Neural_CVPR_2021_supplemental.zip | null |
Partially View-Aligned Representation Learning With Noise-Robust Contrastive Loss | Mouxing Yang, Yunfan Li, Zhenyu Huang, Zitao Liu, Peng Hu, Xi Peng | In real-world applications, it is common that only a portion of data is aligned across views due to spatial, temporal, or spatiotemporal asynchronism, thus leading to the so-called Partially View-aligned Problem (PVP). To solve such a less-touched problem without the help of labels, we propose simultaneously learning representation and aligning data using a noise-robust contrastive loss. In brief, for each sample from one view, our method aims to identify its within-category counterparts from other views, and thus the cross-view correspondence could be established. As the contrastive learning needs data pairs as input, we construct positive pairs using the known correspondences and negative pairs using random sampling. To alleviate or even eliminate the influence of the false negatives caused by random sampling, we propose a noise-robust contrastive loss that could adaptively prevent the false negatives from dominating the network optimization. To the best of our knowledge, this could be the first successful attempt of enabling contrastive learning robust to noisy labels. In fact, this work might remarkably enrich the learning paradigm with noisy labels. More specifically, the traditional noisy labels are defined as incorrect annotations for the supervised tasks such as classification. In contrast, this work proposes that the view correspondence might be false, which is remarkably different from the widely-accepted definition of noisy label. Extensive experiments show the promising performance of our method comparing with 10 state-of-the-art multi-view approaches in the clustering and classification tasks. The code will be publicly released at https://pengxi.me. | https://openaccess.thecvf.com/content/CVPR2021/papers/Yang_Partially_View-Aligned_Representation_Learning_With_Noise-Robust_Contrastive_Loss_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Yang_Partially_View-Aligned_Representation_Learning_With_Noise-Robust_Contrastive_Loss_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Yang_Partially_View-Aligned_Representation_Learning_With_Noise-Robust_Contrastive_Loss_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yang_Partially_View-Aligned_Representation_CVPR_2021_supplemental.pdf | null |
i3DMM: Deep Implicit 3D Morphable Model of Human Heads | Tarun Yenamandra, Ayush Tewari, Florian Bernard, Hans-Peter Seidel, Mohamed Elgharib, Daniel Cremers, Christian Theobalt | We present the first deep implicit 3D morphable model (i3DMM) of full heads. Unlike earlier morphable face models it not only captures identity-specific geometry, texture, and expressions of the frontal face, but also models the entire head, including hair. We collect a new dataset consisting of 64 people with different expressions and hairstyles to train i3DMM. Our approach has the following favorable properties: (i) It is the first full head morphable model that includes hair. (ii) In contrast to mesh-based models it can be trained on merely rigidly aligned scans, without requiring difficult non-rigid registration. (iii) We design a novel architecture to decouple the shape model into an implicit reference shape and a deformation of this reference shape. With that, dense correspondences between shapes can be learned implicitly. (iv) This architecture allows us to semantically disentangle the geometry and color components, as color is learned in the reference space. Geometry is further disentangled as identity, expressions, and hairstyle, while color is disentangled as identity and hairstyle components. We show the merits of i3DMM using ablation studies, comparisons to state-of-the-art models, and applications such as semantic head editing and texture transfer. We will make our model publicly available. | https://openaccess.thecvf.com/content/CVPR2021/papers/Yenamandra_i3DMM_Deep_Implicit_3D_Morphable_Model_of_Human_Heads_CVPR_2021_paper.pdf | http://arxiv.org/abs/2011.14143 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Yenamandra_i3DMM_Deep_Implicit_3D_Morphable_Model_of_Human_Heads_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Yenamandra_i3DMM_Deep_Implicit_3D_Morphable_Model_of_Human_Heads_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yenamandra_i3DMM_Deep_Implicit_CVPR_2021_supplemental.pdf | null |
Searching by Generating: Flexible and Efficient One-Shot NAS With Architecture Generator | Sian-Yao Huang, Wei-Ta Chu | In one-shot NAS, sub-networks need to be searched from the supernet to meet different hardware constraints. However, the search cost is high and N times of searches are needed for N different constraints. In this work, we propose a novel search strategy called architecture generator to search sub-networks by generating them, so that the search process can be much more efficient and flexible. With the trained architecture generator, given target hardware constraints as the input, N good architectures can be generated for N constraints by just one forward pass without re-searching and supernet retraining. Moreover, we propose a novel single-path supernet, called unified supernet, to further improve search efficiency and reduce GPU memory consumption of the architecture generator. With the architecture generator and the unified supernet, we propose a flexible and efficient one-shot NAS framework, called Searching by Generating NAS (SGNAS). With the pre-trained supernt, the search time of SGNAS for N different hardware constraints is only 5 GPU hours, which is 4N times faster than previous SOTA single-path methods. After training from scratch, the top1-accuracy of SGNAS on ImageNet is 77.1%, which is comparable with the SOTAs. The code is available at: https://github.com/eric8607242/SGNAS. | https://openaccess.thecvf.com/content/CVPR2021/papers/Huang_Searching_by_Generating_Flexible_and_Efficient_One-Shot_NAS_With_Architecture_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.07289 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Huang_Searching_by_Generating_Flexible_and_Efficient_One-Shot_NAS_With_Architecture_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Huang_Searching_by_Generating_Flexible_and_Efficient_One-Shot_NAS_With_Architecture_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Huang_Searching_by_Generating_CVPR_2021_supplemental.pdf | null |
Discovering Interpretable Latent Space Directions of GANs Beyond Binary Attributes | Huiting Yang, Liangyu Chai, Qiang Wen, Shuang Zhao, Zixun Sun, Shengfeng He | Generative adversarial networks (GANs) learn to map noise latent vectors to high-fidelity image outputs. It is found that the input latent space shows semantic correlations with the output image space. Recent works aim to interpret the latent space and discover meaningful directions that correspond to human interpretable image transformations. However, these methods either rely on explicit scores of attributes (e.g., memorability) or are restricted to binary ones (e.g., gender), which largely limits the applicability of editing tasks, especially for free-form artistic tasks like style/anime editing. In this paper, we propose an adversarial method, AdvStyle, for discovering interpretable directions in the absence of well-labeled scores or binary attributes. In particular, the proposed adversarial method simultaneously optimizes the discovered directions and the attribute assessor using the target attribute data as positive samples, while the generated ones being negative. In this way, arbitrary attributes can be edited by collecting positive data only, and the proposed method learns a controllable representation enabling manipulation of non-binary attributes like anime styles and facial characteristics. Moreover, the proposed learning strategy attenuates the entanglement between attributes, such that multiattribute manipulation can be easily achieved without any additional constraint. Furthermore, we reveal several interesting semantics with the involuntarily learned negative directions. Extensive experiments on 9 anime attributes and 7 human attributes demonstrate the effectiveness of our adversarial approach qualitatively and quantitatively. Code is available at https://github.com/BERYLSHEEP/AdvStyle. | https://openaccess.thecvf.com/content/CVPR2021/papers/Yang_Discovering_Interpretable_Latent_Space_Directions_of_GANs_Beyond_Binary_Attributes_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Yang_Discovering_Interpretable_Latent_Space_Directions_of_GANs_Beyond_Binary_Attributes_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Yang_Discovering_Interpretable_Latent_Space_Directions_of_GANs_Beyond_Binary_Attributes_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yang_Discovering_Interpretable_Latent_CVPR_2021_supplemental.pdf | null |
ForgeryNet: A Versatile Benchmark for Comprehensive Forgery Analysis | Yinan He, Bei Gan, Siyu Chen, Yichun Zhou, Guojun Yin, Luchuan Song, Lu Sheng, Jing Shao, Ziwei Liu | The rapid progress of photorealistic synthesis techniques has reached at a critical point where the boundary between real and manipulated images starts to blur. Thus, benchmarking and advancing digital forgery analysis have become a pressing issue. However, existing face forgery datasets either have limited diversity or only support coarse-grained analysis. To counter this emerging threat, we construct the ForgeryNet dataset, an extremely large face forgery dataset with unified annotations in image- and video-level data across four tasks: 1) Image Forgery Classification, including two-way (real / fake), three-way (real / fake with identity-replaced forgery approaches / fake with identity-remained forgery approaches), and n-way (real and 15 respective forgery approaches) classification. 2) Spatial Forgery Localization, which segments the manipulated area of fake images compared to their corresponding source real images. 3) Video Forgery Classification, which re-defines the video-level forgery classification with manipulated frames in random positions. This task is important because attackers in real world are free to manipulate any target frame. and 4) Temporal Forgery Localization, to localize the temporal segments which are manipulated. ForgeryNet is by far the largest publicly available deep face forgery dataset in terms of data-scale (2.9 million images, 221,247 videos), manipulations (7 image-level approaches, 8 video-level approaches), perturbations (36 independent and more mixed perturbations) and annotations (6.3 million classification labels, 2.9 million manipulated area annotations and 221,247 temporal forgery segment labels). We perform extensive benchmarking and studies of existing face forensics methods and obtain several valuable observations.We hope that the scale, quality, and variety of ForgeryNet dataset will foster further research and innovation in the area of face forgery classification, spatial and temporal forgery localization etc. | https://openaccess.thecvf.com/content/CVPR2021/papers/He_ForgeryNet_A_Versatile_Benchmark_for_Comprehensive_Forgery_Analysis_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.05630 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/He_ForgeryNet_A_Versatile_Benchmark_for_Comprehensive_Forgery_Analysis_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/He_ForgeryNet_A_Versatile_Benchmark_for_Comprehensive_Forgery_Analysis_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/He_ForgeryNet_A_Versatile_CVPR_2021_supplemental.pdf | null |
Blocks-World Cameras | Jongho Lee, Mohit Gupta | For several vision and robotics applications, 3D geometry of man-made environments such as indoor scenes can be represented with a small number of dominant planes. However, conventional 3D vision techniques typically first acquire dense 3D point clouds before estimating the compact piece-wise planar representations (e.g., by plane-fitting). This approach is costly, both in terms of acquisition and computational requirements, and potentially unreliable due to noisy point clouds. We propose Blocks-World Cameras, a class of imaging systems which directly recover dominant planes of piece-wise planar scenes (Blocks-World), without requiring point clouds. The Blocks-World Cameras are based on a structured-light system projecting a single pattern with a sparse set of cross-shaped features. We develop a novel geometric algorithm for recovering scene planes without explicit correspondence matching, thereby avoiding computationally intensive search or optimization routines. The proposed approach has low device and computational complexity, and requires capturing only one or two images. We demonstrate highly efficient and precise planar-scene sensing with simulations and real experiments, across various imaging conditions, including defocus blur, large lighting variations, ambient illumination, and scene clutter. | https://openaccess.thecvf.com/content/CVPR2021/papers/Lee_Blocks-World_Cameras_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Lee_Blocks-World_Cameras_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Lee_Blocks-World_Cameras_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Lee_Blocks-World_Cameras_CVPR_2021_supplemental.pdf | null |
The Affective Growth of Computer Vision | Norman Makoto Su, David J. Crandall | The success of deep learning has led to intense growth and interest in computer vision, along with concerns about its potential impact on society. Yet we know little about how these changes have affected the people that research and practice computer vision: we as a community spend so much effort trying to replicate the abilities of humans, but so little time considering the impact of this work on ourselves. In this paper, we report on a study in which we asked computer vision researchers and practitioners to write stories about emotionally-salient events that happened to them. Our analysis of over 50 responses found tremendous affective (emotional) strain in the computer vision community. While many describe excitement and success, we found strikingly frequent feelings of isolation, cynicism, apathy, and exasperation over the state of the field. This is especially true among people who do not share the unbridled enthusiasm for normative standards for computer vision research and who do not see themselves as part of the "in-crowd." Our findings suggest that these feelings are closely tied to the kinds of research and professional practices now expected in computer vision. We argue that as a community with significant stature, we need to work towards an inclusive culture that makes transparent and addresses the real emotional toil of its members. | https://openaccess.thecvf.com/content/CVPR2021/papers/Su_The_Affective_Growth_of_Computer_Vision_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Su_The_Affective_Growth_of_Computer_Vision_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Su_The_Affective_Growth_of_Computer_Vision_CVPR_2021_paper.html | CVPR 2021 | null | null |
Lifelong Person Re-Identification via Adaptive Knowledge Accumulation | Nan Pu, Wei Chen, Yu Liu, Erwin M. Bakker, Michael S. Lew | Person ReID methods always learn through a stationary domain that is fixed by the choice of a given dataset. In many contexts (e.g., lifelong learning), those methods are ineffective because the domain is continually changing in which case incremental learning over multiple domains is required potentially. In this work we explore a new and challenging ReID task, namely lifelong person re-identification (LReID), which enables to learn continuously across multiple domains and even generalise on new and unseen domains. Following the cognitive processes in the human brain, we design an Adaptive Knowledge Accumulation (AKA) framework that is endowed with two crucial abilities: knowledge representation and knowledge operation. Our method alleviates catastrophic forgetting on seen domains and demonstrates the ability to generalize to unseen domains. Correspondingly, we also provide a new and large-scale benchmark for LReID. Extensive experiments demonstrate our method outperforms other competitors by a margin of 5.8% mAP in generalising evaluation. | https://openaccess.thecvf.com/content/CVPR2021/papers/Pu_Lifelong_Person_Re-Identification_via_Adaptive_Knowledge_Accumulation_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.12462 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Pu_Lifelong_Person_Re-Identification_via_Adaptive_Knowledge_Accumulation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Pu_Lifelong_Person_Re-Identification_via_Adaptive_Knowledge_Accumulation_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Pu_Lifelong_Person_Re-Identification_CVPR_2021_supplemental.zip | null |
Omnimatte: Associating Objects and Their Effects in Video | Erika Lu, Forrester Cole, Tali Dekel, Andrew Zisserman, William T. Freeman, Michael Rubinstein | Computer vision has become increasingly better at segmenting objects in images and videos; however, scene effects related to the objects -- shadows, reflections, generated smoke, etc. -- are typically overlooked. Identifying such scene effects and associating them with the objects producing them is important for improving our fundamental understanding of visual scenes, and applications such as removing, duplicating, or enhancing objects in video. We take a step towards solving this novel problem of automatically associating objects with their effects in video. Given an ordinary video and a rough segmentation mask over time of one or more subjects of interest, we estimate an omnimatte for each subject -- an alpha matte and color image that includes the subject along with all its related time-varying scene elements. Our model is trained only on the input video in a self-supervised manner, without any manual labels, and is generic -- it produces omnimattes automatically for arbitrary objects and a variety of effects. We show results on real-world videos containing interactions between different types of subjects (cars, animals, people) and complex effects, ranging from semi-transparent smoke and reflections to fully opaque objects attached to the subject. | https://openaccess.thecvf.com/content/CVPR2021/papers/Lu_Omnimatte_Associating_Objects_and_Their_Effects_in_Video_CVPR_2021_paper.pdf | http://arxiv.org/abs/2105.06993 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Lu_Omnimatte_Associating_Objects_and_Their_Effects_in_Video_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Lu_Omnimatte_Associating_Objects_and_Their_Effects_in_Video_CVPR_2021_paper.html | CVPR 2021 | null | null |
Detecting Human-Object Interaction via Fabricated Compositional Learning | Zhi Hou, Baosheng Yu, Yu Qiao, Xiaojiang Peng, Dacheng Tao | Human-Object Interaction (HOI) detection, inferring the relationships between human and objects from images/videos, is a fundamental task for high-level scene understanding. However, HOI detection usually suffers from the open long-tailed nature of interactions with objects, while human has extremely powerful compositional perception ability to cognize rare or unseen HOI samples. Inspired by this, we devise a novel HOI compositional learning framework, termed as Fabricated Compositional Learning (FCL), to address the problem of open long-tailed HOI detection. Specifically, we introduce an object fabricator to generate effective object representations, and then combine verbs and fabricated objects to compose new HOI samples. With the proposed object fabricator, we are able to generate large-scale HOI samples for rare and unseen categories to alleviate the open long-tailed issues in HOI detection. Extensive experiments on the most popular HOI detection dataset, HICO-DET, demonstrate the effectiveness of the proposed method for imbalanced HOI detection and significantly improve the state-of-the-art performance on rare and unseen HOI categories. | https://openaccess.thecvf.com/content/CVPR2021/papers/Hou_Detecting_Human-Object_Interaction_via_Fabricated_Compositional_Learning_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.08214 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Hou_Detecting_Human-Object_Interaction_via_Fabricated_Compositional_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Hou_Detecting_Human-Object_Interaction_via_Fabricated_Compositional_Learning_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Hou_Detecting_Human-Object_Interaction_CVPR_2021_supplemental.pdf | null |
Memory-Efficient Network for Large-Scale Video Compressive Sensing | Ziheng Cheng, Bo Chen, Guanliang Liu, Hao Zhang, Ruiying Lu, Zhengjue Wang, Xin Yuan | Video snapshot compressive imaging (SCI) captures a sequence of video frames in a single shot using a 2D detector. The underlying principle is that during one exposure time, different masks are imposed on the high-speed scene to form a compressed measurement. With the knowledge of masks, optimization algorithms or deep learning methods are employed to reconstruct the desired high-speed video frames from this snapshot measurement. Unfortunately, though these methods can achieve decent results, the long running time of optimization algorithms or huge training memory occupation of deep networks still preclude them in practical applications. In this paper, we develop a memory-efficient network for large-scale video SCI based on multi-group reversible 3D convolutional neural networks. In addition to the basic model for the grayscale SCI system, we take one step further to combine demosaicing and SCI reconstruction to directly recover color video from Bayer measurements. Extensive results on both simulation and real data captured by SCI cameras demonstrate that our proposed model outperforms previous state-of-the-art with less memory and thus can be used in large-scale problems. The code is at https: //github.com/BoChenGroup/RevSCI-net. | https://openaccess.thecvf.com/content/CVPR2021/papers/Cheng_Memory-Efficient_Network_for_Large-Scale_Video_Compressive_Sensing_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.03089 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Cheng_Memory-Efficient_Network_for_Large-Scale_Video_Compressive_Sensing_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Cheng_Memory-Efficient_Network_for_Large-Scale_Video_Compressive_Sensing_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Cheng_Memory-Efficient_Network_for_CVPR_2021_supplemental.pdf | null |
Deep Optimized Priors for 3D Shape Modeling and Reconstruction | Mingyue Yang, Yuxin Wen, Weikai Chen, Yongwei Chen, Kui Jia | Many learning-based approaches have difficulty scaling to unseen data, as the generality of its learned prior is limited to the scale and variations of the training samples. This holds particularly true with 3D learning tasks, given the sparsity of 3D datasets available. We introduce a new learning framework for 3D modeling and reconstruction that greatly improves the generalization ability of a deep generator. Our approach strives to connect the good ends of both learning-based and optimization-based methods. In particular, unlike the common practice that fixes the pre-trained priors at test time, we propose to further optimize the learned prior and latent code according to the input physical measurements after the training. We show that the proposed strategy effectively breaks the barriers constrained by the pre-trained priors and could lead to high-quality adaptation to unseen data. We realize our framework using the implicit surface representation and validate the efficacy of our approach in a variety of challenging tasks that take highly sparse or collapsed observations as input. Experimental results show that our approach compares favorably with the state-of-the-art methods in terms of both generality and accuracy. | https://openaccess.thecvf.com/content/CVPR2021/papers/Yang_Deep_Optimized_Priors_for_3D_Shape_Modeling_and_Reconstruction_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.07241 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Yang_Deep_Optimized_Priors_for_3D_Shape_Modeling_and_Reconstruction_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Yang_Deep_Optimized_Priors_for_3D_Shape_Modeling_and_Reconstruction_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yang_Deep_Optimized_Priors_CVPR_2021_supplemental.zip | null |
Affordance Transfer Learning for Human-Object Interaction Detection | Zhi Hou, Baosheng Yu, Yu Qiao, Xiaojiang Peng, Dacheng Tao | Reasoning the human-object interactions (HOI) is essential for deeper scene understanding, while object affordances (or functionalities) are of great importance for human to discover unseen HOIs with novel objects. Inspired by this, we introduce an affordance transfer learning approach to jointly detect HOIs with novel object and recognize affordances. Specifically, HOI representations can be decoupled into a combination of affordance and object representations, making it possible to compose novel interactions by combining affordance representations and novel object representations from additional images, i.e. transferring the affordance to novel objects. With the proposed affordance transfer learning, the model is also capable of inferring the affordances of novel objects from known affordance representations. The proposed method can thus be used to 1) improve the performance of HOI detection, especially for the HOIs with unseen objects; and 2) infer the affordances of novel objects. Experimental results on two datasets, HICO-DET and HOI-COCO (from V-COCO), demonstrate significant improvements over recent state-of-the-art methods for HOI detection and object affordance detection. Code is available at https://github.com/zhihou7/HOI-CL. | https://openaccess.thecvf.com/content/CVPR2021/papers/Hou_Affordance_Transfer_Learning_for_Human-Object_Interaction_Detection_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.02867 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Hou_Affordance_Transfer_Learning_for_Human-Object_Interaction_Detection_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Hou_Affordance_Transfer_Learning_for_Human-Object_Interaction_Detection_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Hou_Affordance_Transfer_Learning_CVPR_2021_supplemental.pdf | null |
DSC-PoseNet: Learning 6DoF Object Pose Estimation via Dual-Scale Consistency | Zongxin Yang, Xin Yu, Yi Yang | Compared to 2D object bounding-box labeling, it is very difficult for humans to annotate 3D object poses, especially when depth images of scenes are unavailable. This paper investigates whether we can estimate the object poses effectively when only RGB images and 2D object annotations are given. To this end, we present a two-step pose estimation framework to attain 6DoF object poses from 2D object bounding-boxes. In the first step, the framework learns to segment objects from real and synthetic data in a weakly-supervised fashion, and the segmentation masks will act as a prior for pose estimation. In the second step, we design a dual-scale pose estimation network, namely DSC-PoseNet, to predict object poses by employing a differential renderer. To be specific, our DSC-PoseNet firstly predicts object poses in the original image scale by comparing the segmentation masks and the rendered visible object masks. Then, we resize object regions to a fixed scale to estimate poses once again. In this fashion, we eliminate large scale variations and focus on rotation estimation, thus facilitating pose estimation. Moreover, we exploit the initial pose estimation to generate pseudo ground-truth to train our DSC-PoseNet in a self-supervised manner. The estimation results in these two scales are ensembled as our final pose estimation. Extensive experiments on widely-used benchmarks demonstrate that our method outperforms state-of-the-art models trained on synthetic data by a large margin and even is on par with several fully-supervised methods. | https://openaccess.thecvf.com/content/CVPR2021/papers/Yang_DSC-PoseNet_Learning_6DoF_Object_Pose_Estimation_via_Dual-Scale_Consistency_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Yang_DSC-PoseNet_Learning_6DoF_Object_Pose_Estimation_via_Dual-Scale_Consistency_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Yang_DSC-PoseNet_Learning_6DoF_Object_Pose_Estimation_via_Dual-Scale_Consistency_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yang_DSC-PoseNet_Learning_6DoF_CVPR_2021_supplemental.pdf | null |
Rethinking Graph Neural Architecture Search From Message-Passing | Shaofei Cai, Liang Li, Jincan Deng, Beichen Zhang, Zheng-Jun Zha, Li Su, Qingming Huang | Graph neural networks (GNNs) emerged recently as a standard toolkit for learning from data on graphs. Current GNN designing works depend on immense human expertise to explore different message-passing mechanisms, and require manual enumeration to determine the proper message-passing depth. Inspired by the strong searching capability of neural architecture search (NAS) in CNN, this paper proposes Graph Neural Architecture Search (GNAS) with novel-designed search space. The GNAS can automatically learn better architecture with the optimal depth of message passing on the graph. Specifically, we design Graph Neural Architecture Paradigm (GAP) with tree-topology computation procedure and two types of fine-grained atomic operations (feature filtering & neighbor aggregation) from message-passing mechanism to construct powerful graph network search space. Feature filtering performs adaptive feature selection, and neighbor aggregation captures structural information and calculates neighbors' statistics. Experiments show that our GNAS can search for better GNNs with multiple message-passing mechanisms and optimal message-passing depth. The searched network achieves remarkable improvement over state-of-the-art manual designed and search-based GNNs on five large-scale datasets at three classical graph tasks. | https://openaccess.thecvf.com/content/CVPR2021/papers/Cai_Rethinking_Graph_Neural_Architecture_Search_From_Message-Passing_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.14282 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Cai_Rethinking_Graph_Neural_Architecture_Search_From_Message-Passing_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Cai_Rethinking_Graph_Neural_Architecture_Search_From_Message-Passing_CVPR_2021_paper.html | CVPR 2021 | null | null |
Locate Then Segment: A Strong Pipeline for Referring Image Segmentation | Ya Jing, Tao Kong, Wei Wang, Liang Wang, Lei Li, Tieniu Tan | Referring image segmentation aims to segment the objects referred by a natural language expression. Previous methods usually focus on designing an implicit and recurrent feature interaction mechanism to fuse the visual-linguistic features to directly generate the final segmentation mask without explicitly modeling the localization of the referent guided by language expression and designing a powerful segmentation module. To tackle these problems, we view this task from another perspective by decoupling it into a "locate-then-segment" (LTS) scheme. Given a language expression, people generally first perform attention to the corresponding target image regions, then generate a segmentation mask about the object based on its context. The LTS first extracts and fuses both visual and textual features to get a cross-modal representation, then applies a cross-model interaction on the visual-textual features to locate the referred object with position prior, and finally generates the segmentation result with a light-weight network. Our LTS is simple but surprisingly effective. On three popular benchmark datasets, the LTS outperforms all the previous state-of-the-arts methods by a large margin (e.g., +3.2% on RefCOCO+ and +3.4% on RefCOCOg). In addition, our model is more interpretable with explicitly locating the object, which is also proved by visualization experiments. Accordingly, this framework is very promising to serve as a pipeline for referring image segmentation. | https://openaccess.thecvf.com/content/CVPR2021/papers/Jing_Locate_Then_Segment_A_Strong_Pipeline_for_Referring_Image_Segmentation_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.16284 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Jing_Locate_Then_Segment_A_Strong_Pipeline_for_Referring_Image_Segmentation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Jing_Locate_Then_Segment_A_Strong_Pipeline_for_Referring_Image_Segmentation_CVPR_2021_paper.html | CVPR 2021 | null | null |
Exploring Complementary Strengths of Invariant and Equivariant Representations for Few-Shot Learning | Mamshad Nayeem Rizve, Salman Khan, Fahad Shahbaz Khan, Mubarak Shah | In many real-world problems, collecting a large number of labeled samples is infeasible. Few-shot learning (FSL) is the dominant approach to address this issue, where the objective is to quickly adapt to novel categories in presence of a limited number of samples. FSL tasks have been predominantly solved by leveraging the ideas from gradient-based meta-learning and metric learning approaches. However, recent works have demonstrated the significance of powerful feature representations with a simple embedding network that can outperform existing sophisticated FSL algorithms. In this work, we build on this insight and propose a novel training mechanism that simultaneously enforces equivariance and invariance to a general set of geometric transformations. Equivariance or invariance has been employed standalone in the previous works; however, to the best of our knowledge, they have not been used jointly. Simultaneous optimization for both of these contrasting objectives allows the model to jointly learn features that are not only independent of the input transformation but also the features that encode the structure of geometric transformations. These complementary sets of features help generalize well to novel classes with only a few data samples. We achieve additional improvements by incorporating a novel self-supervised distillation objective. Our extensive experimentation shows that even without knowledge distillation our proposed method can outperform current state-of-the-art FSL methods on five popular benchmark datasets. | https://openaccess.thecvf.com/content/CVPR2021/papers/Rizve_Exploring_Complementary_Strengths_of_Invariant_and_Equivariant_Representations_for_Few-Shot_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.01315 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Rizve_Exploring_Complementary_Strengths_of_Invariant_and_Equivariant_Representations_for_Few-Shot_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Rizve_Exploring_Complementary_Strengths_of_Invariant_and_Equivariant_Representations_for_Few-Shot_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Rizve_Exploring_Complementary_Strengths_CVPR_2021_supplemental.pdf | null |
Encoding in Style: A StyleGAN Encoder for Image-to-Image Translation | Elad Richardson, Yuval Alaluf, Or Patashnik, Yotam Nitzan, Yaniv Azar, Stav Shapiro, Daniel Cohen-Or | We present a generic image-to-image translation framework, pixel2style2pixel (pSp). Our pSp framework is based on a novel encoder network that directly generates a series of style vectors which are fed into a pretrained StyleGAN generator, forming the extended W+ latent space. We first show that our encoder can directly embed real images into W+, with no additional optimization. Next, we propose utilizing our encoder to directly solve image-to-image translation tasks, defining them as encoding problems from some input domain into the latent domain. By deviating from the standard invert first, edit later methodology used with previous StyleGAN encoders, our approach can handle a variety of tasks even when the input image is not represented in the StyleGAN domain. We show that solving translation tasks through StyleGAN significantly simplifies the training process, as no adversary is required, has better support for solving tasks without pixel-to-pixel correspondence, and inherently supports multi-modal synthesis via the resampling of styles. Finally, we demonstrate the potential of our framework on a variety of facial image-to-image translation tasks, even when compared to state-of-the-art solutions designed specifically for a single task, and further show that it can be extended beyond the human facial domain. Code is available at https://github.com/eladrich/pixel2style2pixel. | https://openaccess.thecvf.com/content/CVPR2021/papers/Richardson_Encoding_in_Style_A_StyleGAN_Encoder_for_Image-to-Image_Translation_CVPR_2021_paper.pdf | http://arxiv.org/abs/2008.00951 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Richardson_Encoding_in_Style_A_StyleGAN_Encoder_for_Image-to-Image_Translation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Richardson_Encoding_in_Style_A_StyleGAN_Encoder_for_Image-to-Image_Translation_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Richardson_Encoding_in_Style_CVPR_2021_supplemental.pdf | null |
Towards Bridging Event Captioner and Sentence Localizer for Weakly Supervised Dense Event Captioning | Shaoxiang Chen, Yu-Gang Jiang | Dense Event Captioning (DEC) aims to jointly localize and describe multiple events of interest in untrimmed videos, which is an advancement of the conventional video captioning task (generating a single sentence description for a trimmed video). Weakly Supervised Dense Event Captioning (WS-DEC) goes one step further by not relying on human-annotated temporal event boundaries. However, there are few methods trying to tackle this task, and how to connect localization and description remains an open problem. In this paper, we demonstrate that under weak supervision, the event captioning module and localization module should be more closely bridged in order to improve description performance. Different from previous approaches, in our method, the event captioner generates a sentence from a video segment and feeds it to the sentence localizer to reconstruct the segment, and the localizer produces word importance weights as a guidance for the captioner to improve event description. To further bridge the sentence localizer and event captioner, a concept learner is adopted as the basis of the sentence localizer, which can be utilized to construct an induced set of concept features to enhance video features and improve the event captioner. Finally, our proposed method outperforms state-of-the-art WS-DEC methods on the ActivityNet Captions dataset. | https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_Towards_Bridging_Event_Captioner_and_Sentence_Localizer_for_Weakly_Supervised_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Towards_Bridging_Event_Captioner_and_Sentence_Localizer_for_Weakly_Supervised_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Towards_Bridging_Event_Captioner_and_Sentence_Localizer_for_Weakly_Supervised_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Chen_Towards_Bridging_Event_CVPR_2021_supplemental.pdf | null |
DER: Dynamically Expandable Representation for Class Incremental Learning | Shipeng Yan, Jiangwei Xie, Xuming He | We address the problem of class incremental learning, which is a core step towards achieving adaptive vision intelligence. In particular, we consider the task setting of incremental learning with limited memory and aim to achieve a better stability-plasticity trade-off. To this end, we propose a novel two-stage learning approach that utilizes a dynamically expandable representation for more effective incremental concept modeling. Specifically, at each incremental step, we freeze the previously learned representation and augment it with additional feature dimensions from a new learnable feature extractor. Moreover, we dynamically expand the representation according to the complexity of novel concepts by introducing a channel-level mask-based pruning strategy. This enables us to integrate new visual concepts with retaining learned knowledge. Furthermore, we introduce an auxiliary loss to encourage the model to learn diverse and discriminate features for novel concepts. We conduct extensive experiments on the three class incremental learning benchmarks and our method consistently outperforms other methods with a large margin. | https://openaccess.thecvf.com/content/CVPR2021/papers/Yan_DER_Dynamically_Expandable_Representation_for_Class_Incremental_Learning_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.16788 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Yan_DER_Dynamically_Expandable_Representation_for_Class_Incremental_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Yan_DER_Dynamically_Expandable_Representation_for_Class_Incremental_Learning_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yan_DER_Dynamically_Expandable_CVPR_2021_supplemental.pdf | null |
Fine-Grained Angular Contrastive Learning With Coarse Labels | Guy Bukchin, Eli Schwartz, Kate Saenko, Ori Shahar, Rogerio Feris, Raja Giryes, Leonid Karlinsky | Few-shot learning methods offer pre-training techniques optimized for easier later adaptation of the model to new classes (unseen during training) using one or a few examples. This adaptivity to unseen classes is especially important for many practical applications where the pre-trained label space cannot remain fixed for effective use and the model needs to be "specialized" to support new categories on the fly. One particularly interesting scenario, essentially overlooked by the few-shot literature, is Coarse-to-Fine Few-Shot (C2FS), where the training classes (e.g. animals) are of much `coarser granularity' than the target (test) classes (e.g. breeds). A very practical example of C2FS is when the target classes are sub-classes of the training classes. Intuitively, it is especially challenging as (both regular and few-shot) supervised pre-training tends to learn to ignore intra-class variability which is essential for separating sub-classes. In this paper, we introduce a novel 'Angular normalization' module that allows to effectively combine supervised and self-supervised contrastive pre-training to approach the proposed C2FS task, demonstrating significant gains in a broad study over multiple baselines and datasets. We hope that this work will help to pave the way for future research on this new, challenging, and very practical topic of C2FS classification. | https://openaccess.thecvf.com/content/CVPR2021/papers/Bukchin_Fine-Grained_Angular_Contrastive_Learning_With_Coarse_Labels_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.03515 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Bukchin_Fine-Grained_Angular_Contrastive_Learning_With_Coarse_Labels_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Bukchin_Fine-Grained_Angular_Contrastive_Learning_With_Coarse_Labels_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Bukchin_Fine-Grained_Angular_Contrastive_CVPR_2021_supplemental.zip | null |
Polarimetric Normal Stereo | Yoshiki Fukao, Ryo Kawahara, Shohei Nobuhara, Ko Nishino | We introduce a novel method for recovering per-pixel surface normals from a pair of polarization cameras. Unlike past methods that use polarimetric observations as auxiliary features for correspondence matching, we fully integrate them in cost volume construction and filtering to directly recover per-pixel surface normals, not as byproducts of recovered disparities. Our key idea is to introduce a polarimetric cost volume of distance defined on the polarimetric observations and the polarization state computed from the surface normal. We adapt a belief propagation algorithm to filter this cost volume. The filtering algorithm simultaneously estimates the disparities and surface normals as separate entities, while effectively denoising the original noisy polarimetric observations of a quad-Bayer polarization camera. In addition, in contrast to past methods, we model polarimetric light reflection of mesoscopic surface roughness, which is essential to account for its illumination-dependency. We demonstrate the effectiveness of our method on a number of complex, real objects. Our method offers a simple and detailed 3D sensing capability for complex, non-Lambertian surfaces. | https://openaccess.thecvf.com/content/CVPR2021/papers/Fukao_Polarimetric_Normal_Stereo_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Fukao_Polarimetric_Normal_Stereo_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Fukao_Polarimetric_Normal_Stereo_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Fukao_Polarimetric_Normal_Stereo_CVPR_2021_supplemental.zip | null |
Manifold Regularized Dynamic Network Pruning | Yehui Tang, Yunhe Wang, Yixing Xu, Yiping Deng, Chao Xu, Dacheng Tao, Chang Xu | Neural network pruning is an essential approach for reducing the computational complexity of deep models so that they can be well deployed on resource-limited devices. Compared with conventional methods, the recently developed dynamic pruning methods determine redundant filters variant to each input instance which achieves higher acceleration. Most of the existing methods discover effective sub-networks for each instance independently and do not utilize the relationship between different inputs. To maximally excavate redundancy in the given network architecture, this paper proposes a new paradigm that dynamically removes redundant filters by embedding the manifold information of all instances into the space of pruned networks (dubbed as ManiDP). We first investigate the recognition complexity and feature similarity between images in the training set. Then, the manifold relationship between instances and the pruned sub-networks will be aligned in the training procedure. The effectiveness of the proposed method is verified on several benchmarks, which shows better performance in terms of both accuracy and computational cost compared to the state-of-the-art methods. For example, our method can reduce 55.3% FLOPs of ResNet-34 with only 0.57% top-1 accuracy degradation on ImageNet. | https://openaccess.thecvf.com/content/CVPR2021/papers/Tang_Manifold_Regularized_Dynamic_Network_Pruning_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.05861 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Tang_Manifold_Regularized_Dynamic_Network_Pruning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Tang_Manifold_Regularized_Dynamic_Network_Pruning_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Tang_Manifold_Regularized_Dynamic_CVPR_2021_supplemental.pdf | null |
ViPNAS: Efficient Video Pose Estimation via Neural Architecture Search | Lumin Xu, Yingda Guan, Sheng Jin, Wentao Liu, Chen Qian, Ping Luo, Wanli Ouyang, Xiaogang Wang | Human pose estimation has achieved significant progress in recent years. However, most of the recent methods focus on improving accuracy using complicated models and ignoring real-time efficiency. To achieve a better trade-off between accuracy and efficiency, we propose a novel neural architecture search (NAS) method, termed ViPNAS, to search networks in both spatial and temporal levels for fast online video pose estimation. In the spatial level, we carefully design the search space with five different dimensions including network depth, width, kernel size, group number, and attentions. In the temporal level, we search from a series of temporal feature fusions to optimize the total accuracy and speed across multiple video frames. To the best of our knowledge, we are the first to search for the temporal feature fusion and automatic computation allocation in videos. Extensive experiments demonstrate the effectiveness of our approach on the challenging COCO2017 and PoseTrack2018 datasets. Our discovered model family, S-ViPNAS and T-ViPNAS, achieve significantly higher inference speed (CPU real-time) without sacrificing the accuracy compared to the previous state-of-the-art methods. | https://openaccess.thecvf.com/content/CVPR2021/papers/Xu_ViPNAS_Efficient_Video_Pose_Estimation_via_Neural_Architecture_Search_CVPR_2021_paper.pdf | http://arxiv.org/abs/2105.10154 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Xu_ViPNAS_Efficient_Video_Pose_Estimation_via_Neural_Architecture_Search_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Xu_ViPNAS_Efficient_Video_Pose_Estimation_via_Neural_Architecture_Search_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Xu_ViPNAS_Efficient_Video_CVPR_2021_supplemental.pdf | null |
Open Domain Generalization with Domain-Augmented Meta-Learning | Yang Shu, Zhangjie Cao, Chenyu Wang, Jianmin Wang, Mingsheng Long | Leveraging datasets available to learn a model with high generalization ability to unseen domains is important for computer vision, especially when the unseen domain's annotated data are unavailable. We study the problem of learning from different source domains to achieve high performance on an unknown target domain, where the distributions and label sets of each individual source domain and the target domain are different. The problem can be generally applied to diverse source domains and widely applicable to real-world applications. We propose a Domain-Augmented Meta-Learning framework to learn open-domain generalizable representations. We augment domains on both feature-level by a new Dirichlet mixup and label-level by distilled soft-labeling, which complements each domain with missing classes and other domain knowledge. We conduct meta-learning over domains by designing new meta-learning tasks and losses to preserve domain unique knowledge and generalize knowledge across domains simultaneously. Experiment results on various multi-domain datasets demonstrate that the proposed Domain-Augmented Meta-Learning outperforms previous methods for unseen target classification. | https://openaccess.thecvf.com/content/CVPR2021/papers/Shu_Open_Domain_Generalization_with_Domain-Augmented_Meta-Learning_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.03620 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Shu_Open_Domain_Generalization_with_Domain-Augmented_Meta-Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Shu_Open_Domain_Generalization_with_Domain-Augmented_Meta-Learning_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Shu_Open_Domain_Generalization_CVPR_2021_supplemental.pdf | null |
DeepTag: An Unsupervised Deep Learning Method for Motion Tracking on Cardiac Tagging Magnetic Resonance Images | Meng Ye, Mikael Kanski, Dong Yang, Qi Chang, Zhennan Yan, Qiaoying Huang, Leon Axel, Dimitris Metaxas | Cardiac tagging magnetic resonance imaging (t-MRI) is the gold standard for regional myocardium deformation and cardiac strain estimation. However, this technique has not been widely used in clinical diagnosis, as a result of the difficulty of motion tracking encountered with t-MRI images. In this paper, we propose a novel deep learning-based fully unsupervised method for in vivo motion tracking on t-MRI images. We first estimate the motion field (INF) between any two consecutive t-MRI frames by a bi-directional generative diffeomorphic registration neural network. Using this result, we then estimate the Lagrangian motion field between the reference frame and any other frame through a differentiable composition layer. By utilizing temporal information to perform reasonable estimations on spatio-temporal motion fields, this novel method provides a useful solution for motion tracking and image registration in dynamic medical imaging. Our method has been validated on a representative clinical t-MRI dataset; the experimental results show that our method is superior to conventional motion tracking methods in terms of landmark tracking accuracy and inference efficiency. | https://openaccess.thecvf.com/content/CVPR2021/papers/Ye_DeepTag_An_Unsupervised_Deep_Learning_Method_for_Motion_Tracking_on_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.02772 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Ye_DeepTag_An_Unsupervised_Deep_Learning_Method_for_Motion_Tracking_on_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Ye_DeepTag_An_Unsupervised_Deep_Learning_Method_for_Motion_Tracking_on_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Ye_DeepTag_An_Unsupervised_CVPR_2021_supplemental.zip | null |
Learning by Planning: Language-Guided Global Image Editing | Jing Shi, Ning Xu, Yihang Xu, Trung Bui, Franck Dernoncourt, Chenliang Xu | Recently, language-guided global image editing draws increasing attention with growing application potentials. However, previous GAN-based methods are not only confined to domain-specific, low-resolution data but also lacking in interpretability. To overcome the collective difficulties, we develop a text-to-operation model to map the vague editing language request into a series of editing operations, e.g., change contrast, brightness, and saturation. Each operation is interpretable and differentiable. Furthermore, the only supervision in the task is the target image, which is insufficient for a stable training of sequential decisions. Hence, we propose a novel operation planning algorithm to generate possible editing sequences from the target image as pseudo ground truth. Comparison experiments on the newly collected MA5k-Req dataset and GIER dataset show the advantages of our methods. Code is available at https://github.com/jshi31/T2ONet. | https://openaccess.thecvf.com/content/CVPR2021/papers/Shi_Learning_by_Planning_Language-Guided_Global_Image_Editing_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Shi_Learning_by_Planning_Language-Guided_Global_Image_Editing_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Shi_Learning_by_Planning_Language-Guided_Global_Image_Editing_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Shi_Learning_by_Planning_CVPR_2021_supplemental.pdf | null |
Curriculum Graph Co-Teaching for Multi-Target Domain Adaptation | Subhankar Roy, Evgeny Krivosheev, Zhun Zhong, Nicu Sebe, Elisa Ricci | In this paper we address multi-target domain adaptation (MTDA), where given one labeled source dataset and multiple unlabeled target datasets that differ in data distributions, the task is to learn a robust predictor for all the target domains. We identify two key aspects that can help to alleviate multiple domain-shifts in the MTDA: feature aggregation and curriculum learning. To this end, we propose Curriculum Graph Co-Teaching (CGCT) that uses a dual classifier head, with one of them being a graph convolutional network (GCN) which aggregates features from similar samples across the domains. To prevent the classifiers from over-fitting on its own noisy pseudo-labels we develop a co-teaching strategy with the dual classifier head that is assisted by curriculum learning to obtain more reliable pseudo-labels. Furthermore, when the domain labels are available, we propose Domain-aware Curriculum Learning (DCL), a sequential adaptation strategy that first adapts on the easier target domains, followed by the harder ones. We experimentally demonstrate the effectiveness of our proposed frameworks on several benchmarks and advance the state-of-the-art in the MTDA by large margins (e.g. +5.6% on the DomainNet). | https://openaccess.thecvf.com/content/CVPR2021/papers/Roy_Curriculum_Graph_Co-Teaching_for_Multi-Target_Domain_Adaptation_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.00808 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Roy_Curriculum_Graph_Co-Teaching_for_Multi-Target_Domain_Adaptation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Roy_Curriculum_Graph_Co-Teaching_for_Multi-Target_Domain_Adaptation_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Roy_Curriculum_Graph_Co-Teaching_CVPR_2021_supplemental.pdf | null |
Uncalibrated Neural Inverse Rendering for Photometric Stereo of General Surfaces | Berk Kaya, Suryansh Kumar, Carlos Oliveira, Vittorio Ferrari, Luc Van Gool | This paper presents an uncalibrated deep neural network framework for the photometric stereo problem. For training models to solve the problem, existing neural network-based methods either require exact light directions or ground-truth surface normals of the object or both. However, in practice, it is challenging to procure both of this information precisely, which restricts the broader adoption of photometric stereo algorithms for vision application. To bypass this difficulty, we propose an uncalibrated neural inverse rendering approach to this problem. Our method first estimates the light directions from the input images and then optimizes an image reconstruction loss to calculate the surface normals, bidirectional reflectance distribution function value, and depth. Additionally, our formulation explicitly models the concave and convex parts of a complex surface to consider the effects of interreflections in the image formation process. Extensive evaluation of the proposed method on the challenging subjects generally shows comparable or better results than the supervised and classical approaches. | https://openaccess.thecvf.com/content/CVPR2021/papers/Kaya_Uncalibrated_Neural_Inverse_Rendering_for_Photometric_Stereo_of_General_Surfaces_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.06777 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Kaya_Uncalibrated_Neural_Inverse_Rendering_for_Photometric_Stereo_of_General_Surfaces_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Kaya_Uncalibrated_Neural_Inverse_Rendering_for_Photometric_Stereo_of_General_Surfaces_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Kaya_Uncalibrated_Neural_Inverse_CVPR_2021_supplemental.pdf | null |
Improving the Transferability of Adversarial Samples With Adversarial Transformations | Weibin Wu, Yuxin Su, Michael R. Lyu, Irwin King | Although deep neural networks (DNNs) have achieved tremendous performance in diverse vision challenges, they are surprisingly susceptible to adversarial examples, which are born of intentionally perturbing benign samples in a human-imperceptible fashion. It thus poses security concerns on the deployment of DNNs in practice, particularly in safety- and security-sensitive domains. To investigate the robustness of DNNs, transfer-based attacks have attracted a growing interest recently due to their high practical applicability, where attackers craft adversarial samples with local models and employ the resultant samples to attack a remote black-box model. However, existing transfer-based attacks frequently suffer from low success rates due to overfitting to the adopted local model. To boost the transferability of adversarial samples, we propose to improve the robustness of synthesized adversarial samples via adversarial transformations. Specifically, we employ an adversarial transformation network to model the most harmful distortions that can destroy adversarial noises and require the synthesized adversarial samples to become resistant to such adversarial transformations. Extensive experiments on the ImageNet benchmark showcase the superiority of our method to state-of-the-art baselines in attacking both undefended and defended models. | https://openaccess.thecvf.com/content/CVPR2021/papers/Wu_Improving_the_Transferability_of_Adversarial_Samples_With_Adversarial_Transformations_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wu_Improving_the_Transferability_of_Adversarial_Samples_With_Adversarial_Transformations_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wu_Improving_the_Transferability_of_Adversarial_Samples_With_Adversarial_Transformations_CVPR_2021_paper.html | CVPR 2021 | null | null |
Self-Supervised Learning for Semi-Supervised Temporal Action Proposal | Xiang Wang, Shiwei Zhang, Zhiwu Qing, Yuanjie Shao, Changxin Gao, Nong Sang | Self-supervised learning presents a remarkable performance to utilize unlabeled data for various video tasks. In this paper, we focus on applying the power of self-supervised methods to improve semi-supervised action proposal generation. Particularly, we design a Self-supervised Semi-supervised Temporal Action Proposal (SSTAP) framework. The SSTAP contains two crucial branches, i.e., temporal-aware semi-supervised branch and relation-aware self-supervised branch. The semi-supervised branch improves the proposal model by introducing two temporal perturbations, i.e., temporal feature shift and temporal feature flip, in the mean teacher framework. The self-supervised branch defines two pretext tasks, including masked feature reconstruction and clip-order prediction, to learn the relation of temporal clues. By this means, SSTAP can better explore unlabeled videos, and improve the discriminative abilities of learned action features. We extensively evaluate the proposed SSTAP on THUMOS14 and ActivityNet v1.3 datasets. The experimental results demonstrate that SSTAP significantly outperforms state-of-the-art semi-supervised methods and even matches fully-supervised methods. The code will be released once this paper is accepted. | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_Self-Supervised_Learning_for_Semi-Supervised_Temporal_Action_Proposal_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.03214 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Self-Supervised_Learning_for_Semi-Supervised_Temporal_Action_Proposal_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Self-Supervised_Learning_for_Semi-Supervised_Temporal_Action_Proposal_CVPR_2021_paper.html | CVPR 2021 | null | null |
Learning Compositional Representation for 4D Captures With Neural ODE | Boyan Jiang, Yinda Zhang, Xingkui Wei, Xiangyang Xue, Yanwei Fu | Learning based representation has become the key to the success of many computer vision systems. While many 3D representations have been proposed, it is still an unaddressed problem how to represent a dynamically changing 3D object. In this paper, we introduce a compositional representation for 4D captures, i.e. a deforming 3D object over a temporal span, that disentangles shape, initial state, and motion respectively. Each component is represented by a latent code via a trained encoder. To model the motion, a neural Ordinary Differential Equation (ODE) is trained to update the initial state conditioned on the learned motion code, and a decoder takes the shape code and the updated state code to reconstruct the 3D model at each time stamp. To this end, we propose an Identity Exchange Training (IET) strategy to encourage the network to learn effectively decoupling each component. Extensive experiments demonstrate that the proposed method outperforms existing state-of-the-art deep learning based methods on 4D reconstruction, and significantly improves on various tasks, including motion transfer and completion. | https://openaccess.thecvf.com/content/CVPR2021/papers/Jiang_Learning_Compositional_Representation_for_4D_Captures_With_Neural_ODE_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.08271 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Jiang_Learning_Compositional_Representation_for_4D_Captures_With_Neural_ODE_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Jiang_Learning_Compositional_Representation_for_4D_Captures_With_Neural_ODE_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Jiang_Learning_Compositional_Representation_CVPR_2021_supplemental.pdf | null |
Effective Snapshot Compressive-Spectral Imaging via Deep Denoising and Total Variation Priors | Haiquan Qiu, Yao Wang, Deyu Meng | Snapshot compressive imaging (SCI) is a new type of compressive imaging system that compresses multiple frames of images into a single snapshot measurement, which enjoys low cost, low bandwidth, and high-speed sensing rate. By applying the existing SCI methods to deal with hyperspectral images, however, could not fully exploit the underlying structures, and thereby demonstrate unsatisfactory reconstruction performance. To remedy such issue, this paper aims to propose a new effective method by taking advantage of two intrinsic priors of the hyperspectral images, namely deep image denoising and total variation (TV) priors. Specifically, we propose an optimization objective to utilize these two priors. By solving this optimization objective, our method is equivalent to incorporate a weighted FFDNet and a 2DTV or 3DTV denoiser into the plug-and-play framework. Extensive numerical experiments demonstrate the outperformance of the proposed method over several state-of-the-art alternatives. Additionally, we provide a detailed convergence analysis of the resulting plug-and-play algorithm under relatively weak conditions such as without using diminishing step sizes. The code is available at https://github.com/ucker/SCI-TV-FFDNet. | https://openaccess.thecvf.com/content/CVPR2021/papers/Qiu_Effective_Snapshot_Compressive-Spectral_Imaging_via_Deep_Denoising_and_Total_Variation_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Qiu_Effective_Snapshot_Compressive-Spectral_Imaging_via_Deep_Denoising_and_Total_Variation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Qiu_Effective_Snapshot_Compressive-Spectral_Imaging_via_Deep_Denoising_and_Total_Variation_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Qiu_Effective_Snapshot_Compressive-Spectral_CVPR_2021_supplemental.pdf | null |
LAFEAT: Piercing Through Adversarial Defenses With Latent Features | Yunrui Yu, Xitong Gao, Cheng-Zhong Xu | Deep convolutional neural networks are susceptible to adversarial attacks. They can be easily deceived to give an incorrect output by adding a tiny perturbation to the input. This presents a great challenge in making CNNs robust against such attacks. An influx of new defense techniques have been proposed to this end. In this paper, we show that latent features in certain "robust" models are surprisingly susceptible to adversarial attacks. On top of this, we introduce a unified Linfinity white-box attack algorithm which harnesses latent features in its gradient descent steps, namely LAFEAT. We show that not only is it computationally much more efficient for successful attacks, but it is also a stronger adversary than the current state-of-the-art across a wide range of defense mechanisms. This suggests that model robustness could be contingent the effective use of the defender's hidden components, and it should no longer be viewed from a holistic perspective. | https://openaccess.thecvf.com/content/CVPR2021/papers/Yu_LAFEAT_Piercing_Through_Adversarial_Defenses_With_Latent_Features_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.09284 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Yu_LAFEAT_Piercing_Through_Adversarial_Defenses_With_Latent_Features_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Yu_LAFEAT_Piercing_Through_Adversarial_Defenses_With_Latent_Features_CVPR_2021_paper.html | CVPR 2021 | null | null |
Exploiting Spatial Dimensions of Latent in GAN for Real-Time Image Editing | Hyunsu Kim, Yunjey Choi, Junho Kim, Sungjoo Yoo, Youngjung Uh | Generative adversarial networks (GANs) synthesize realistic images from random latent vectors. Although manipulating the latent vectors controls the synthesized outputs, editing real images with GANs suffers from i) time-consuming optimization for projecting real images to the latent vectors, ii) or inaccurate embedding through an encoder. We propose StyleMapGAN: the intermediate latent space has spatial dimensions, and a spatially variant modulation replaces AdaIN. It makes the embedding through an encoder more accurate than existing optimization-based methods while maintaining the properties of GANs. Experimental results demonstrate that our method significantly outperforms state-of-the-art models in various image manipulation tasks such as local editing and image interpolation. Last but not least, conventional editing methods on GANs are still valid on our StyleMapGAN. Source code is available at https://github.com/naver-ai/StyleMapGAN. | https://openaccess.thecvf.com/content/CVPR2021/papers/Kim_Exploiting_Spatial_Dimensions_of_Latent_in_GAN_for_Real-Time_Image_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.14754 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Kim_Exploiting_Spatial_Dimensions_of_Latent_in_GAN_for_Real-Time_Image_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Kim_Exploiting_Spatial_Dimensions_of_Latent_in_GAN_for_Real-Time_Image_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Kim_Exploiting_Spatial_Dimensions_CVPR_2021_supplemental.pdf | null |
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