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Few-Shot Open-Set Recognition Using Meta-Learning | Bo Liu, Hao Kang, Haoxiang Li, Gang Hua, Nuno Vasconcelos | The problem of open-set recognition is considered. While previous approaches only consider this problem in the context of large-scale classifier training, we seek a unified solution for this and the low-shot classification setting. It is argued that the classic softmax classifier is a poor solution for open-set recognition, since it tends to overfit on the training classes. Randomization is then proposed as a solution to this problem. This suggests the use of meta-learning techniques, commonly used for few-shot classification, for the solution of open-set recognition. A new oPen sEt mEta LEaRning (PEELER) algorithm is then introduced. This combines the random selection of a set of novel classes per episode, a loss that maximizes the posterior entropy for examples of those classes, and a new metric learning formulation based on the Mahalanobis distance. Experimental results show that PEELER achieves state of the art open set recognition performance for both few-shot and large-scale recognition. On CIFAR and miniImageNet, it achieves substantial gains in seen/unseen class detection AUROC for a given seen-class classification accuracy. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Liu_Few-Shot_Open-Set_Recognition_Using_Meta-Learning_CVPR_2020_paper.pdf | http://arxiv.org/abs/2005.13713 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Liu_Few-Shot_Open-Set_Recognition_Using_Meta-Learning_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Liu_Few-Shot_Open-Set_Recognition_Using_Meta-Learning_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Sequential 3D Human Pose and Shape Estimation From Point Clouds | Kangkan Wang, Jin Xie, Guofeng Zhang, Lei Liu, Jian Yang | This work addresses the problem of 3D human pose and shape estimation from a sequence of point clouds. Existing sequential 3D human shape estimation methods mainly focus on the template model fitting from a sequence of depth images or the parametric model regression from a sequence of RGB images. In this paper, we propose a novel sequential 3D human pose and shape estimation framework from a sequence of point clouds. Specifically, the proposed framework can regress 3D coordinates of mesh vertices at different resolutions from the latent features of point clouds. Based on the estimated 3D coordinates and features at the low resolution, we develop a spatial-temporal mesh attention convolution (MAC) to predict the 3D coordinates of mesh vertices at the high resolution. By assigning specific attentional weights to different neighboring points in the spatial and temporal domains, our spatial-temporal MAC can capture structured spatial and temporal features of point clouds. We further generalize our framework to the real data of human bodies with a weakly supervised fine-tuning method. The experimental results on SURREAL, Human3.6M, DFAUST and the real detailed data demonstrate that the proposed approach can accurately recover the 3D body model sequence from a sequence of point clouds. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wang_Sequential_3D_Human_Pose_and_Shape_Estimation_From_Point_Clouds_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Sequential_3D_Human_Pose_and_Shape_Estimation_From_Point_Clouds_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Sequential_3D_Human_Pose_and_Shape_Estimation_From_Point_Clouds_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Wang_Sequential_3D_Human_CVPR_2020_supplemental.zip | null | null |
Sequential Mastery of Multiple Visual Tasks: Networks Naturally Learn to Learn and Forget to Forget | Guy Davidson, Michael C. Mozer | We explore the behavior of a standard convolutional neural net in a continual-learning setting that introduces visual classification tasks sequentially and requires the net to master new tasks while preserving mastery of previously learned tasks. This setting corresponds to that which human learners face as they acquire domain expertise serially, for example, as an individual studies a textbook. Through simulations involving sequences of ten related visual tasks, we find reason for optimism that nets will scale well as they advance from having a single skill to becoming multi-skill domain experts. We observe two key phenomena. First, forward facilitation---the accelerated learning of task n+1 having learned n previous tasks---grows with n. Second, backward interference---the forgetting of the n previous tasks when learning task n+1 ---diminishes with n. Amplifying forward facilitation is the goal of research on metalearning, and attenuating backward interference is the goal of research on catastrophic forgetting. We find that both of these goals are attained simply through broader exposure to a domain. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Davidson_Sequential_Mastery_of_Multiple_Visual_Tasks_Networks_Naturally_Learn_to_CVPR_2020_paper.pdf | http://arxiv.org/abs/1905.10837 | https://www.youtube.com/watch?v=RI-dAKndhdI | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Davidson_Sequential_Mastery_of_Multiple_Visual_Tasks_Networks_Naturally_Learn_to_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Davidson_Sequential_Mastery_of_Multiple_Visual_Tasks_Networks_Naturally_Learn_to_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Davidson_Sequential_Mastery_of_CVPR_2020_supplemental.pdf | null | null |
Siam R-CNN: Visual Tracking by Re-Detection | Paul Voigtlaender, Jonathon Luiten, Philip H.S. Torr, Bastian Leibe | We present Siam R-CNN, a Siamese re-detection architecture which unleashes the full power of two-stage object detection approaches for visual object tracking. We combine this with a novel tracklet-based dynamic programming algorithm, which takes advantage of re-detections of both the first-frame template and previous-frame predictions, to model the full history of both the object to be tracked and potential distractor objects. This enables our approach to make better tracking decisions, as well as to re-detect tracked objects after long occlusion. Finally, we propose a novel hard example mining strategy to improve Siam R-CNN's robustness to similar looking objects. Siam R-CNN achieves the current best performance on ten tracking benchmarks, with especially strong results for long-term tracking. We make our code and models available at www.vision.rwth-aachen.de/page/siamrcnn. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Voigtlaender_Siam_R-CNN_Visual_Tracking_by_Re-Detection_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Voigtlaender_Siam_R-CNN_Visual_Tracking_by_Re-Detection_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Voigtlaender_Siam_R-CNN_Visual_Tracking_by_Re-Detection_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Voigtlaender_Siam_R-CNN_Visual_CVPR_2020_supplemental.zip | null | null |
Eternal Sunshine of the Spotless Net: Selective Forgetting in Deep Networks | Aditya Golatkar, Alessandro Achille, Stefano Soatto | We explore the problem of selectively forgetting a particular subset of the data used for training a deep neural network. While the effects of the data to be forgotten can be hidden from the output of the network, insights may still be gleaned by probing deep into its weights. We propose a method for "scrubbing" the weights clean of information about a particular set of training data. The method does not require retraining from scratch, nor access to the data originally used for training. Instead, the weights are modified so that any probing function of the weights is indistinguishable from the same function applied to the weights of a network trained without the data to be forgotten. This condition is a generalized and weaker form of Differential Privacy. Exploiting ideas related to the stability of stochastic gradient descent, we introduce an upper-bound on the amount of information remaining in the weights, which can be estimated efficiently even for deep neural networks. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Golatkar_Eternal_Sunshine_of_the_Spotless_Net_Selective_Forgetting_in_Deep_CVPR_2020_paper.pdf | http://arxiv.org/abs/1911.04933 | https://www.youtube.com/watch?v=OijT2gL7gyA | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Golatkar_Eternal_Sunshine_of_the_Spotless_Net_Selective_Forgetting_in_Deep_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Golatkar_Eternal_Sunshine_of_the_Spotless_Net_Selective_Forgetting_in_Deep_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Golatkar_Eternal_Sunshine_of_CVPR_2020_supplemental.pdf | null | null |
MSG-GAN: Multi-Scale Gradients for Generative Adversarial Networks | Animesh Karnewar, Oliver Wang | While Generative Adversarial Networks (GANs) have seen huge successes in image synthesis tasks, they are notoriously difficult to adapt to different datasets, in part due to instability during training and sensitivity to hyperparameters. One commonly accepted reason for this instability is that gradients passing from the discriminator to the generator become uninformative when there isn't enough overlap in the supports of the real and fake distributions. In this work, we propose the Multi-Scale Gradient Generative Adversarial Network (MSG-GAN), a simple but effective technique for addressing this by allowing the flow of gradients from the discriminator to the generator at multiple scales. This technique provides a stable approach for high resolution image synthesis, and serves as an alternative to the commonly used progressive growing technique. We show that MSG-GAN converges stably on a variety of image datasets of different sizes, resolutions and domains, as well as different types of loss functions and architectures, all with the same set of fixed hyperparameters. When compared to state-of-the-art GANs, our approach matches or exceeds the performance in most of the cases we tried. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Karnewar_MSG-GAN_Multi-Scale_Gradients_for_Generative_Adversarial_Networks_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Karnewar_MSG-GAN_Multi-Scale_Gradients_for_Generative_Adversarial_Networks_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Karnewar_MSG-GAN_Multi-Scale_Gradients_for_Generative_Adversarial_Networks_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Karnewar_MSG-GAN_Multi-Scale_Gradients_CVPR_2020_supplemental.pdf | null | null |
Transferring Cross-Domain Knowledge for Video Sign Language Recognition | Dongxu Li, Xin Yu, Chenchen Xu, Lars Petersson, Hongdong Li | Word-level sign language recognition (WSLR) is a fundamental task in sign language interpretation. It requires models to recognize isolated sign words from videos. However, annotating WSLR data needs expert knowledge, thus limiting WSLR dataset acquisition. On the contrary, there are abundant subtitled sign news videos on the internet. Since these videos have no word-level annotation and exhibit a large domain gap from isolated signs, they cannot be directly used for training WSLR models. We observe that despite the existence of a large domain gap, isolated and news signs share the same visual concepts, such as hand gestures and body movements. Motivated by this observation, we propose a novel method that learns domain-invariant visual concepts and fertilizes WSLR models by transferring knowledge of subtitled news sign to them. To this end, we extract news signs using a base WSLR model, and then design a classifier jointly trained on news and isolated signs to coarsely align these two domain features. In order to learn domain-invariant features within each class and suppress domain-specific features, our method further resorts to an external memory to store the class centroids of the aligned news signs. We then design a temporal attention based on the learnt descriptor to improve recognition performance. Experimental results on standard WSLR datasets show that our method outperforms previous state-of-the-art methods significantly. We also demonstrate the effectiveness of our method on automatically localizing signs from sign news, achieving 28.1 for AP@0.5. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Li_Transferring_Cross-Domain_Knowledge_for_Video_Sign_Language_Recognition_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.03703 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Transferring_Cross-Domain_Knowledge_for_Video_Sign_Language_Recognition_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Transferring_Cross-Domain_Knowledge_for_Video_Sign_Language_Recognition_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Li_Transferring_Cross-Domain_Knowledge_CVPR_2020_supplemental.zip | null | null |
Flow Contrastive Estimation of Energy-Based Models | Ruiqi Gao, Erik Nijkamp, Diederik P. Kingma, Zhen Xu, Andrew M. Dai, Ying Nian Wu | This paper studies a training method to jointly estimate an energy-based model and a flow-based model, in which the two models are iteratively updated based on a shared adversarial value function. This joint training method has the following traits. (1) The update of the energy-based model is based on noise contrastive estimation, with the flow model serving as a strong noise distribution. (2) The update of the flow model approximately minimizes the Jensen-Shannon divergence between the flow model and the data distribution. (3) Unlike generative adversarial networks (GAN) which estimates an implicit probability distribution defined by a generator model, our method estimates two explicit probabilistic distributions on the data. Using the proposed method we demonstrate a significant improvement on the synthesis quality of the flow model, and show the effectiveness of unsupervised feature learning by the learned energy-based model. Furthermore, the proposed training method can be easily adapted to semi-supervised learning. We achieve competitive results to the state-of-the-art semi-supervised learning methods. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Gao_Flow_Contrastive_Estimation_of_Energy-Based_Models_CVPR_2020_paper.pdf | http://arxiv.org/abs/1912.00589 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Gao_Flow_Contrastive_Estimation_of_Energy-Based_Models_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Gao_Flow_Contrastive_Estimation_of_Energy-Based_Models_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Gao_Flow_Contrastive_Estimation_CVPR_2020_supplemental.pdf | null | null |
Improving the Robustness of Capsule Networks to Image Affine Transformations | Jindong Gu, Volker Tresp | Convolutional neural networks (CNNs) achieve translational invariance by using pooling operations. However, the operations do not preserve the spatial relationships in the learned representations. Hence, CNNs cannot extrapolate to various geometric transformations of inputs. Recently, Capsule Networks (CapsNets) have been proposed to tackle this problem. In CapsNets, each entity is represented by a vector and routed to high-level entity representations by a dynamic routing algorithm. CapsNets have been shown to be more robust than CNNs to affine transformations of inputs. However, there is still a huge gap between their performance on transformed inputs compared to untransformed versions. In this work, we first revisit the routing procedure by (un)rolling its forward and backward passes. Our investigation reveals that the routing procedure contributes neither to the generalization ability nor to the affine robustness of the CapsNets. Furthermore, we explore the limitations of capsule transformations and propose affine CapsNets (Aff-CapsNets), which are more robust to affine transformations. On our benchmark task, where models are trained on the MNIST dataset and tested on the AffNIST dataset, our Aff-CapsNets improve the benchmark performance by a large margin (from 79% to 93.21%), without using any routing mechanism. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Gu_Improving_the_Robustness_of_Capsule_Networks_to_Image_Affine_Transformations_CVPR_2020_paper.pdf | http://arxiv.org/abs/1911.07968 | https://www.youtube.com/watch?v=yVyEI9310QI | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Gu_Improving_the_Robustness_of_Capsule_Networks_to_Image_Affine_Transformations_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Gu_Improving_the_Robustness_of_Capsule_Networks_to_Image_Affine_Transformations_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Gu_Improving_the_Robustness_CVPR_2020_supplemental.pdf | null | null |
Interactive Two-Stream Decoder for Accurate and Fast Saliency Detection | Huajun Zhou, Xiaohua Xie, Jian-Huang Lai, Zixuan Chen, Lingxiao Yang | Recently, contour information largely improves the performance of saliency detection. However, the discussion on the correlation between saliency and contour remains scarce. In this paper, we first analyze such correlation and then propose an interactive two-stream decoder to explore multiple cues, including saliency, contour and their correlation. Specifically, our decoder consists of two branches, a saliency branch and a contour branch. Each branch is assigned to learn distinctive features for predicting the corresponding map. Meanwhile, the intermediate connections are forced to learn the correlation by interactively transmitting the features from each branch to the other one. In addition, we develop an adaptive contour loss to automatically discriminate hard examples during learning process. Extensive experiments on six benchmarks well demonstrate that our network achieves competitive performance with a fast speed around 50 FPS. Moreover, our VGG-based model only contains 17.08 million parameters, which is significantly smaller than other VGG-based approaches. Code has been made available at: https://github.com/moothes/ITSD-pytorch. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zhou_Interactive_Two-Stream_Decoder_for_Accurate_and_Fast_Saliency_Detection_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhou_Interactive_Two-Stream_Decoder_for_Accurate_and_Fast_Saliency_Detection_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhou_Interactive_Two-Stream_Decoder_for_Accurate_and_Fast_Saliency_Detection_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
ViewAL: Active Learning With Viewpoint Entropy for Semantic Segmentation | Yawar Siddiqui, Julien Valentin, Matthias Niessner | We propose ViewAL, a novel active learning strategy for semantic segmentation that exploits viewpoint consistency in multi-view datasets. Our core idea is that inconsistencies in model predictions across viewpoints provide a very reliable measure of uncertainty and encourage the model to perform well irrespective of the viewpoint under which objects are observed. To incorporate this uncertainty measure, we introduce a new viewpoint entropy formulation, which is the basis of our active learning strategy. In addition, we propose uncertainty computations on a superpixel level, which exploits inherently localized signal in the segmentation task, directly lowering the annotation costs. This combination of viewpoint entropy and the use of superpixels allows to efficiently select samples that are highly informative for improving the network. We demonstrate that our proposed active learning strategy not only yields the best-performing models for the same amount of required labeled data, but also significantly reduces labeling effort. For instance, our method achieves 95% of maximum achievable network performance using only 7%, 17%, and 24% labeled data on SceneNet-RGBD, ScanNet, and Matterport3D, respectively. On these datasets, the best state-of-the-art method achieves the same performance with 14%, 27% and 33% labeled data. Finally, we demonstrate that labeling using superpixels yields the same quality of ground-truth compared to labeling whole images, but requires 25% less time. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Siddiqui_ViewAL_Active_Learning_With_Viewpoint_Entropy_for_Semantic_Segmentation_CVPR_2020_paper.pdf | http://arxiv.org/abs/1911.11789 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Siddiqui_ViewAL_Active_Learning_With_Viewpoint_Entropy_for_Semantic_Segmentation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Siddiqui_ViewAL_Active_Learning_With_Viewpoint_Entropy_for_Semantic_Segmentation_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Siddiqui_ViewAL_Active_Learning_CVPR_2020_supplemental.pdf | null | null |
A U-Net Based Discriminator for Generative Adversarial Networks | Edgar Schonfeld, Bernt Schiele, Anna Khoreva | Among the major remaining challenges for generative adversarial networks (GANs) is the capacity to synthesize globally and locally coherent images with object shapes and textures indistinguishable from real images. To target this issue we propose an alternative U-Net based discriminator architecture, borrowing the insights from the segmentation literature. The proposed U-Net based architecture allows to provide detailed per-pixel feedback to the generator while maintaining the global coherence of synthesized images, by providing the global image feedback as well. Empowered by the per-pixel response of the discriminator, we further propose a per-pixel consistency regularization technique based on the CutMix data augmentation, encouraging the U-Net discriminator to focus more on semantic and structural changes between real and fake images. This improves the U-Net discriminator training, further enhancing the quality of generated samples. The novel discriminator improves over the state of the art in terms of the standard distribution and image quality metrics, enabling the generator to synthesize images with varying structure, appearance and levels of detail, maintaining global and local realism. Compared to the BigGAN baseline, we achieve an average improvement of 2.7 FID points across FFHQ, CelebA, and the proposed COCO-Animals dataset. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Schonfeld_A_U-Net_Based_Discriminator_for_Generative_Adversarial_Networks_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Schonfeld_A_U-Net_Based_Discriminator_for_Generative_Adversarial_Networks_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Schonfeld_A_U-Net_Based_Discriminator_for_Generative_Adversarial_Networks_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Schonfeld_A_U-Net_Based_CVPR_2020_supplemental.pdf | null | null |
Diversified Arbitrary Style Transfer via Deep Feature Perturbation | Zhizhong Wang, Lei Zhao, Haibo Chen, Lihong Qiu, Qihang Mo, Sihuan Lin, Wei Xing, Dongming Lu | Image style transfer is an underdetermined problem, where a large number of solutions can satisfy the same constraint (the content and style). Although there have been some efforts to improve the diversity of style transfer by introducing an alternative diversity loss, they have restricted generalization, limited diversity and poor scalability. In this paper, we tackle these limitations and propose a simple yet effective method for diversified arbitrary style transfer. The key idea of our method is an operation called deep feature perturbation (DFP), which uses an orthogonal random noise matrix to perturb the deep image feature maps while keeping the original style information unchanged. Our DFP operation can be easily integrated into many existing WCT (whitening and coloring transform)-based methods, and empower them to generate diverse results for arbitrary styles. Experimental results demonstrate that this learning-free and universal method can greatly increase the diversity while maintaining the quality of stylization. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wang_Diversified_Arbitrary_Style_Transfer_via_Deep_Feature_Perturbation_CVPR_2020_paper.pdf | http://arxiv.org/abs/1909.08223 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Diversified_Arbitrary_Style_Transfer_via_Deep_Feature_Perturbation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Diversified_Arbitrary_Style_Transfer_via_Deep_Feature_Perturbation_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
15 Keypoints Is All You Need | Michael Snower, Asim Kadav, Farley Lai, Hans Peter Graf | Pose-tracking is an important problem that requires identifying unique human pose-instances and matching them temporally across different frames in a video. However, existing pose-tracking methods are unable to accurately model temporal relationships and require significant computation, often computing the tracks offline. We present an efficient multi-person pose-tracking method, KeyTrack that only relies on keypoint information without using any RGB or optical flow to locate and track human keypoints in real-time. KeyTrack is a top-down approach that learns spatio-temporal pose relationships by modeling the multi-person pose-tracking problem as a novel Pose Entailment task using a Transformer based architecture. Furthermore, KeyTrack uses a novel, parameter-free, keypoint refinement technique that improves the keypoint estimates used by the Transformers. We achieve state-of-the-art results on PoseTrack'17 and PoseTrack'18 benchmarks while using only a fraction of the computation used by most other methods for computing the tracking information. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Snower_15_Keypoints_Is_All_You_Need_CVPR_2020_paper.pdf | http://arxiv.org/abs/1912.02323 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Snower_15_Keypoints_Is_All_You_Need_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Snower_15_Keypoints_Is_All_You_Need_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Snower_15_Keypoints_Is_CVPR_2020_supplemental.pdf | null | null |
LUVLi Face Alignment: Estimating Landmarks' Location, Uncertainty, and Visibility Likelihood | Abhinav Kumar, Tim K. Marks, Wenxuan Mou, Ye Wang, Michael Jones, Anoop Cherian, Toshiaki Koike-Akino, Xiaoming Liu, Chen Feng | Modern face alignment methods have become quite accurate at predicting the locations of facial landmarks, but they do not typically estimate the uncertainty of their predicted locations nor predict whether landmarks are visible. In this paper, we present a novel framework for jointly predicting landmark locations, associated uncertainties of these predicted locations, and landmark visibilities. We model these as mixed random variables and estimate them using a deep network trained using our proposed Location, Uncertainty, and Visibility Likelihood (LUVLi) loss. In addition, we release an entirely new labeling of a large face alignment dataset with over 19,000 face images in a full range of head poses. Each face is manually labeled with the ground-truth locations of 68 landmarks, with the additional information of whether each landmarks is visible, self-occluded (due to extreme head poses), or externally occluded. Not only does our joint estimation yield accurate estimates of the uncertainty of predicted landmark locations, but it also yields state-of-the-art estimates for the landmark locations themselves on mulitple standard face alignment datasets. Our method's estimates of the uncertainty of predicted landmark locations could be used to automatically identify input images on which face alignment fails, which can be critical for downstream tasks. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Kumar_LUVLi_Face_Alignment_Estimating_Landmarks_Location_Uncertainty_and_Visibility_Likelihood_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.02980 | https://www.youtube.com/watch?v=8lrVQvTrdrw | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Kumar_LUVLi_Face_Alignment_Estimating_Landmarks_Location_Uncertainty_and_Visibility_Likelihood_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Kumar_LUVLi_Face_Alignment_Estimating_Landmarks_Location_Uncertainty_and_Visibility_Likelihood_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Kumar_LUVLi_Face_Alignment_CVPR_2020_supplemental.zip | https://cove.thecvf.com/datasets/372 | null |
Learning to Cartoonize Using White-Box Cartoon Representations | Xinrui Wang, Jinze Yu | This paper presents an approach for image cartoonization. By observing the cartoon painting behavior and consulting artists, we propose to separately identify three white-box representations from images: the surface representation that contains smooth surface of cartoon images, the structure representation that refers to the sparse color-blocks and flatten global content in the celluloid style workflow, and the texture representation that reflects high-frequency texture, contours and details in cartoon images. A Generative Adversarial Network (GAN) framework is used to learn the extracted representations and to cartoonize images. The learning objectives of our method are separately based on each extracted representations, making our framework controllable and adjustable. This enables our approach to meet artists' requirements in different styles and diverse use cases. Qualitative comparisons and quantitative analyses, as well as user studies, have been conducted to validate the effectiveness of this approach, and our method outperforms previous methods in all comparisons. Finally, the ablation study demonstrates the influence of each component in our framework. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wang_Learning_to_Cartoonize_Using_White-Box_Cartoon_Representations_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Learning_to_Cartoonize_Using_White-Box_Cartoon_Representations_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Learning_to_Cartoonize_Using_White-Box_Cartoon_Representations_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Wang_Learning_to_Cartoonize_CVPR_2020_supplemental.pdf | null | null |
PointAugment: An Auto-Augmentation Framework for Point Cloud Classification | Ruihui Li, Xianzhi Li, Pheng-Ann Heng, Chi-Wing Fu | We present PointAugment, a new auto-augmentation framework that automatically optimizes and augments point cloud samples to enrich the data diversity when we train a classification network. Different from existing auto-augmentation methods for 2D images, PointAugment is sample-aware and takes an adversarial learning strategy to jointly optimize an augmentor network and a classifier network, such that the augmentor can learn to produce augmented samples that best fit the classifier. Moreover, we formulate a learnable point augmentation function with a shape-wise transformation and a point-wise displacement, and carefully design loss functions to adopt the augmented samples based on the learning progress of the classifier. Extensive experiments also confirm PointAugment's effectiveness and robustness to improve the performance of various networks on shape classification and retrival. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Li_PointAugment_An_Auto-Augmentation_Framework_for_Point_Cloud_Classification_CVPR_2020_paper.pdf | http://arxiv.org/abs/2002.10876 | https://www.youtube.com/watch?v=dU_H6I1dh0M | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_PointAugment_An_Auto-Augmentation_Framework_for_Point_Cloud_Classification_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_PointAugment_An_Auto-Augmentation_Framework_for_Point_Cloud_Classification_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Li_PointAugment_An_Auto-Augmentation_CVPR_2020_supplemental.pdf | null | null |
Siamese Box Adaptive Network for Visual Tracking | Zedu Chen, Bineng Zhong, Guorong Li, Shengping Zhang, Rongrong Ji | Most of the existing trackers usually rely on either a multi-scale searching scheme or pre-defined anchor boxes to accurately estimate the scale and aspect ratio of a target. Unfortunately, they typically call for tedious and heuristic configurations. To address this issue, we propose a simple yet effective visual tracking framework (named Siamese Box Adaptive Network, SiamBAN) by exploiting the expressive power of the fully convolutional network (FCN). SiamBAN views the visual tracking problem as a parallel classification and regression problem, and thus directly classifies objects and regresses their bounding boxes in a unified FCN. The no-prior box design avoids hyper-parameters associated with the candidate boxes, making SiamBAN more flexible and general. Extensive experiments on visual tracking benchmarks including VOT2018, VOT2019, OTB100, NFS, UAV123, and LaSOT demonstrate that SiamBAN achieves state-of-the-art performance and runs at 40 FPS, confirming its effectiveness and efficiency. The code will be available at https://github.com/hqucv/siamban. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Chen_Siamese_Box_Adaptive_Network_for_Visual_Tracking_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.06761 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Chen_Siamese_Box_Adaptive_Network_for_Visual_Tracking_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Chen_Siamese_Box_Adaptive_Network_for_Visual_Tracking_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Interpretable and Accurate Fine-grained Recognition via Region Grouping | Zixuan Huang, Yin Li | We present an interpretable deep model for fine-grained visual recognition. At the core of our method lies the integration of region-based part discovery and attribution within a deep neural network. Our model is trained using image-level object labels, and provides an interpretation of its results via the segmentation of object parts and the identification of their contributions towards classification. To facilitate the learning of object parts without direct supervision, we explore a simple prior of the occurrence of object parts. We demonstrate that this prior, when combined with our region-based part discovery and attribution, leads to an interpretable model that remains highly accurate. Our model is evaluated on major fine-grained recognition datasets, including CUB-200, CelebA and iNaturalist. Our results compares favourably to state-of-the-art methods on classification tasks, and outperforms previous approaches on the localization of object parts. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Huang_Interpretable_and_Accurate_Fine-grained_Recognition_via_Region_Grouping_CVPR_2020_paper.pdf | http://arxiv.org/abs/2005.10411 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Huang_Interpretable_and_Accurate_Fine-grained_Recognition_via_Region_Grouping_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Huang_Interpretable_and_Accurate_Fine-grained_Recognition_via_Region_Grouping_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Low-Rank Compression of Neural Nets: Learning the Rank of Each Layer | Yerlan Idelbayev, Miguel A. Carreira-Perpinan | Neural net compression can be achieved by approximating each layer's weight matrix by a low-rank matrix. The real difficulty in doing this is not in training the resulting neural net (made up of one low-rank matrix per layer), but in determining what the optimal rank of each layer is--effectively, an architecture search problem with one hyperparameter per layer. We show that, with a suitable formulation, this problem is amenable to a mixed discrete-continuous optimization jointly over the ranks and over the matrix elements, and give a corresponding algorithm. We show that this indeed can select ranks much better than existing approaches, making low-rank compression much more attractive than previously thought. For example, we can make a VGG network faster than a ResNet and with nearly the same classification error. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Idelbayev_Low-Rank_Compression_of_Neural_Nets_Learning_the_Rank_of_Each_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Idelbayev_Low-Rank_Compression_of_Neural_Nets_Learning_the_Rank_of_Each_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Idelbayev_Low-Rank_Compression_of_Neural_Nets_Learning_the_Rank_of_Each_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Idelbayev_Low-Rank_Compression_of_CVPR_2020_supplemental.zip | null | null |
There and Back Again: Revisiting Backpropagation Saliency Methods | Sylvestre-Alvise Rebuffi, Ruth Fong, Xu Ji, Andrea Vedaldi | Saliency methods seek to explain the predictions of a model by producing an importance map across each input sample. A popular class of such methods is based on backpropagating a signal and analyzing the resulting gradient. Despite much research on such methods, relatively little work has been done to clarify the differences between such methods as well as the desiderata of these techniques. Thus, there is a need for rigorously understanding the relationships between different methods as well as their failure modes. In this work, we conduct a thorough analysis of backpropagation-based saliency methods and propose a single framework under which several such methods can be unified. As a result of our study, we make three additional contributions. First, we use our framework to propose NormGrad, a novel saliency method based on the spatial contribution of gradients of convolutional weights. Second, we combine saliency maps at different layers to test the ability of saliency methods to extract complementary information at different network levels (e.g. trading off spatial resolution and distinctiveness) and we explain why some methods fail at specific layers (e.g., Grad-CAM anywhere besides the last convolutional layer). Third, we introduce a class-sensitivity metric and a meta-learning inspired paradigm applicable to any saliency method for improving sensitivity to the output class being explained. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Rebuffi_There_and_Back_Again_Revisiting_Backpropagation_Saliency_Methods_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.02866 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Rebuffi_There_and_Back_Again_Revisiting_Backpropagation_Saliency_Methods_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Rebuffi_There_and_Back_Again_Revisiting_Backpropagation_Saliency_Methods_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Rebuffi_There_and_Back_CVPR_2020_supplemental.pdf | null | null |
Learning Meta Face Recognition in Unseen Domains | Jianzhu Guo, Xiangyu Zhu, Chenxu Zhao, Dong Cao, Zhen Lei, Stan Z. Li | Face recognition systems are usually faced with unseen domains in real-world applications and show unsatisfactory performance due to their poor generalization. For example, a well-trained model on webface data cannot deal with the ID vs. Spot task in surveillance scenario. In this paper, we aim to learn a generalized model that can directly handle new unseen domains without any model updating. To this end, we propose a novel face recognition method via meta-learning named Meta Face Recognition (MFR). MFR synthesizes the source/target domain shift with a meta-optimization objective, which requires the model to learn effective representations not only on synthesized source domains but also on synthesized target domains. Specifically, we build domain-shift batches through a domain-level sampling strategy and get back-propagated gradients/meta-gradients on synthesized source/target domains by optimizing multi-domain distributions. The gradients and meta-gradients are further combined to update the model to improve generalization. Besides, we propose two benchmarks for generalized face recognition evaluation. Experiments on our benchmarks validate the generalization of our method compared to several baselines and other state-of-the-arts. The proposed benchmarks and code will be available at https://github.com/cleardusk/MFR. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Guo_Learning_Meta_Face_Recognition_in_Unseen_Domains_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.07733 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Guo_Learning_Meta_Face_Recognition_in_Unseen_Domains_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Guo_Learning_Meta_Face_Recognition_in_Unseen_Domains_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Guo_Learning_Meta_Face_CVPR_2020_supplemental.pdf | null | null |
MineGAN: Effective Knowledge Transfer From GANs to Target Domains With Few Images | Yaxing Wang, Abel Gonzalez-Garcia, David Berga, Luis Herranz, Fahad Shahbaz Khan, Joost van de Weijer | One of the attractive characteristics of deep neural networks is their ability to transfer knowledge obtained in one domain to other related domains. As a result, high-quality networks can be trained in domains with relatively little training data. This property has been extensively studied for discriminative networks but has received significantly less attention for generative models. Given the often enormous effort required to train GANs, both computationally as well as in the dataset collection, the re-use of pretrained GANs is a desirable objective. We propose a novel knowledge transfer method for generative models based on mining the knowledge that is most beneficial to a specific target domain, either from a single or multiple pretrained GANs. This is done using a miner network that identifies which part of the generative distribution of each pretrained GAN outputs samples closest to the target domain. Mining effectively steers GAN sampling towards suitable regions of the latent space, which facilitates the posterior finetuning and avoids pathologies of other methods such as mode collapse and lack of flexibility. We perform experiments on several complex datasets using various GAN architectures (BigGAN, Progressive GAN) and show that the proposed method, called MineGAN, effectively transfers knowledge to domains with few target images, outperforming existing methods. In addition, MineGAN can successfully transfer knowledge from multiple pretrained GANs. Our code is available at: https://github.com/yaxingwang/MineGAN. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wang_MineGAN_Effective_Knowledge_Transfer_From_GANs_to_Target_Domains_With_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=4IAkpHrnkoU | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_MineGAN_Effective_Knowledge_Transfer_From_GANs_to_Target_Domains_With_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_MineGAN_Effective_Knowledge_Transfer_From_GANs_to_Target_Domains_With_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Wang_MineGAN_Effective_Knowledge_CVPR_2020_supplemental.pdf | null | null |
State-Aware Tracker for Real-Time Video Object Segmentation | Xi Chen, Zuoxin Li, Ye Yuan, Gang Yu, Jianxin Shen, Donglian Qi | In this work, we address the task of semi-supervised video object segmentation (VOS) and explore how to make efficient use of video property to tackle the challenge of semi-supervision. We propose a novel pipeline called State-Aware Tracker (SAT), which can produce accurate segmentation results with real-time speed. For higher efficiency, SAT takes advantage of the inter-frame consistency and deals with each target object as a tracklet. For more stable and robust performance over video sequences, SAT gets awareness for each state and makes self-adaptation via two feedback loops. One loop assists SAT in generating more stable tracklets. The other loop helps to construct a more robust and holistic target representation. SAT achieves a promising result of 72.3% J&F mean with 39 FPS on DAVIS 2017-Val dataset, which shows a decent trade-off between efficiency and accuracy. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Chen_State-Aware_Tracker_for_Real-Time_Video_Object_Segmentation_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.00482 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Chen_State-Aware_Tracker_for_Real-Time_Video_Object_Segmentation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Chen_State-Aware_Tracker_for_Real-Time_Video_Object_Segmentation_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
DualSDF: Semantic Shape Manipulation Using a Two-Level Representation | Zekun Hao, Hadar Averbuch-Elor, Noah Snavely, Serge Belongie | We are seeing a Cambrian explosion of 3D shape representations for use in machine learning. Some representations seek high expressive power in capturing high-resolution detail. Other approaches seek to represent shapes as compositions of simple parts, which are intuitive for people to understand and easy to edit and manipulate. However, it is difficult to achieve both fidelity and interpretability in the same representation. We propose DualSDF, a representation expressing shapes at two levels of granularity, one capturing fine details and the other representing an abstracted proxy shape using simple and semantically consistent shape primitives. To achieve a tight coupling between the two representations, we use a variational objective over a shared latent space. Our two-level model gives rise to a new shape manipulation technique in which a user can interactively manipulate the coarse proxy shape and see the changes instantly mirrored in the high-resolution shape. Moreover, our model actively augments and guides the manipulation towards producing semantically meaningful shapes, making complex manipulations possible with minimal user input. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Hao_DualSDF_Semantic_Shape_Manipulation_Using_a_Two-Level_Representation_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Hao_DualSDF_Semantic_Shape_Manipulation_Using_a_Two-Level_Representation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Hao_DualSDF_Semantic_Shape_Manipulation_Using_a_Two-Level_Representation_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Hao_DualSDF_Semantic_Shape_CVPR_2020_supplemental.zip | null | null |
Can We Learn Heuristics for Graphical Model Inference Using Reinforcement Learning? | Safa Messaoud, Maghav Kumar, Alexander G. Schwing | Combinatorial optimization is frequently used in computer vision. For instance, in applications like semantic segmentation, human pose estimation and action recognition, programs are formulated for solving inference in Conditional Random Fields (CRFs) to produce a structured output that is consistent with visual features of the image. However, solving inference in CRFs is in general intractable, and approximation methods are computationally demanding and limited to unary, pairwise and hand-crafted forms of higher order potentials. In this paper, we show that we can learn program heuristics, i.e., policies, for solving inference in higher order CRFs for the task of semantic segmentation, using reinforcement learning. Our method solves inference tasks efficiently without imposing any constraints on the form of the potentials. We show compelling results on the Pascal VOC and MOTS datasets. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Messaoud_Can_We_Learn_Heuristics_for_Graphical_Model_Inference_Using_Reinforcement_CVPR_2020_paper.pdf | http://arxiv.org/abs/2005.01508 | https://www.youtube.com/watch?v=160zaip0i0E | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Messaoud_Can_We_Learn_Heuristics_for_Graphical_Model_Inference_Using_Reinforcement_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Messaoud_Can_We_Learn_Heuristics_for_Graphical_Model_Inference_Using_Reinforcement_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Messaoud_Can_We_Learn_CVPR_2020_supplemental.pdf | null | null |
D3S - A Discriminative Single Shot Segmentation Tracker | Alan Lukezic, Jiri Matas, Matej Kristan | Template-based discriminative trackers are currently the dominant tracking paradigm due to their robustness, but are restricted to bounding box tracking and a limited range of transformation models, which reduces their localization accuracy. We propose a discriminative single-shot segmentation tracker - D3S, which narrows the gap between visual object tracking and video object segmentation. A single-shot network applies two target models with complementary geometric properties, one invariant to a broad range of transformations, including non-rigid deformations, the other assuming a rigid object to simultaneously achieve high robustness and online target segmentation. Without per-dataset finetuning and trained only for segmentation as the primary output, D3S outperforms all trackers on VOT2016, VOT2018 and GOT-10k benchmarks and performs close to the state-of-the-art trackers on the TrackingNet. D3S outperforms the leading segmentation tracker SiamMask on video segmentation benchmark and performs on par with top video object segmentation algorithms, while running an order of magnitude faster, close to real-time. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Lukezic_D3S_-_A_Discriminative_Single_Shot_Segmentation_Tracker_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Lukezic_D3S_-_A_Discriminative_Single_Shot_Segmentation_Tracker_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Lukezic_D3S_-_A_Discriminative_Single_Shot_Segmentation_Tracker_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Lukezic_D3S_-_A_CVPR_2020_supplemental.pdf | null | null |
Cross-Spectral Face Hallucination via Disentangling Independent Factors | Boyan Duan, Chaoyou Fu, Yi Li, Xingguang Song, Ran He | The cross-sensor gap is one of the challenges that have aroused much research interests in Heterogeneous Face Recognition (HFR). Although recent methods have attempted to fill the gap with deep generative networks, most of them suffer from the inevitable misalignment between different face modalities. Instead of imaging sensors, the misalignment primarily results from facial geometric variations that are independent of the spectrum. Rather than building a monolithic but complex structure, this paper proposes a Pose Aligned Cross-spectral Hallucination (PACH) approach to disentangle the independent factors and deal with them in individual stages. In the first stage, an Unsupervised Face Alignment (UFA) module is designed to align the facial shapes of the near-infrared (NIR) images with those of the visible (VIS) images in a generative way, where UV maps are effectively utilized as the shape guidance. Thus the task of the second stage becomes spectrum translation with aligned paired data. We develop a Texture Prior Synthesis (TPS) module to achieve complexion control and consequently generate more realistic VIS images than existing methods. Experiments on three challenging NIR-VIS datasets verify the effectiveness of our approach in producing visually appealing images and achieving state-of-the-art performance in HFR. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Duan_Cross-Spectral_Face_Hallucination_via_Disentangling_Independent_Factors_CVPR_2020_paper.pdf | http://arxiv.org/abs/1909.04365 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Duan_Cross-Spectral_Face_Hallucination_via_Disentangling_Independent_Factors_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Duan_Cross-Spectral_Face_Hallucination_via_Disentangling_Independent_Factors_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Deep Face Super-Resolution With Iterative Collaboration Between Attentive Recovery and Landmark Estimation | Cheng Ma, Zhenyu Jiang, Yongming Rao, Jiwen Lu, Jie Zhou | Recent works based on deep learning and facial priors have succeeded in super-resolving severely degraded facial images. However, the prior knowledge is not fully exploited in existing methods, since facial priors such as landmark and component maps are always estimated by low-resolution or coarsely super-resolved images, which may be inaccurate and thus affect the recovery performance. In this paper, we propose a deep face super-resolution (FSR) method with iterative collaboration between two recurrent networks which focus on facial image recovery and landmark estimation respectively. In each recurrent step, the recovery branch utilizes the prior knowledge of landmarks to yield higher-quality images which facilitate more accurate landmark estimation in turn. Therefore, the iterative information interaction between two processes boosts the performance of each other progressively. Moreover, a new attentive fusion module is designed to strengthen the guidance of landmark maps, where facial components are generated individually and aggregated attentively for better restoration. Quantitative and qualitative experimental results show the proposed method significantly outperforms state-of-the-art FSR methods in recovering high-quality face images. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Ma_Deep_Face_Super-Resolution_With_Iterative_Collaboration_Between_Attentive_Recovery_and_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.13063 | https://www.youtube.com/watch?v=4ADU0XWS6Rk | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Ma_Deep_Face_Super-Resolution_With_Iterative_Collaboration_Between_Attentive_Recovery_and_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Ma_Deep_Face_Super-Resolution_With_Iterative_Collaboration_Between_Attentive_Recovery_and_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Ma_Deep_Face_Super-Resolution_CVPR_2020_supplemental.pdf | null | null |
Weakly-Supervised 3D Human Pose Learning via Multi-View Images in the Wild | Umar Iqbal, Pavlo Molchanov, Jan Kautz | One major challenge for monocular 3D human pose estimation in-the-wild is the acquisition of training data that contains unconstrained images annotated with accurate 3D poses. In this paper, we address this challenge by proposing a weakly-supervised approach that does not require 3D annotations and learns to estimate 3D poses from unlabeled multi-view data, which can be acquired easily in in-the-wild environments. We propose a novel end-to-end learning framework that enables weakly-supervised training using multi-view consistency. Since multi-view consistency is prone to degenerated solutions, we adopt a 2.5D pose representation and propose a novel objective function that can only be minimized when the predictions of the trained model are consistent and plausible across all camera views. We evaluate our proposed approach on two large scale datasets (Human3.6M and MPII-INF-3DHP) where it achieves state-of-the-art performance among semi-/weakly-supervised methods. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Iqbal_Weakly-Supervised_3D_Human_Pose_Learning_via_Multi-View_Images_in_the_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.07581 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Iqbal_Weakly-Supervised_3D_Human_Pose_Learning_via_Multi-View_Images_in_the_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Iqbal_Weakly-Supervised_3D_Human_Pose_Learning_via_Multi-View_Images_in_the_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Iqbal_Weakly-Supervised_3D_Human_CVPR_2020_supplemental.zip | null | null |
Data Uncertainty Learning in Face Recognition | Jie Chang, Zhonghao Lan, Changmao Cheng, Yichen Wei | Modeling data uncertainty is important for noisy images, but seldom explored for face recognition. The pioneer work, PFE, considers uncertainty by modeling each face image embedding as a Gaussian distribution. It is quite effective. However, it uses fixed feature (mean of the Gaussian) from an existing model. It only estimates the variance and relies on an ad-hoc and costly metric. Thus, it is not easy to use. It is unclear how uncertainty affects feature learning. This work applies data uncertainty learning to face recognition, such that the feature (mean) and uncertainty (variance) are learnt simultaneously, for the first time. Two learning methods are proposed. They are easy to use and outperform existing deterministic methods as well as PFE on challenging unconstrained scenarios. We also provide insightful analysis on how incorporating uncertainty estimation helps reducing the adverse effects of noisy samples and affects the feature learning. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Chang_Data_Uncertainty_Learning_in_Face_Recognition_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.11339 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Chang_Data_Uncertainty_Learning_in_Face_Recognition_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Chang_Data_Uncertainty_Learning_in_Face_Recognition_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Learning Fast and Robust Target Models for Video Object Segmentation | Andreas Robinson, Felix Jaremo Lawin, Martin Danelljan, Fahad Shahbaz Khan, Michael Felsberg | Video object segmentation (VOS) is a highly challenging problem since the initial mask, defining the target object, is only given at test-time. The main difficulty is to effectively handle appearance changes and similar background objects, while maintaining accurate segmentation. Most previous approaches fine-tune segmentation networks on the first frame, resulting in impractical frame-rates and risk of overfitting. More recent methods integrate generative target appearance models, but either achieve limited robustness or require large amounts of training data. We propose a novel VOS architecture consisting of two network components. The target appearance model consists of a light-weight module, which is learned during the inference stage using fast optimization techniques to predict a coarse but robust target segmentation. The segmentation model is exclusively trained offline, designed to process the coarse scores into high quality segmentation masks. Our method is fast, easily trainable and remains highly effective in cases of limited training data. We perform extensive experiments on the challenging YouTube-VOS and DAVIS datasets. Our network achieves favorable performance, while operating at higher frame-rates compared to state-of-the-art. Code and trained models are available at https://github.com/andr345/frtm-vos. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Robinson_Learning_Fast_and_Robust_Target_Models_for_Video_Object_Segmentation_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.00908 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Robinson_Learning_Fast_and_Robust_Target_Models_for_Video_Object_Segmentation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Robinson_Learning_Fast_and_Robust_Target_Models_for_Video_Object_Segmentation_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Robinson_Learning_Fast_and_CVPR_2020_supplemental.pdf | null | null |
Transferring and Regularizing Prediction for Semantic Segmentation | Yiheng Zhang, Zhaofan Qiu, Ting Yao, Chong-Wah Ngo, Dong Liu, Tao Mei | Semantic segmentation often requires a large set of images with pixel-level annotations. In the view of extremely expensive expert labeling, recent research has shown that the models trained on photo-realistic synthetic data (e.g., computer games) with computer-generated annotations can be adapted to real images. Despite this progress, without constraining the prediction on real images, the models will easily overfit on synthetic data due to severe domain mismatch. In this paper, we novelly exploit the intrinsic properties of semantic segmentation to alleviate such problem for model transfer. Specifically, we present a Regularizer of Prediction Transfer (RPT) that imposes the intrinsic properties as constraints to regularize model transfer in an unsupervised fashion. These constraints include patch-level, cluster-level and context-level semantic prediction consistencies at different levels of image formation. As the transfer is label-free and data-driven, the robustness of prediction is addressed by selectively involving a subset of image regions for model regularization. Extensive experiments are conducted to verify the proposal of RPT on the transfer of models trained on GTA5 and SYNTHIA (synthetic data) to Cityscapes dataset (urban street scenes). RPT shows consistent improvements when injecting the constraints on several neural networks for semantic segmentation. More remarkably, when integrating RPT into the adversarial-based segmentation framework, we report to-date the best results: mIoU of 53.2%/51.7% when transferring from GTA5/SYNTHIA to Cityscapes, respectively. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zhang_Transferring_and_Regularizing_Prediction_for_Semantic_Segmentation_CVPR_2020_paper.pdf | http://arxiv.org/abs/2006.06570 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_Transferring_and_Regularizing_Prediction_for_Semantic_Segmentation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_Transferring_and_Regularizing_Prediction_for_Semantic_Segmentation_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Adaptive Loss-Aware Quantization for Multi-Bit Networks | Zhongnan Qu, Zimu Zhou, Yun Cheng, Lothar Thiele | We investigate the compression of deep neural networks by quantizing their weights and activations into multiple binary bases, known as multi-bit networks (MBNs), which accelerate the inference and reduce the storage for the deployment on low-resource mobile and embedded platforms. We propose Adaptive Loss-aware Quantization (ALQ), a new MBN quantization pipeline that is able to achieve an average bitwidth below one-bit without notable loss in inference accuracy. Unlike previous MBN quantization solutions that train a quantizer by minimizing the error to reconstruct full precision weights, ALQ directly minimizes the quantization-induced error on the loss function involving neither gradient approximation nor full precision maintenance. ALQ also exploits strategies including adaptive bitwidth, smooth bitwidth reduction, and iterative trained quantization to allow a smaller network size without loss in accuracy. Experiment results on popular image datasets show that ALQ outperforms state-of-the-art compressed networks in terms of both storage and accuracy. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Qu_Adaptive_Loss-Aware_Quantization_for_Multi-Bit_Networks_CVPR_2020_paper.pdf | http://arxiv.org/abs/1912.08883 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Qu_Adaptive_Loss-Aware_Quantization_for_Multi-Bit_Networks_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Qu_Adaptive_Loss-Aware_Quantization_for_Multi-Bit_Networks_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Qu_Adaptive_Loss-Aware_Quantization_CVPR_2020_supplemental.pdf | null | null |
MaskGAN: Towards Diverse and Interactive Facial Image Manipulation | Cheng-Han Lee, Ziwei Liu, Lingyun Wu, Ping Luo | Facial image manipulation has achieved great progress in recent years. However, previous methods either operate on a predefined set of face attributes or leave users little freedom to interactively manipulate images. To overcome these drawbacks, we propose a novel framework termed MaskGAN, enabling diverse and interactive face manipulation. Our key insight is that semantic masks serve as a suitable intermediate representation for flexible face manipulation with fidelity preservation. MaskGAN has two main components: 1) Dense Mapping Network (DMN) and 2) Editing Behavior Simulated Training (EBST). Specifically, DMN learns style mapping between a free-form user modified mask and a target image, enabling diverse generation results. EBST models the user editing behavior on the source mask, making the overall framework more robust to various manipulated inputs. Specifically, it introduces dual-editing consistency as the auxiliary supervision signal. To facilitate extensive studies, we construct a large-scale high-resolution face dataset with fine-grained mask annotations named CelebAMask-HQ. MaskGAN is comprehensively evaluated on two challenging tasks: attribute transfer and style copy, demonstrating superior performance over other state-of-the-art methods. The code, models, and dataset are available at https://github.com/switchablenorms/CelebAMask-HQ. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Lee_MaskGAN_Towards_Diverse_and_Interactive_Facial_Image_Manipulation_CVPR_2020_paper.pdf | http://arxiv.org/abs/1907.11922 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Lee_MaskGAN_Towards_Diverse_and_Interactive_Facial_Image_Manipulation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Lee_MaskGAN_Towards_Diverse_and_Interactive_Facial_Image_Manipulation_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Lee_MaskGAN_Towards_Diverse_CVPR_2020_supplemental.pdf | null | null |
ClusterFit: Improving Generalization of Visual Representations | Xueting Yan, Ishan Misra, Abhinav Gupta, Deepti Ghadiyaram, Dhruv Mahajan | Pre-training convolutional neural networks with weakly-supervised and self-supervised strategies is becoming increasingly popular for several computer vision tasks. However, due to the lack of strong discriminative signals, these learned representations may overfit to the pre-training objective (e.g., hashtag prediction) and not generalize well to downstream tasks. In this work, we present a simple strategy - ClusterFit to improve the robustness of the visual representations learned during pre-training. Given a dataset, we (a) cluster its features extracted from a pre-trained network using k-means and (b) re-train a new network from scratch on this dataset using cluster assignments as pseudo-labels. We empirically show that clustering helps reduce the pre-training task-specific information from the extracted features thereby minimizing overfitting to the same. Our approach is extensible to different pre-training frameworks -- weak- and self-supervised, modalities -- images and videos, and pre-training tasks -- object and action classification. Through extensive transfer learning experiments on 11 different target datasets of varied vocabularies and granularities, we show that ClusterFit significantly improves the representation quality compared to the state-of-the-art large-scale (millions / billions) weakly-supervised image and video models and self-supervised image models. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Yan_ClusterFit_Improving_Generalization_of_Visual_Representations_CVPR_2020_paper.pdf | http://arxiv.org/abs/1912.03330 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Yan_ClusterFit_Improving_Generalization_of_Visual_Representations_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Yan_ClusterFit_Improving_Generalization_of_Visual_Representations_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Robust Homography Estimation via Dual Principal Component Pursuit | Tianjiao Ding, Yunchen Yang, Zhihui Zhu, Daniel P. Robinson, Rene Vidal, Laurent Kneip, Manolis C. Tsakiris | We revisit robust estimation of homographies over point correspondences between two or three views, a fundamental problem in geometric vision. The analysis serves as a platform to support a rigorous investigation of Dual Principal Component Pursuit (DPCP) as a valid and powerful alternative to RANSAC for robust model fitting in multiple-view geometry. Homography fitting is cast as a robust nullspace estimation problem over either homographic or epipolar/trifocal embeddings. We prove that the nullspace of epipolar or trifocal embeddings in the homographic scenario, of dimension 3 and 6 for two and three views respectively, is defined by unique, computable homographies. Experiments show that DPCP performs on par with USAC with local optimization, while requiring an order of magnitude less computing time, and it also outperforms a recent deep learning implementation for homography estimation. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Ding_Robust_Homography_Estimation_via_Dual_Principal_Component_Pursuit_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Ding_Robust_Homography_Estimation_via_Dual_Principal_Component_Pursuit_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Ding_Robust_Homography_Estimation_via_Dual_Principal_Component_Pursuit_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Face X-Ray for More General Face Forgery Detection | Lingzhi Li, Jianmin Bao, Ting Zhang, Hao Yang, Dong Chen, Fang Wen, Baining Guo | In this paper we propose a novel image representation called face X-ray for detecting forgery in face images. The face X-ray of an input face image is a greyscale image that reveals whether the input image can be decomposed into the blending of two images from different sources. It does so by showing the blending boundary for a forged image and the absence of blending for a real image. We observe that most existing face manipulation methods share a common step: blending the altered face into an existing background image. For this reason, face X-ray provides an effective way for detecting forgery generated by most existing face manipulation algorithms. Face X-ray is general in the sense that it only assumes the existence of a blending step and does not rely on any knowledge of the artifacts associated with a specific face manipulation technique. Indeed, the algorithm for computing face X-ray can be trained without fake images generated by any of the state-of-the-art face manipulation methods. Extensive experiments show that face X-ray remains effective when applied to forgery generated by unseen face manipulation techniques, while most existing face forgery detection or deepfake detection algorithms experience a significant performance drop. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Li_Face_X-Ray_for_More_General_Face_Forgery_Detection_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Face_X-Ray_for_More_General_Face_Forgery_Detection_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Face_X-Ray_for_More_General_Face_Forgery_Detection_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Exploring Unlabeled Faces for Novel Attribute Discovery | Hyojin Bahng, Sunghyo Chung, Seungjoo Yoo, Jaegul Choo | Despite remarkable success in unpaired image-to-image translation, existing systems still require a large amount of labeled images. This is a bottleneck for their real-world applications; in practice, a model trained on labeled CelebA dataset does not work well for test images from a different distribution -- greatly limiting their application to unlabeled images of a much larger quantity. In this paper, we attempt to alleviate this necessity for labeled data in the facial image translation domain. We aim to explore the degree to which you can discover novel attributes from unlabeled faces and perform high-quality translation. To this end, we use prior knowledge about the visual world as guidance to discover novel attributes and transfer them via a novel normalization method. Experiments show that our method trained on unlabeled data produces high-quality translations, preserves identity, and be perceptually realistic, as good as, or better than, state-of-the-art methods trained on labeled data. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Bahng_Exploring_Unlabeled_Faces_for_Novel_Attribute_Discovery_CVPR_2020_paper.pdf | http://arxiv.org/abs/1912.03085 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Bahng_Exploring_Unlabeled_Faces_for_Novel_Attribute_Discovery_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Bahng_Exploring_Unlabeled_Faces_for_Novel_Attribute_Discovery_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Spatially Attentive Output Layer for Image Classification | Ildoo Kim, Woonhyuk Baek, Sungwoong Kim | Most convolutional neural networks (CNNs) for image classification use a global average pooling (GAP) followed by a fully-connected (FC) layer for output logits. However, this spatial aggregation procedure inherently restricts the utilization of location-specific information at the output layer, although this spatial information can be beneficial for classification. In this paper, we propose a novel spatial output layer on top of the existing convolutional feature maps to explicitly exploit the location-specific output information. In specific, given the spatial feature maps, we replace the previous GAP-FC layer with a spatially attentive output layer (SAOL) by employing a attention mask on spatial logits. The proposed location-specific attention selectively aggregates spatial logits within a target region, which leads to not only the performance improvement but also spatially interpretable outputs. Moreover, the proposed SAOL also permits to fully exploit location-specific self-supervision as well as self-distillation to enhance the generalization ability during training. The proposed SAOL with self-supervision and self-distillation can be easily plugged into existing CNNs. Experimental results on various classification tasks with representative architectures show consistent performance improvements by SAOL at almost the same computational cost. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Kim_Spatially_Attentive_Output_Layer_for_Image_Classification_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.07570 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Kim_Spatially_Attentive_Output_Layer_for_Image_Classification_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Kim_Spatially_Attentive_Output_Layer_for_Image_Classification_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
A Shared Multi-Attention Framework for Multi-Label Zero-Shot Learning | Dat Huynh, Ehsan Elhamifar | In this work, we develop a shared multi-attention model for multi-label zero-shot learning. We argue that designing attention mechanism for recognizing multiple seen and unseen labels in an image is a non-trivial task as there is no training signal to localize unseen labels and an image only contains a few present labels that need attentions out of thousands of possible labels. Therefore, instead of generating attentions for unseen labels which have unknown behaviors and could focus on irrelevant regions due to the lack of any training sample, we let the unseen labels select among a set of shared attentions which are trained to be label-agnostic and to focus on only relevant/foreground regions through our novel loss. Finally, we learn a compatibility function to distinguish labels based on the selected attention. We further propose a novel loss function that consists of three components guiding the attention to focus on diverse and relevant image regions while utilizing all attention features. By extensive experiments, we show that our method improves the state of the art by 2.9% and 1.4% F1 score on the NUS-WIDE and the large scale Open Images datasets, respectively. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Huynh_A_Shared_Multi-Attention_Framework_for_Multi-Label_Zero-Shot_Learning_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Huynh_A_Shared_Multi-Attention_Framework_for_Multi-Label_Zero-Shot_Learning_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Huynh_A_Shared_Multi-Attention_Framework_for_Multi-Label_Zero-Shot_Learning_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Huynh_A_Shared_Multi-Attention_CVPR_2020_supplemental.pdf | null | null |
Optical Flow in the Dark | Yinqiang Zheng, Mingfang Zhang, Feng Lu | Many successful optical flow estimation methods have been proposed, but they become invalid when tested in dark scenes because low-light scenarios are not considered when they are designed and current optical flow benchmark datasets lack low-light samples. Even if we preprocess to enhance the dark images, which achieves great visual perception, it still leads to poor optical flow results or even worse ones, because information like motion consistency may be broken while enhancing. We propose an end-to-end data-driven method that avoids error accumulation and learns optical flow directly from low-light noisy images. Specifically, we develop a method to synthesize large-scale low-light optical flow datasets by simulating the noise model on dark raw images. We also collect a new optical flow dataset in raw format with a large range of exposure to be used as a benchmark. The models trained on our synthetic dataset can relatively maintain optical flow accuracy as the image brightness descends and they outperform the existing methods greatly on low-light images. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zheng_Optical_Flow_in_the_Dark_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zheng_Optical_Flow_in_the_Dark_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zheng_Optical_Flow_in_the_Dark_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Painting Many Pasts: Synthesizing Time Lapse Videos of Paintings | Amy Zhao, Guha Balakrishnan, Kathleen M. Lewis, Fredo Durand, John V. Guttag, Adrian V. Dalca | We introduce a new video synthesis task: synthesizing time lapse videos depicting how a given painting might have been created. Artists paint using unique combinations of brushes, strokes, and colors. There are often many possible ways to create a given painting. Our goal is to learn to capture this rich range of possibilities. Creating distributions of long-term videos is a challenge for learning-based video synthesis methods. We present a probabilistic model that, given a single image of a completed painting, recurrently synthesizes steps of the painting process. We implement this model as a convolutional neural network, and introduce a novel training scheme to enable learning from a limited dataset of painting time lapses. We demonstrate that this model can be used to sample many time steps, enabling long-term stochastic video synthesis. We evaluate our method on digital and watercolor paintings collected from video websites, and show that human raters find our synthetic videos to be similar to time lapse videos produced by real artists. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zhao_Painting_Many_Pasts_Synthesizing_Time_Lapse_Videos_of_Paintings_CVPR_2020_paper.pdf | http://arxiv.org/abs/2001.01026 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhao_Painting_Many_Pasts_Synthesizing_Time_Lapse_Videos_of_Paintings_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhao_Painting_Many_Pasts_Synthesizing_Time_Lapse_Videos_of_Paintings_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Zhao_Painting_Many_Pasts_CVPR_2020_supplemental.zip | null | null |
Learning a Neural Solver for Multiple Object Tracking | Guillem Braso, Laura Leal-Taixe | Graphs offer a natural way to formulate Multiple Object Tracking (MOT) within the tracking-by-detection paradigm. However, they also introduce a major challenge for learning methods, as defining a model that can operate on such structured domain is not trivial. As a consequence, most learning-based work has been devoted to learning better features for MOT and then using these with well-established optimization frameworks. In this work, we exploit the classical network flow formulation of MOT to define a fully differentiable framework based on Message Passing Networks (MPNs). By operating directly on the graph domain, our method can reason globally over an entire set of detections and predict final solutions. Hence, we show that learning in MOT does not need to be restricted to feature extraction, but it can also be applied to the data association step. We show a significant improvement in both MOTA and IDF1 on three publicly available benchmarks. Our code is available at https://bit.ly/motsolv. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Braso_Learning_a_Neural_Solver_for_Multiple_Object_Tracking_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Braso_Learning_a_Neural_Solver_for_Multiple_Object_Tracking_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Braso_Learning_a_Neural_Solver_for_Multiple_Object_Tracking_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Braso_Learning_a_Neural_CVPR_2020_supplemental.pdf | null | null |
Rethinking Data Augmentation for Image Super-resolution: A Comprehensive Analysis and a New Strategy | Jaejun Yoo, Namhyuk Ahn, Kyung-Ah Sohn | Data augmentation is an effective way to improve the performance of deep networks. Unfortunately, current methods are mostly developed for high-level vision tasks (e.g., classification) and few are studied for low-level vision tasks (e.g., image restoration). In this paper, we provide a comprehensive analysis of the existing augmentation methods applied to the super-resolution task. We find that the methods discarding or manipulating the pixels or features too much hamper the image restoration, where the spatial relationship is very important. Based on our analyses, we propose CutBlur that cuts a low-resolution patch and pastes it to the corresponding high-resolution image region and vice versa. The key intuition of CutBlur is to enable a model to learn not only "how" but also "where" to super-resolve an image. By doing so, the model can understand "how much", instead of blindly learning to apply super-resolution to every given pixel. Our method consistently and significantly improves the performance across various scenarios, especially when the model size is big and the data is collected under real-world environments. We also show that our method improves other low-level vision tasks, such as denoising and compression artifact removal. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Yoo_Rethinking_Data_Augmentation_for_Image_Super-resolution_A_Comprehensive_Analysis_and_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=aHKv-Om-S-4 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Yoo_Rethinking_Data_Augmentation_for_Image_Super-resolution_A_Comprehensive_Analysis_and_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Yoo_Rethinking_Data_Augmentation_for_Image_Super-resolution_A_Comprehensive_Analysis_and_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Yoo_Rethinking_Data_Augmentation_CVPR_2020_supplemental.pdf | null | null |
Evade Deep Image Retrieval by Stashing Private Images in the Hash Space | Yanru Xiao, Cong Wang, Xing Gao | With the rapid growth of visual content, deep learning to hash is gaining popularity in the image retrieval community recently. Although it greatly facilitates search efficiency, privacy is also at risks when images on the web are retrieved at a large scale and exploited as a rich mine of personal information. An adversary can extract private images by querying similar images from the targeted category for any usable model. Existing methods based on image processing preserve privacy at a sacrifice of perceptual quality. In this paper, we propose a new mechanism based on adversarial examples to "stash" private images in the deep hash space while maintaining perceptual similarity. We first find that a simple approach of hamming distance maximization is not robust against brute-force adversaries. Then we develop a new loss function by maximizing the hamming distance to not only the original category, but also the centers from all the classes, partitioned into clusters of various sizes. The extensive experiment shows that the proposed defense can harden the attacker's efforts by 2-7 orders of magnitude, without significant increase of computational overhead and perceptual degradation. We also demonstrate 30-60% transferability in hash space with a black-box setting. The code is available at: https://github.com/sugarruy/hashstash | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Xiao_Evade_Deep_Image_Retrieval_by_Stashing_Private_Images_in_the_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Xiao_Evade_Deep_Image_Retrieval_by_Stashing_Private_Images_in_the_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Xiao_Evade_Deep_Image_Retrieval_by_Stashing_Private_Images_in_the_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
GanHand: Predicting Human Grasp Affordances in Multi-Object Scenes | Enric Corona, Albert Pumarola, Guillem Alenya, Francesc Moreno-Noguer, Gregory Rogez | The rise of deep learning has brought remarkable progress in estimating hand geometry from images where the hands are part of the scene. This paper focuses on a new problem not explored so far, consisting in predicting how a human would grasp one or several objects, given a single RGB image of these objects. This is a problem with enormous potential in e.g. augmented reality, robotics or prosthetic design. In order to predict feasible grasps, we need to understand the semantic content of the image, its geometric structure and all potential interactions with a hand physical model. To this end, we introduce a generative model that jointly reasons in all these levels and 1) regresses the 3D shape and pose of the objects in the scene; 2) estimates the grasp types; and 3) refines the 51-DoF of a 3D hand model that minimize a graspability loss. To train this model we build the YCB-Affordance dataset, that contains more than 133k images of 21 objects in the YCB-Video dataset. We have annotated these images with more than 28M plausible 3D human grasps according to a 33-class taxonomy. A thorough evaluation in synthetic and real images shows that our model can robustly predict realistic grasps, even in cluttered scenes with multiple objects in close contact. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Corona_GanHand_Predicting_Human_Grasp_Affordances_in_Multi-Object_Scenes_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Corona_GanHand_Predicting_Human_Grasp_Affordances_in_Multi-Object_Scenes_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Corona_GanHand_Predicting_Human_Grasp_Affordances_in_Multi-Object_Scenes_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
EventSR: From Asynchronous Events to Image Reconstruction, Restoration, and Super-Resolution via End-to-End Adversarial Learning | Lin Wang, Tae-Kyun Kim, Kuk-Jin Yoon | Event cameras sense intensity changes and have many advantages over conventional cameras. To take advantage of event cameras, some methods have been proposed to reconstruct intensity images from event streams. However, the outputs are still in low resolution (LR), noisy, and unrealistic. The low-quality outputs stem broader applications of event cameras, where high spatial resolution (HR) is needed as well as high temporal resolution, dynamic range, and no motion blur. We consider the problem of reconstructing and super-resolving intensity images from pure events, when no ground truth (GT) HR images and down-sampling kernels are available. To tackle the challenges, we propose a novel end-to-end pipeline that reconstructs LR images from event streams, enhances the image qualities and upsamples the enhanced images, called EventSR. For the absence of real GT images, our method is primarily unsupervised, deploying adversarial learning. To train EventSR, we create an open dataset including both real-world and simulated scenes. The use of both datasets boosts up the network performance, and the network architectures and various loss functions in each phase help improve the image qualities. The whole pipeline is trained in three phases. While each phase is mainly for one of the three tasks, the networks in earlier phases are fine-tuned by respective loss functions in an end-to-end manner. Experimental results show that EventSR generates high-quality SR images from events for both simulated and real-world data. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wang_EventSR_From_Asynchronous_Events_to_Image_Reconstruction_Restoration_and_Super-Resolution_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.07640 | https://www.youtube.com/watch?v=_yVlvrZSGcE | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_EventSR_From_Asynchronous_Events_to_Image_Reconstruction_Restoration_and_Super-Resolution_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_EventSR_From_Asynchronous_Events_to_Image_Reconstruction_Restoration_and_Super-Resolution_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Quaternion Product Units for Deep Learning on 3D Rotation Groups | Xuan Zhang, Shaofei Qin, Yi Xu, Hongteng Xu | We propose a novel quaternion product unit (QPU) to represent data on 3D rotation groups. The QPU leverages quaternion algebra and the law of 3D rotation group, representing 3D rotation data as quaternions and merging them via a weighted chain of Hamilton products. We prove that the representations derived by the proposed QPU can be disentangled into "rotation-invariant" features and "rotation-equivariant" features, respectively, which supports the rationality and the efficiency of the QPU in theory. We design quaternion neural networks based on our QPUs and make our models compatible with existing deep learning models. Experiments on both synthetic and real-world data show that the proposed QPU is beneficial for the learning tasks requiring rotation robustness. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zhang_Quaternion_Product_Units_for_Deep_Learning_on_3D_Rotation_Groups_CVPR_2020_paper.pdf | http://arxiv.org/abs/1912.07791 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_Quaternion_Product_Units_for_Deep_Learning_on_3D_Rotation_Groups_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_Quaternion_Product_Units_for_Deep_Learning_on_3D_Rotation_Groups_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Zhang_Quaternion_Product_Units_CVPR_2020_supplemental.pdf | null | null |
3D Human Mesh Regression With Dense Correspondence | Wang Zeng, Wanli Ouyang, Ping Luo, Wentao Liu, Xiaogang Wang | Estimating 3D mesh of the human body from a single 2D image is an important task with many applications such as augmented reality and Human-Robot interaction. However, prior works reconstructed 3D mesh from global image feature extracted by using convolutional neural network (CNN), where the dense correspondences between the mesh surface and the image pixels are missing, leading to suboptimal solution. This paper proposes a model-free 3D human mesh estimation framework, named DecoMR, which explicitly establishes the dense correspondence between the mesh and the local image features in the UV space (i.e. a 2D space used for texture mapping of 3D mesh). DecoMR first predicts pixel-to-surface dense correspondence map (i.e., IUV image), with which we transfer local features from the image space to the UV space. Then the transferred local image features are processed in the UV space to regress a location map, which is well aligned with transferred features. Finally we reconstruct 3D human mesh from the regressed location map with a predefined mapping function. We also observe that the existing discontinuous UV map are unfriendly to the learning of network. Therefore, we propose a novel UV map that maintains most of the neighboring relations on the original mesh surface. Experiments demonstrate that our proposed local feature alignment and continuous UV map outperforms existing 3D mesh based methods on multiple public benchmarks. Code will be made available at https: //github.com/zengwang430521/DecoMR. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zeng_3D_Human_Mesh_Regression_With_Dense_Correspondence_CVPR_2020_paper.pdf | http://arxiv.org/abs/2006.05734 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zeng_3D_Human_Mesh_Regression_With_Dense_Correspondence_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zeng_3D_Human_Mesh_Regression_With_Dense_Correspondence_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Zeng_3D_Human_Mesh_CVPR_2020_supplemental.pdf | null | null |
Learning to Shadow Hand-Drawn Sketches | Qingyuan Zheng, Zhuoru Li, Adam Bargteil | We present a fully automatic method to generate detailed and accurate artistic shadows from pairs of line drawing sketches and lighting directions. We also contribute a new dataset of one thousand examples of pairs of line drawings and shadows that are tagged with lighting directions. Remarkably, the generated shadows quickly communicate the underlying 3D structure of the sketched scene. Consequently, the shadows generated by our approach can be used directly or as an excellent starting point for artists. We demonstrate that the deep learning network we propose takes a hand-drawn sketch, builds a 3D model in latent space, and renders the resulting shadows. The generated shadows respect the hand-drawn lines and underlying 3D space and contain sophisticated and accurate details, such as self-shadowing effects. Moreover, the generated shadows contain artistic effects, such as rim lighting or halos appearing from backlighting, that would be achievable with traditional 3D rendering methods. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zheng_Learning_to_Shadow_Hand-Drawn_Sketches_CVPR_2020_paper.pdf | http://arxiv.org/abs/2002.11812 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zheng_Learning_to_Shadow_Hand-Drawn_Sketches_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zheng_Learning_to_Shadow_Hand-Drawn_Sketches_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Zheng_Learning_to_Shadow_CVPR_2020_supplemental.zip | null | null |
Optimizing Rank-Based Metrics With Blackbox Differentiation | Michal Rolinek, Vit Musil, Anselm Paulus, Marin Vlastelica, Claudio Michaelis, Georg Martius | Rank-based metrics are some of the most widely used criteria for performance evaluation of computer vision models. Despite years of effort, direct optimization for these metrics remains a challenge due to their non-differentiable and non-decomposable nature. We present an efficient, theoretically sound, and general method for differentiating rank-based metrics with mini-batch gradient descent. In addition, we address optimization instability and sparsity of the supervision signal that both arise from using rank-based metrics as optimization targets. Resulting losses based on recall and Average Precision are applied to image retrieval and object detection tasks. We obtain performance that is competitive with state-of-the-art on standard image retrieval datasets and consistently improve performance of near state-of-the-art object detectors. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Rolinek_Optimizing_Rank-Based_Metrics_With_Blackbox_Differentiation_CVPR_2020_paper.pdf | http://arxiv.org/abs/1912.03500 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Rolinek_Optimizing_Rank-Based_Metrics_With_Blackbox_Differentiation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Rolinek_Optimizing_Rank-Based_Metrics_With_Blackbox_Differentiation_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Rolinek_Optimizing_Rank-Based_Metrics_CVPR_2020_supplemental.pdf | null | null |
Fast Texture Synthesis via Pseudo Optimizer | Wu Shi, Yu Qiao | Texture synthesis using deep neural networks can generate high quality and diversified textures. However, it usually requires a heavy optimization process. The following works accelerate the process by using feed-forward networks, but at the cost of scalability. diversity or quality. We propose a new efficient method that aims to simulate the optimization process while retains most of the properties. Our method takes a noise image and the gradients from a descriptor network as inputs, and synthesize a refined image with respect to the target image. The proposed method can synthesize images with better quality and diversity than the other fast synthesis methods do. Moreover, our method trained on a large scale dataset can generalize to synthesize unseen textures. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Shi_Fast_Texture_Synthesis_via_Pseudo_Optimizer_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Shi_Fast_Texture_Synthesis_via_Pseudo_Optimizer_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Shi_Fast_Texture_Synthesis_via_Pseudo_Optimizer_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Shi_Fast_Texture_Synthesis_CVPR_2020_supplemental.pdf | null | null |
ENSEI: Efficient Secure Inference via Frequency-Domain Homomorphic Convolution for Privacy-Preserving Visual Recognition | Song Bian, Tianchen Wang, Masayuki Hiromoto, Yiyu Shi, Takashi Sato | In this work, we propose ENSEI, a secure inference (SI) framework based on the frequency-domain secure convolution (FDSC) protocol for the efficient execution of image inference in the encrypted domain. Our observation is that, under the combination of homomorphic encryption and secret sharing, homomorphic convolution can be obliviously carried out in the frequency domain, significantly simplifying the related computations. We provide protocol designs and parameter derivations for number-theoretic transform (NTT) based FDSC. In the experiment, we thoroughly study the accuracy-efficiency trade-offs between time- and frequency-domain homomorphic convolution. With ENSEI, compared to the best known works, we achieve 5--11x online time reduction, up to 33x setup time reduction, and up to 10x reduction in the overall inference time. A further 33% of bandwidth reductions can be obtained on binary neural networks with only 3% of accuracy degradation on the CIFAR-10 dataset. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Bian_ENSEI_Efficient_Secure_Inference_via_Frequency-Domain_Homomorphic_Convolution_for_Privacy-Preserving_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.05328 | https://www.youtube.com/watch?v=qlweWKjbie0 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Bian_ENSEI_Efficient_Secure_Inference_via_Frequency-Domain_Homomorphic_Convolution_for_Privacy-Preserving_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Bian_ENSEI_Efficient_Secure_Inference_via_Frequency-Domain_Homomorphic_Convolution_for_Privacy-Preserving_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Bian_ENSEI_Efficient_Secure_CVPR_2020_supplemental.pdf | null | null |
Learning Dynamic Relationships for 3D Human Motion Prediction | Qiongjie Cui, Huaijiang Sun, Fei Yang | 3D human motion prediction, i.e., forecasting future sequences from given historical poses, is a fundamental task for action analysis, human-computer interaction, machine intelligence. Recently, the state-of-the-art method assumes that the whole human motion sequence involves a fully-connected graph formed by links between each joint pair. Although encouraging performance has been made, due to the neglect of the inherent and meaningful characteristics of the natural connectivity of human joints, unexpected results may be produced. Moreover, such a complicated topology greatly increases the training difficulty. To tackle these issues, we propose a deep generative model based on graph networks and adversarial learning. Specifically, the skeleton pose is represented as a novel dynamic graph, in which natural connectivities of the joint pairs are exploited explicitly, and the links of geometrically separated joints can also be learned implicitly. Notably, in the proposed model, the natural connection strength is adaptively learned, whereas, in previous schemes, it was constant. Our approach is evaluated on two representations (i.e., angle-based, position-based) from various large-scale 3D skeleton benchmarks (e.g., H3.6M, CMU, 3DPW MoCap). Extensive experiments demonstrate that our approach achieves significant improvements against existing baselines in accuracy and visualization. Code will be available at https://github.com/cuiqiongjie/LDRGCN. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Cui_Learning_Dynamic_Relationships_for_3D_Human_Motion_Prediction_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Cui_Learning_Dynamic_Relationships_for_3D_Human_Motion_Prediction_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Cui_Learning_Dynamic_Relationships_for_3D_Human_Motion_Prediction_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
SAM: The Sensitivity of Attribution Methods to Hyperparameters | Naman Bansal, Chirag Agarwal, Anh Nguyen | Attribution methods can provide powerful insights into the reasons for a classifier's decision. We argue that a key desideratum of an explanation is its robustness to input hyperparameter changes that are often randomly set or empirically tuned. High sensitivity to arbitrary hyperparameter choices does not only impede reproducibility but also questions the correctness of an explanation and impairs the trust by end-users. In this paper, we provide a thorough empirical study on the sensitivity of existing attribution methods. We found an alarming trend that many methods are highly sensitive to changes in their common hyperparameters e.g. even changing a random seed can yield a different explanation! In contrast, explanations generated for robust classifiers that are trained to be invariant to pixel-wise perturbations are surprisingly more robust. Interestingly, such sensitivity is not reflected in the average explanation correctness scores over the entire dataset as commonly reported in the literature. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Bansal_SAM_The_Sensitivity_of_Attribution_Methods_to_Hyperparameters_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.08754 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Bansal_SAM_The_Sensitivity_of_Attribution_Methods_to_Hyperparameters_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Bansal_SAM_The_Sensitivity_of_Attribution_Methods_to_Hyperparameters_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Bansal_SAM_The_Sensitivity_CVPR_2020_supplemental.pdf | null | null |
Learning to Optimize on SPD Manifolds | Zhi Gao, Yuwei Wu, Yunde Jia, Mehrtash Harandi | Many tasks in computer vision and machine learning are modeled as optimization problems with constraints in the form of Symmetric Positive Definite (SPD) matrices. Solving such optimization problems is challenging due to the non-linearity of the SPD manifold, making optimization with SPD constraints heavily relying on expert knowledge and human involvement. In this paper, we propose a meta-learning method to automatically learn an iterative optimizer on SPD manifolds. Specifically, we introduce a novel recurrent model that takes into account the structure of input gradients and identifies the updating scheme of optimization. We parameterize the optimizer by the recurrent model and utilize Riemannian operations to ensure that our method is faithful to the geometry of SPD manifolds. Compared with existing SPD optimizers, our optimizer effectively exploits the underlying data distribution and learns a better optimization trajectory in a data-driven manner. Extensive experiments on various computer vision tasks including metric nearness, clustering, and similarity learning demonstrate that our optimizer outperforms existing state-of-the-art methods consistently. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Gao_Learning_to_Optimize_on_SPD_Manifolds_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Gao_Learning_to_Optimize_on_SPD_Manifolds_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Gao_Learning_to_Optimize_on_SPD_Manifolds_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Gao_Learning_to_Optimize_CVPR_2020_supplemental.pdf | null | null |
RGBD-Dog: Predicting Canine Pose from RGBD Sensors | Sinead Kearney, Wenbin Li, Martin Parsons, Kwang In Kim, Darren Cosker | The automatic extraction of animal 3D pose from images without markers is of interest in a range of scientific fields. Most work to date predicts animal pose from RGB images, based on 2D labelling of joint positions. However, due to the difficult nature of obtaining training data, no ground truth dataset of 3D animal motion is available to quantitatively evaluate these approaches. In addition, a lack of 3D animal pose data also makes it difficult to train 3D pose-prediction methods in a similar manner to the popular field of body-pose prediction. In our work, we focus on the problem of 3D canine pose estimation from RGBD images, recording a diverse range of dog breeds with several Microsoft Kinect v2s, simultaneously obtaining the 3D ground truth skeleton via a motion capture system. We generate a dataset of synthetic RGBD images from this data. A stacked hourglass network is trained to predict 3D joint locations, which is then constrained using prior models of shape and pose. We evaluate our model on both synthetic and real RGBD images and compare our results to previously published work fitting canine models to images. Finally, despite our training set consisting only of dog data, visual inspection implies that our network can produce good predictions for images of other quadrupeds - e.g. horses or cats - when their pose is similar to that contained in our training set. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Kearney_RGBD-Dog_Predicting_Canine_Pose_from_RGBD_Sensors_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Kearney_RGBD-Dog_Predicting_Canine_Pose_from_RGBD_Sensors_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Kearney_RGBD-Dog_Predicting_Canine_Pose_from_RGBD_Sensors_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Kearney_RGBD-Dog_Predicting_Canine_CVPR_2020_supplemental.zip | null | null |
CookGAN: Causality Based Text-to-Image Synthesis | Bin Zhu, Chong-Wah Ngo | This paper addresses the problem of text-to-image synthesis from a new perspective, i.e., the cause-and-effect chain in image generation. Causality is a common phenomenon in cooking. The dish appearance changes depending on the cooking actions and ingredients. The challenge of synthesis is that a generated image should depict the visual result of action-on-object. This paper presents a new network architecture, CookGAN, that mimics visual effect in causality chain, preserves fine-grained details and progressively upsamples image. Particularly, a cooking simulator sub-network is proposed to incrementally make changes to food images based on the interaction between ingredients and cooking methods over a series of steps. Experiments on Recipe1M verify that CookGAN manages to generate food images with reasonably impressive inception score. Furthermore, the images are semantically interpretable and manipulable. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zhu_CookGAN_Causality_Based_Text-to-Image_Synthesis_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhu_CookGAN_Causality_Based_Text-to-Image_Synthesis_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhu_CookGAN_Causality_Based_Text-to-Image_Synthesis_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Zhu_CookGAN_Causality_Based_CVPR_2020_supplemental.pdf | null | null |
Image Based Virtual Try-On Network From Unpaired Data | Assaf Neuberger, Eran Borenstein, Bar Hilleli, Eduard Oks, Sharon Alpert | This paper presents a new image-based virtual try-on approach (Outfit-VITON) that helps visualize how a composition of clothing items selected from various reference images form a cohesive outfit on a person in a query image. Our algorithm has two distinctive properties. First, it is inexpensive, as it simply requires a large set of single (non-corresponding) images (both real and catalog) of people wearing various garments without explicit 3D information. The training phase requires only single images, eliminating the need for manually creating image pairs, where one image shows a person wearing a particular garment and the other shows the same catalog garment alone. Secondly, it can synthesize images of multiple garments composed into a single, coherent outfit; and it enables control of the type of garments rendered in the final outfit. Once trained, our approach can then synthesize a cohesive outfit from multiple images of clothed human models, while fitting the outfit to the body shape and pose of the query person. An online optimization step takes care of fine details such as intricate textures and logos. Quantitative and qualitative evaluations on an image dataset containing large shape and style variations demonstrate superior accuracy compared to existing state-of-the-art methods, especially when dealing with highly detailed garments. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Neuberger_Image_Based_Virtual_Try-On_Network_From_Unpaired_Data_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Neuberger_Image_Based_Virtual_Try-On_Network_From_Unpaired_Data_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Neuberger_Image_Based_Virtual_Try-On_Network_From_Unpaired_Data_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Neuberger_Image_Based_Virtual_CVPR_2020_supplemental.pdf | null | null |
EventCap: Monocular 3D Capture of High-Speed Human Motions Using an Event Camera | Lan Xu, Weipeng Xu, Vladislav Golyanik, Marc Habermann, Lu Fang, Christian Theobalt | The high frame rate is a critical requirement for capturing fast human motions. In this setting, existing markerless image-based methods are constrained by the lighting requirement, the high data bandwidth and the consequent high computation overhead. In this paper, we propose EventCap -- the first approach for 3D capturing of high-speed human motions using a single event camera. Our method combines model-based optimization and CNN-based human pose detection to capture high frequency motion details and to reduce the drifting in the tracking. As a result, we can capture fast motions at millisecond resolution with significantly higher data efficiency than using high frame rate videos. Experiments on our new event-based fast human motion dataset demonstrate the effectiveness and accuracy of our method, as well as its robustness to challenging lighting conditions. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Xu_EventCap_Monocular_3D_Capture_of_High-Speed_Human_Motions_Using_an_CVPR_2020_paper.pdf | http://arxiv.org/abs/1908.11505 | https://www.youtube.com/watch?v=cpKKCisB5mQ | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Xu_EventCap_Monocular_3D_Capture_of_High-Speed_Human_Motions_Using_an_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Xu_EventCap_Monocular_3D_Capture_of_High-Speed_Human_Motions_Using_an_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Xu_EventCap_Monocular_3D_CVPR_2020_supplemental.pdf | null | null |
Dreaming to Distill: Data-Free Knowledge Transfer via DeepInversion | Hongxu Yin, Pavlo Molchanov, Jose M. Alvarez, Zhizhong Li, Arun Mallya, Derek Hoiem, Niraj K. Jha, Jan Kautz | We introduce DeepInversion, a new method for synthesizing images from the image distribution used to train a deep neural network. We "invert" a trained network (teacher) to synthesize class-conditional input images starting from random noise, without using any additional information about the training dataset. Keeping the teacher fixed, our method optimizes the input while regularizing the distribution of intermediate feature maps using information stored in the batch normalization layers of the teacher. Further, we improve the diversity of synthesized images using Adaptive DeepInversion, which maximizes the Jensen-Shannon divergence between the teacher and student network logits. The resulting synthesized images from networks trained on the CIFAR-10 and ImageNet datasets demonstrate high fidelity and degree of realism, and help enable a new breed of data-free applications - ones that do not require any real images or labeled data. We demonstrate the applicability of our proposed method to three tasks of immense practical importance - (i) data-free network pruning, (ii) data-free knowledge transfer, and (iii) data-free continual learning. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Yin_Dreaming_to_Distill_Data-Free_Knowledge_Transfer_via_DeepInversion_CVPR_2020_paper.pdf | http://arxiv.org/abs/1912.08795 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Yin_Dreaming_to_Distill_Data-Free_Knowledge_Transfer_via_DeepInversion_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Yin_Dreaming_to_Distill_Data-Free_Knowledge_Transfer_via_DeepInversion_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Yin_Dreaming_to_Distill_CVPR_2020_supplemental.pdf | null | null |
Spherical Space Domain Adaptation With Robust Pseudo-Label Loss | Xiang Gu, Jian Sun, Zongben Xu | Adversarial domain adaptation (DA) has been an effective approach for learning domain-invariant features by adversarial training. In this paper, we propose a novel adversarial DA approach completely defined in spherical feature space, in which we define spherical classifier for label prediction and spherical domain discriminator for discriminating domain labels. To utilize pseudo-label robustly, we develop a robust pseudo-label loss in the spherical feature space, which weights the importance of estimated labels of target data by posterior probability of correct labeling, modeled by Gaussian-uniform mixture model in spherical feature space. Extensive experiments show that our method achieves state-of-the-art results, and also confirm effectiveness of spherical classifier, spherical discriminator and spherical robust pseudo-label loss. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Gu_Spherical_Space_Domain_Adaptation_With_Robust_Pseudo-Label_Loss_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Gu_Spherical_Space_Domain_Adaptation_With_Robust_Pseudo-Label_Loss_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Gu_Spherical_Space_Domain_Adaptation_With_Robust_Pseudo-Label_Loss_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Gu_Spherical_Space_Domain_CVPR_2020_supplemental.pdf | null | null |
Approximating shapes in images with low-complexity polygons | Muxingzi Li, Florent Lafarge, Renaud Marlet | We present an algorithm for extracting and vectorizing objects in images with polygons. Departing from a polygonal partition that oversegments an image into convex cells, the algorithm refines the geometry of the partition while labeling its cells by a semantic class. The result is a set of polygons, each capturing an object in the image. The quality of a configuration is measured by an energy that accounts for both the fidelity to input data and the complexity of the output polygons. To efficiently explore the configuration space, we perform splitting and merging operations in tandem on the cells of the polygonal partition. The exploration mechanism is controlled by a priority queue that sorts the operations most likely to decrease the energy. We show the potential of our algorithm on different types of scenes, from organic shapes to man-made objects through floor maps, and demonstrate its efficiency compared to existing vectorization methods. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Li_Approximating_shapes_in_images_with_low-complexity_polygons_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Approximating_shapes_in_images_with_low-complexity_polygons_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Approximating_shapes_in_images_with_low-complexity_polygons_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Vec2Face: Unveil Human Faces From Their Blackbox Features in Face Recognition | Chi Nhan Duong, Thanh-Dat Truong, Khoa Luu, Kha Gia Quach, Hung Bui, Kaushik Roy | Unveiling face images of a subject given his/her high-level representations extracted from a blackbox Face Recognition engine is extremely challenging. It is because the limitations of accessible information from that engine including its structure and uninterpretable extracted features. This paper presents a novel generative structure with Bijective Metric Learning, namely Bijective Generative Adversarial Networks in a Distillation framework (DiBiGAN), for synthesizing faces of an identity given that person's features. In order to effectively address this problem, this work firstly introduces a bijective metric so that the distance measurement and metric learning process can be directly adopted in image domain for an image reconstruction task. Secondly, a distillation process is introduced to maximize the information exploited from the blackbox face recognition engine. Then a Feature-Conditional Generator Structure with Exponential Weighting Strategy is presented for a more robust generator that can synthesize realistic faces with ID preservation. Results on several benchmarking datasets including CelebA, LFW, AgeDB, CFP-FP against matching engines have demonstrated the effectiveness of DiBiGAN on both image realism and ID preservation properties. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Duong_Vec2Face_Unveil_Human_Faces_From_Their_Blackbox_Features_in_Face_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.06958 | https://www.youtube.com/watch?v=-ouFyKjbkoc | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Duong_Vec2Face_Unveil_Human_Faces_From_Their_Blackbox_Features_in_Face_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Duong_Vec2Face_Unveil_Human_Faces_From_Their_Blackbox_Features_in_Face_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
SiamCAR: Siamese Fully Convolutional Classification and Regression for Visual Tracking | Dongyan Guo, Jun Wang, Ying Cui, Zhenhua Wang, Shengyong Chen | By decomposing the visual tracking task into two subproblems as classification for pixel category and regression for object bounding box at this pixel, we propose a novel fully convolutional Siamese network to solve visual tracking end-to-end in a per-pixel manner. The proposed framework SiamCAR consists of two simple subnetworks: one Siamese subnetwork for feature extraction and one classification-regression subnetwork for bounding box prediction. Different from state-of-the-art trackers like Siamese-RPN, SiamRPN++ and SPM, which are based on region proposal, the proposed framework is both proposal and anchor free. Consequently, we are able to avoid the tricky hyper-parameter tuning of anchors and reduce human intervention. The proposed framework is simple, neat and effective. Extensive experiments and comparisons with state-of-the-art trackers are conducted on challenging benchmarks including GOT-10K, LaSOT, UAV123 and OTB-50. Without bells and whistles, our SiamCAR achieves the leading performance with a considerable real-time speed. The code is available at https://github.com/ohhhyeahhh/SiamCAR. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Guo_SiamCAR_Siamese_Fully_Convolutional_Classification_and_Regression_for_Visual_Tracking_CVPR_2020_paper.pdf | http://arxiv.org/abs/1911.07241 | https://www.youtube.com/watch?v=PflVU-iQSt8 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Guo_SiamCAR_Siamese_Fully_Convolutional_Classification_and_Regression_for_Visual_Tracking_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Guo_SiamCAR_Siamese_Fully_Convolutional_Classification_and_Regression_for_Visual_Tracking_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Deep Image Spatial Transformation for Person Image Generation | Yurui Ren, Xiaoming Yu, Junming Chen, Thomas H. Li, Ge Li | Pose-guided person image generation is to transform a source person image to a target pose. This task requires spatial manipulations of source data. However, Convolutional Neural Networks are limited by the lack of ability to spatially transform the inputs. In this paper, we propose a differentiable global-flow local-attention framework to reassemble the inputs at the feature level. Specifically, our model first calculates the global correlations between sources and targets to predict flow fields. Then, the flowed local patch pairs are extracted from the feature maps to calculate the local attention coefficients. Finally, we warp the source features using a content-aware sampling method with the obtained local attention coefficients. The results of both subjective and objective experiments demonstrate the superiority of our model. Besides, additional results in video animation and view synthesis show that our model is applicable to other tasks requiring spatial transformation. Our source code is available at https://github.com/RenYurui/Global-Flow-Local-Attention. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Ren_Deep_Image_Spatial_Transformation_for_Person_Image_Generation_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.00696 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Ren_Deep_Image_Spatial_Transformation_for_Person_Image_Generation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Ren_Deep_Image_Spatial_Transformation_for_Person_Image_Generation_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Fashion Editing With Adversarial Parsing Learning | Haoye Dong, Xiaodan Liang, Yixuan Zhang, Xujie Zhang, Xiaohui Shen, Zhenyu Xie, Bowen Wu, Jian Yin | Interactive fashion image manipulation, which enables users to edit images with sketches and color strokes, is an interesting research problem with great application value. Existing works often treat it as a general inpainting task and do not fully leverage the semantic structural information in fashion images. Moreover, they directly utilize conventional convolution and normalization layers to restore the incomplete image, which tends to wash away the sketch and color information. In this paper, we propose a novel Fashion Editing Generative Adversarial Network (FE-GAN), which is capable of manipulating fashion images by free-form sketches and sparse color strokes. FE-GAN consists of two modules: 1) a free-form parsing network that learns to control the human parsing generation by manipulating sketch and color; 2) a parsing-aware inpainting network that renders detailed textures with semantic guidance from the human parsing map. A new attention normalization layer is further applied at multiple scales in the decoder of the inpainting network to enhance the quality of the synthesized image. Extensive experiments on high-resolution fashion image datasets demonstrate that the proposed FE-GAN significantly outperforms the state-of-the-art methods on fashion image manipulation. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Dong_Fashion_Editing_With_Adversarial_Parsing_Learning_CVPR_2020_paper.pdf | http://arxiv.org/abs/1906.00884 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Dong_Fashion_Editing_With_Adversarial_Parsing_Learning_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Dong_Fashion_Editing_With_Adversarial_Parsing_Learning_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Multiview-Consistent Semi-Supervised Learning for 3D Human Pose Estimation | Rahul Mitra, Nitesh B. Gundavarapu, Abhishek Sharma, Arjun Jain | The best performing methods for 3D human pose estimation from monocular images require large amounts of in-the-wild 2D and controlled 3D pose annotated datasets which are costly and require sophisticated systems to acquire. To reduce this annotation dependency, we propose Multiview-Consistent Semi Supervised Learning (MCSS) framework that utilizes similarity in pose information from unannotated, uncalibrated but synchronized multi-view videos of human motions as additional weak supervision signal to guide 3D human pose regression. Our framework applies hard-negative mining based on temporal relations in multi-view videos to arrive at a multi-view consistent pose embedding and when jointly trained with limited 3D pose annotations, our approach improves the baseline by 25% and state-of-the-art by 8.7%, whilst using substantially smaller networks. Lastly, but importantly, we demonstrate the advantages of the learned embedding and establish view-invariant pose retrieval benchmarks on two popular, publicly available multi-view human pose datasets, Human 3.6M and MPI-INF-3DHP, to facilitate future research. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Mitra_Multiview-Consistent_Semi-Supervised_Learning_for_3D_Human_Pose_Estimation_CVPR_2020_paper.pdf | http://arxiv.org/abs/1908.05293 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Mitra_Multiview-Consistent_Semi-Supervised_Learning_for_3D_Human_Pose_Estimation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Mitra_Multiview-Consistent_Semi-Supervised_Learning_for_3D_Human_Pose_Estimation_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Mitra_Multiview-Consistent_Semi-Supervised_Learning_CVPR_2020_supplemental.pdf | null | null |
Attack to Explain Deep Representation | Mohammad A. A. K. Jalwana, Naveed Akhtar, Mohammed Bennamoun, Ajmal Mian | Deep visual models are susceptible to extremely low magnitude perturbations to input images. Though carefully crafted, the perturbation patterns generally appear noisy, yet they are able to perform controlled manipulation of model predictions. This observation is used to argue that deep representation is misaligned with human perception. This paper counter-argues and proposes the first attack on deep learning that aims at explaining the learned representation instead of fooling it. By extending the input domain of the manipulative signal and employing a model faithful channelling, we iteratively accumulate adversarial perturbations for a deep model. The accumulated signal gradually manifests itself as a collection of visually salient features of the target label (in model fooling), casting adversarial perturbations as primitive features of the target label. Our attack provides the first demonstration of systematically computing perturbations for adversarially non-robust classifiers that comprise salient visual features of objects. We leverage the model explaining character of our algorithm to perform image generation, inpainting and interactive image manipulation by attacking adversarially robust classifiers. The visually appealing results across these applications demonstrate the utility of our attack (and perturbations in general) beyond model fooling. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Jalwana_Attack_to_Explain_Deep_Representation_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Jalwana_Attack_to_Explain_Deep_Representation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Jalwana_Attack_to_Explain_Deep_Representation_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
FALCON: A Fourier Transform Based Approach for Fast and Secure Convolutional Neural Network Predictions | Shaohua Li, Kaiping Xue, Bin Zhu, Chenkai Ding, Xindi Gao, David Wei, Tao Wan | Deep learning as a service has been widely deployed to utilize deep neural network models to provide prediction services. However, this raises privacy concerns since clients need to send sensitive information to servers. In this paper, we focus on the scenario where clients want to classify private images with a convolutional neural network model hosted in the server, while both parties keep their data private. We present FALCON, a fast and secure approach for CNN predictions based on fast Fourier Transform. Our solution enables linear layers of a CNN model to be evaluated simply and efficiently with fully homomorphic encryption. We also introduce the first efficient and privacy-preserving protocol for softmax function, which is an indispensable component in CNNs and has not yet been evaluated in previous work due to its high complexity. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Li_FALCON_A_Fourier_Transform_Based_Approach_for_Fast_and_Secure_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=Ay1i7FHuGEE | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_FALCON_A_Fourier_Transform_Based_Approach_for_Fast_and_Secure_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Li_FALCON_A_Fourier_Transform_Based_Approach_for_Fast_and_Secure_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
The Knowledge Within: Methods for Data-Free Model Compression | Matan Haroush, Itay Hubara, Elad Hoffer, Daniel Soudry | Background: Recently, an extensive amount of research has been focused on compressing and accelerating Deep Neural Networks (DNN). So far, high compression rate algorithms require part of the training dataset for a low precision calibration, or a fine-tuning process. However, this requirement is unacceptable when the data is unavailable or contains sensitive information, as in medical and biometric use-cases. Contributions: We present three methods for generating synthetic samples from trained models. Then, we demonstrate how these samples can be used to calibrate and fine-tune quantized models without using any real data in the process. Our best performing method has a negligible accuracy degradation compared to the original training set. This method, which leverages intrinsic batch normalization layers' statistics of the trained model, can be used to evaluate data similarity. Our approach opens a path towards genuine data-free model compression, alleviating the need for training data during model deployment. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Haroush_The_Knowledge_Within_Methods_for_Data-Free_Model_Compression_CVPR_2020_paper.pdf | http://arxiv.org/abs/1912.01274 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Haroush_The_Knowledge_Within_Methods_for_Data-Free_Model_Compression_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Haroush_The_Knowledge_Within_Methods_for_Data-Free_Model_Compression_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Haroush_The_Knowledge_Within_CVPR_2020_supplemental.pdf | null | null |
PropagationNet: Propagate Points to Curve to Learn Structure Information | Xiehe Huang, Weihong Deng, Haifeng Shen, Xiubao Zhang, Jieping Ye | Deep learning technique has dramatically boosted the performance of face alignment algorithms. However, due to large variability and lack of samples, the alignment problem in unconstrained situations, e.g. large head poses, exaggerated expression, and uneven illumination, is still largely unsolved. In this paper, we explore the instincts and reasons behind our two proposals, i.e. Propagation Module and Focal Wing Loss, to tackle the problem. Concretely, we present a novel structure-infused face alignment algorithm based on heatmap regression via propagating landmark heatmaps to boundary heatmaps, which provide structure information for further attention map generation. Moreover, we propose a Focal Wing Loss for mining and emphasizing the difficult samples under in-the-wild condition. In addition, we adopt methods like CoordConv and Anti-aliased CNN from other fields that address the shift variance problem of CNN for face alignment. When implementing extensive experiments on different benchmarks, i.e. WFLW, 300W, and COFW, our method outperforms the state-of-the-arts by a significant margin. Our proposed approach achieves 4.05% mean error on WFLW, 2.93% mean error on 300W full-set, and 3.71% mean error on COFW. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Huang_PropagationNet_Propagate_Points_to_Curve_to_Learn_Structure_Information_CVPR_2020_paper.pdf | http://arxiv.org/abs/2006.14308 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Huang_PropagationNet_Propagate_Points_to_Curve_to_Learn_Structure_Information_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Huang_PropagationNet_Propagate_Points_to_Curve_to_Learn_Structure_Information_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
S3VAE: Self-Supervised Sequential VAE for Representation Disentanglement and Data Generation | Yizhe Zhu, Martin Renqiang Min, Asim Kadav, Hans Peter Graf | We propose a sequential variational autoencoder to learn disentangled representations of sequential data (e.g., videos and audios) under self-supervision. Specifically, we exploit the benefits of some readily accessible supervision signals from input data itself or some off-the-shelf functional models and accordingly design auxiliary tasks for our model to utilize these signals. With the supervision of the signals, our model can easily disentangle the representation of an input sequence into static factors and dynamic factors (i.e., time-invariant and time-varying parts). Comprehensive experiments across videos and audios verify the effectiveness of our model on representation disentanglement and generation of sequential data, and demonstrate that, our model with self-supervision performs comparable to, if not better than, the fully-supervised model with ground truth labels, and outperforms state-of-the-art unsupervised models by a large margin. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zhu_S3VAE_Self-Supervised_Sequential_VAE_for_Representation_Disentanglement_and_Data_Generation_CVPR_2020_paper.pdf | http://arxiv.org/abs/2005.11437 | https://www.youtube.com/watch?v=qyEwf92IPFw | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhu_S3VAE_Self-Supervised_Sequential_VAE_for_Representation_Disentanglement_and_Data_Generation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhu_S3VAE_Self-Supervised_Sequential_VAE_for_Representation_Disentanglement_and_Data_Generation_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Same Features, Different Day: Weakly Supervised Feature Learning for Seasonal Invariance | Jaime Spencer, Richard Bowden, Simon Hadfield | "Like night and day" is a commonly used expression to imply that two things are completely different. Unfortunately, this tends to be the case for current visual feature representations of the same scene across varying seasons or times of day. The aim of this paper is to provide a dense feature representation that can be used to perform localization, sparse matching or image retrieval, regardless of the current seasonal or temporal appearance. Recently, there have been several proposed methodologies for deep learning dense feature representations. These methods make use of ground truth pixel-wise correspondences between pairs of images and focus on the spatial properties of the features. As such, they don't address temporal or seasonal variation. Furthermore, obtaining the required pixel-wise correspondence data to train in cross-seasonal environments is highly complex in most scenarios. We propose Deja-Vu, a weakly supervised approach to learning season invariant features that does not require pixel-wise ground truth data. The proposed system only requires coarse labels indicating if two images correspond to the same location or not. From these labels, the network is trained to produce "similar" dense feature maps for corresponding locations despite environmental changes. Code will be made available at: https://github.com/jspenmar/DejaVu_Features | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Spencer_Same_Features_Different_Day_Weakly_Supervised_Feature_Learning_for_Seasonal_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.13431 | https://www.youtube.com/watch?v=lD8jh--0T-Y | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Spencer_Same_Features_Different_Day_Weakly_Supervised_Feature_Learning_for_Seasonal_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Spencer_Same_Features_Different_Day_Weakly_Supervised_Feature_Learning_for_Seasonal_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Spencer_Same_Features_Different_CVPR_2020_supplemental.pdf | null | null |
Implicit Functions in Feature Space for 3D Shape Reconstruction and Completion | Julian Chibane, Thiemo Alldieck, Gerard Pons-Moll | While many works focus on 3D reconstruction from images, in this paper, we focus on 3D shape reconstruction and completion from a variety of 3D inputs, which are deficient in some respect: low and high resolution voxels, sparse and dense point clouds, complete or incomplete. Processing of such 3D inputs is an increasingly important problem as they are the output of 3D scanners, which are becoming more accessible, and are the intermediate output of 3D computer vision algorithms. Recently, learned implicit functions have shown great promise as they produce continuous reconstructions. However, we identified two limitations in reconstruction from 3D inputs: 1) details present in the input data are not retained, and 2) poor reconstruction of articulated humans. To solve this, we propose Implicit Feature Networks (IF-Nets), which deliver continuous outputs, can handle multiple topologies, and complete shapes for missing or sparse input data retaining the nice properties of recent learned implicit functions, but critically they can also retain detail when it is present in the input data, and can reconstruct articulated humans. Our work differs from prior work in two crucial aspects. First, instead of using a single vector to encode a 3D shape, we extract a learnable 3-dimensional multi-scale tensor of deep features, which is aligned with the original Euclidean space embedding the shape. Second, instead of classifying x-y-z point coordinates directly, we classify deep features extracted from the tensor at a continuous query point. We show that this forces our model to make decisions based on global and local shape structure, as opposed to point coordinates, which are arbitrary under Euclidean transformations. Experiments demonstrate that IF-Nets outperform prior work in 3D object reconstruction in ShapeNet, and obtain significantly more accurate 3D human reconstructions. Code and project website is available at https://virtualhumans.mpi-inf.mpg.de/ifnets/. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Chibane_Implicit_Functions_in_Feature_Space_for_3D_Shape_Reconstruction_and_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=GvFkgqRpVEY | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Chibane_Implicit_Functions_in_Feature_Space_for_3D_Shape_Reconstruction_and_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Chibane_Implicit_Functions_in_Feature_Space_for_3D_Shape_Reconstruction_and_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Chibane_Implicit_Functions_in_CVPR_2020_supplemental.pdf | null | null |
AdaCoSeg: Adaptive Shape Co-Segmentation With Group Consistency Loss | Chenyang Zhu, Kai Xu, Siddhartha Chaudhuri, Li Yi, Leonidas J. Guibas, Hao Zhang | We introduce AdaCoSeg, a deep neural network architecture for adaptive co-segmentation of a set of 3D shapes represented as point clouds. Differently from the familiar single-instance segmentation problem, co-segmentation is intrinsically contextual: how a shape is segmented can vary depending on the set it is in. Hence, our network features an adaptive learning module to produce a consistent shape segmentation which adapts to a set. Specifically, given an input set of unsegmented shapes, we first employ an offline pre-trained part prior network to propose per-shape parts. Then the co-segmentation network iteratively and jointly optimizes the part labelings across the set subjected to a novel group consistency loss defined by matrix ranks. While the part prior network can be trained with noisy and inconsistently segmented shapes, the final output of AdaSeg is a consistent part labeling for the input set, with each shape segmented into up to (a user-specified) K parts. Overall, our method is weakly supervised, producing segmentations tailored to the test set, without consistent ground-truth segmentations. We show qualitative and quantitative results from AdaSeg and evaluate it via ablation studies and comparisons to state-of-the-art co-segmentation methods. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zhu_AdaCoSeg_Adaptive_Shape_Co-Segmentation_With_Group_Consistency_Loss_CVPR_2020_paper.pdf | http://arxiv.org/abs/1903.10297 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhu_AdaCoSeg_Adaptive_Shape_Co-Segmentation_With_Group_Consistency_Loss_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhu_AdaCoSeg_Adaptive_Shape_Co-Segmentation_With_Group_Consistency_Loss_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Learning Combinatorial Solver for Graph Matching | Tao Wang, He Liu, Yidong Li, Yi Jin, Xiaohui Hou, Haibin Ling | Learning-based approaches to graph matching have been developed and explored for more than a decade, have grown rapidly in scope and popularity in recent years. However, previous learning-based algorithms, with or without deep learning strategy, mainly focus on the learning of node and/or edge affinities generation, and pay less attention on the learning of the combinatorial solver. In this paper we propose a fully trainable framework for graph matching, in which learning of affinities and solving for combinatorial optimization are not explicitly separated as in many previous arts. We firstly convert the problem of building node correspondences between two input graphs to the problem of selecting reliable nodes from a constructed assignment graph. Subsequently, the graph network block module is adopted to perform computation on the graph to form structured representations for each node. It finally predicts a label for each node that is used for node classification, and the training is performed under the supervision of both permutation differences and the one-to-one matching constraints. The proposed method is evaluated on four public benchmarks in comparison with several state-of-the-art algorithms, and the experimental results illustrate its excellent performance. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wang_Learning_Combinatorial_Solver_for_Graph_Matching_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Learning_Combinatorial_Solver_for_Graph_Matching_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Learning_Combinatorial_Solver_for_Graph_Matching_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Nonparametric Object and Parts Modeling With Lie Group Dynamics | David S. Hayden, Jason Pacheco, John W. Fisher III | Articulated motion analysis often utilizes strong prior knowledge such as a known or trained parts model for humans. Yet, the world contains a variety of articulating objects--mammals, insects, mechanized structures--where the number and configuration of parts for a particular object is unknown in advance. Here, we relax such strong assumptions via an unsupervised, Bayesian nonparametric parts model that infers an unknown number of parts with motions coupled by a body dynamic and parameterized by SE(D), the Lie group of rigid transformations. We derive an inference procedure that utilizes short observation sequences (image, depth, point cloud or mesh) of an object in motion without need for markers or learned body models. Efficient Gibbs decompositions for inference over distributions on SE(D) demonstrate robust part decompositions of moving objects under both 3D and 2D observation models. The inferred representation permits novel analysis, such as object segmentation by relative part motion, and transfers to new observations of the same object type. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Hayden_Nonparametric_Object_and_Parts_Modeling_With_Lie_Group_Dynamics_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Hayden_Nonparametric_Object_and_Parts_Modeling_With_Lie_Group_Dynamics_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Hayden_Nonparametric_Object_and_Parts_Modeling_With_Lie_Group_Dynamics_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Hayden_Nonparametric_Object_and_CVPR_2020_supplemental.zip | null | null |
A Neural Rendering Framework for Free-Viewpoint Relighting | Zhang Chen, Anpei Chen, Guli Zhang, Chengyuan Wang, Yu Ji, Kiriakos N. Kutulakos, Jingyi Yu | We present a novel Relightable Neural Renderer (RNR) for simultaneous view synthesis and relighting using multi-view image inputs. Existing neural rendering (NR) does not explicitly model the physical rendering process and hence has limited capabilities on relighting. RNR instead models image formation in terms of environment lighting, object intrinsic attributes, and light transport function (LTF), each corresponding to a learnable component. In particular, the incorporation of a physically based rendering process not only enables relighting but also improves the quality of view synthesis. Comprehensive experiments on synthetic and real data show that RNR provides a practical and effective solution for conducting free-viewpoint relighting. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Chen_A_Neural_Rendering_Framework_for_Free-Viewpoint_Relighting_CVPR_2020_paper.pdf | http://arxiv.org/abs/1911.11530 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Chen_A_Neural_Rendering_Framework_for_Free-Viewpoint_Relighting_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Chen_A_Neural_Rendering_Framework_for_Free-Viewpoint_Relighting_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Attribution in Scale and Space | Shawn Xu, Subhashini Venugopalan, Mukund Sundararajan | We study the attribution problem for deep networks applied to perception tasks. For vision tasks, attribution techniques attribute the prediction of a network to the pixels of the input image. We propose a new technique called Blur Integrated Gradients (Blur IG). This technique has several advantages over other methods. First, it can tell at what scale a network recognizes an object. It produces scores in the scale/frequency dimension, that we find captures interesting phenomena. Second, it satisfies the scale-space axioms, which imply that it employs perturbations that are free of artifact. We therefore produce explanations that are cleaner and consistent with the operation of deep networks. Third, it eliminates the need for baseline parameter for Integrated Gradients for perception tasks. This is desirable because the choice of baseline has a significant effect on the explanations. We compare the proposed technique against previous techniques and demonstrate application on three tasks: ImageNet object recognition, Diabetic Retinopathy prediction, and AudioSet audio event identification. Code and examples are at https://github.com/PAIR-code/saliency. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Xu_Attribution_in_Scale_and_Space_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.03383 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Xu_Attribution_in_Scale_and_Space_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Xu_Attribution_in_Scale_and_Space_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Probabilistic Regression for Visual Tracking | Martin Danelljan, Luc Van Gool, Radu Timofte | Visual tracking is fundamentally the problem of regressing the state of the target in each video frame. While significant progress has been achieved, trackers are still prone to failures and inaccuracies. It is therefore crucial to represent the uncertainty in the target estimation. Although current prominent paradigms rely on estimating a state-dependent confidence score, this value lacks a clear probabilistic interpretation, complicating its use. In this work, we therefore propose a probabilistic regression formulation and apply it to tracking. Our network predicts the conditional probability density of the target state given an input image. Crucially, our formulation is capable of modeling label noise stemming from inaccurate annotations and ambiguities in the task. The regression network is trained by minimizing the Kullback-Leibler divergence. When applied for tracking, our formulation not only allows a probabilistic representation of the output, but also substantially improves the performance. Our tracker sets a new state-of-the-art on six datasets, achieving 59.8% AUC on LaSOT and 75.8% Success on TrackingNet. The code and models are available at https://github.com/visionml/pytracking. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Danelljan_Probabilistic_Regression_for_Visual_Tracking_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.12565 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Danelljan_Probabilistic_Regression_for_Visual_Tracking_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Danelljan_Probabilistic_Regression_for_Visual_Tracking_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Danelljan_Probabilistic_Regression_for_CVPR_2020_supplemental.pdf | null | null |
3DRegNet: A Deep Neural Network for 3D Point Registration | G. Dias Pais, Srikumar Ramalingam, Venu Madhav Govindu, Jacinto C. Nascimento, Rama Chellappa, Pedro Miraldo | We present 3DRegNet, a novel deep learning architecture for the registration of 3D scans. Given a set of 3D point correspondences, we build a deep neural network to address the following two challenges: (i) classification of the point correspondences into inliers/outliers, and (ii) regression of the motion parameters that align the scans into a common reference frame. With regard to regression, we present two alternative approaches: (i) a Deep Neural Network (DNN) registration and (ii) a Procrustes approach using SVD to estimate the transformation. Our correspondence-based approach achieves a higher speedup compared to competing baselines. We further propose the use of a refinement network, which consists of a smaller 3DRegNet as a refinement to improve the accuracy of the registration. Extensive experiments on two challenging datasets demonstrate that we outperform other methods and achieve state-of-the-art results. The code is available. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Pais_3DRegNet_A_Deep_Neural_Network_for_3D_Point_Registration_CVPR_2020_paper.pdf | http://arxiv.org/abs/1904.01701 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Pais_3DRegNet_A_Deep_Neural_Network_for_3D_Point_Registration_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Pais_3DRegNet_A_Deep_Neural_Network_for_3D_Point_Registration_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Pais_3DRegNet_A_Deep_CVPR_2020_supplemental.pdf | null | null |
SEAN: Image Synthesis With Semantic Region-Adaptive Normalization | Peihao Zhu, Rameen Abdal, Yipeng Qin, Peter Wonka | We propose semantic region-adaptive normalization (SEAN), a simple but effective building block for Generative Adversarial Networks conditioned on segmentation masks that describe the semantic regions in the desired output image. Using SEAN normalization, we can build a network architecture that can control the style of each semantic region individually, e.g., we can specify one style reference image per region. SEAN is better suited to encode, transfer, and synthesize style than the best previous method in terms of reconstruction quality, variability, and visual quality. We evaluate SEAN on multiple datasets and report better quantitative metrics (e.g. FID, PSNR) than the current state of the art. SEAN also pushes the frontier of interactive image editing. We can interactively edit images by changing segmentation masks or the style for any given region. We can also interpolate styles from two reference images per region. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zhu_SEAN_Image_Synthesis_With_Semantic_Region-Adaptive_Normalization_CVPR_2020_paper.pdf | http://arxiv.org/abs/1911.12861 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhu_SEAN_Image_Synthesis_With_Semantic_Region-Adaptive_Normalization_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhu_SEAN_Image_Synthesis_With_Semantic_Region-Adaptive_Normalization_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Zhu_SEAN_Image_Synthesis_CVPR_2020_supplemental.pdf | null | null |
Robust Reference-Based Super-Resolution With Similarity-Aware Deformable Convolution | Gyumin Shim, Jinsun Park, In So Kweon | In this paper, we propose a novel and efficient reference feature extraction module referred to as the Similarity Search and Extraction Network (SSEN) for reference-based super-resolution (RefSR) tasks. The proposed module extracts aligned relevant features from a reference image to increase the performance over single image super-resolution (SISR) methods. In contrast to conventional algorithms which utilize brute-force searches or optical flow estimations, the proposed algorithm is end-to-end trainable without any additional supervision or heavy computation, predicting the best match with a single network forward operation. Moreover, the proposed module is aware of not only the best matching position but also the relevancy of the best match. This makes our algorithm substantially robust when irrelevant reference images are given, overcoming the major cause of the performance degradation when using existing RefSR methods. Furthermore, our module can be utilized for self-similarity SR if no reference image is available. Experimental results demonstrate the superior performance of the proposed algorithm compared to previous works both quantitatively and qualitatively. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Shim_Robust_Reference-Based_Super-Resolution_With_Similarity-Aware_Deformable_Convolution_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=hjNJ2JVPp3s | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Shim_Robust_Reference-Based_Super-Resolution_With_Similarity-Aware_Deformable_Convolution_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Shim_Robust_Reference-Based_Super-Resolution_With_Similarity-Aware_Deformable_Convolution_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Search to Distill: Pearls Are Everywhere but Not the Eyes | Yu Liu, Xuhui Jia, Mingxing Tan, Raviteja Vemulapalli, Yukun Zhu, Bradley Green, Xiaogang Wang | Standard Knowledge Distillation (KD) approaches distill the knowledge of a cumbersome teacher model into the parameters of a student model with a pre-defined architecture. However, the knowledge of a neural network, which is represented by the network's output distribution conditioned on its input, depends not only on its parameters but also on its architecture. Hence, a more generalized approach for KD is to distill the teacher's knowledge into both the parameters and architecture of the student. To achieve this, we present a new Architecture-aware Knowledge Distillation (AKD) approach that finds student models (pearls for the teacher) that are best for distilling the given teacher model. In particular, we leverage Neural Architecture Search (NAS), equipped with our KD-guided reward, to search for the best student architectures for a given teacher. Experimental results show our proposed AKD consistently outperforms the conventional NAS plus KD approach, and achieves state-of-the-art results on the ImageNet classification task under various latency settings. Furthermore, the best AKD student architecture for the ImageNet classification task also transfers well to other tasks such as million level face recognition and ensemble learning. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Liu_Search_to_Distill_Pearls_Are_Everywhere_but_Not_the_Eyes_CVPR_2020_paper.pdf | http://arxiv.org/abs/1911.09074 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Liu_Search_to_Distill_Pearls_Are_Everywhere_but_Not_the_Eyes_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Liu_Search_to_Distill_Pearls_Are_Everywhere_but_Not_the_Eyes_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Boosting Semantic Human Matting With Coarse Annotations | Jinlin Liu, Yuan Yao, Wendi Hou, Miaomiao Cui, Xuansong Xie, Changshui Zhang, Xian-Sheng Hua | Semantic human matting aims to estimate the per-pixel opacity of the foreground human regions. It is quite challenging that usually requires user interactive trimaps and plenty of high quality annotated data. Annotating such kind of data is labor intensive and requires great skills beyond normal users, especially considering the very detailed hair part of humans. In contrast, coarse annotated human dataset is much easier to acquire and collect from the public dataset. In this paper, we propose to leverage coarse annotated data coupled with fine annotated data to boost end-to-end semantic human matting without trimaps as extra input. Specifically, We train a mask prediction network to estimate the coarse semantic mask using the hybrid data, and then propose a quality unification network to unify the quality of the previous coarse mask outputs. A matting refinement network takes the unified mask and the input image to predict the final alpha matte. The collected coarse annotated dataset enriches our dataset significantly, allows generating high quality alpha matte for real images. Experimental results show that the proposed method performs comparably against state-of-the-art methods. Moreover, the proposed method can be used for refining coarse annotated public dataset, as well as semantic segmentation methods, which reduces the cost of annotating high quality human data to a great extent. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Liu_Boosting_Semantic_Human_Matting_With_Coarse_Annotations_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.04955 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Liu_Boosting_Semantic_Human_Matting_With_Coarse_Annotations_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Liu_Boosting_Semantic_Human_Matting_With_Coarse_Annotations_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Liu_Boosting_Semantic_Human_CVPR_2020_supplemental.pdf | null | null |
Few-Shot Learning via Embedding Adaptation With Set-to-Set Functions | Han-Jia Ye, Hexiang Hu, De-Chuan Zhan, Fei Sha | Learning with limited data is a key challenge for visual recognition. Many few-shot learning methods address this challenge by learning an instance embedding function from seen classes and apply the function to instances from unseen classes with limited labels. This style of transfer learning is task-agnostic: the embedding function is not learned optimally discriminative with respect to the unseen classes, where discerning among them leads to the target task. In this paper, we propose a novel approach to adapt the instance embeddings to the target classification task with a set-to-set function, yielding embeddings that are task-specific and are discriminative. We empirically investigated various instantiations of such set-to-set functions and observed the Transformer is most effective --- as it naturally satisfies key properties of our desired model. We denote this model as FEAT (few-shot embedding adaptation w/ Transformer) and validate it on both the standard few-shot classification benchmark and four extended few-shot learning settings with essential use cases, i.e., cross-domain, transductive, generalized few-shot learning, and low-shot learning. It archived consistent improvements over baseline models as well as previous methods, and established the new state-of-the-art results on two benchmarks. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Ye_Few-Shot_Learning_via_Embedding_Adaptation_With_Set-to-Set_Functions_CVPR_2020_paper.pdf | http://arxiv.org/abs/1812.03664 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Ye_Few-Shot_Learning_via_Embedding_Adaptation_With_Set-to-Set_Functions_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Ye_Few-Shot_Learning_via_Embedding_Adaptation_With_Set-to-Set_Functions_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Ye_Few-Shot_Learning_via_CVPR_2020_supplemental.pdf | null | null |
FM2u-Net: Face Morphological Multi-Branch Network for Makeup-Invariant Face Verification | Wenxuan Wang, Yanwei Fu, Xuelin Qian, Yu-Gang Jiang, Qi Tian, Xiangyang Xue | It is challenging in learning a makeup-invariant face verification model, due to (1) insufficient makeup/non-makeup face training pairs, (2) the lack of diverse makeup faces, and (3) the significant appearance changes caused by cosmetics. To address these challenges, we propose a unified Face Morphological Multi-branch Network (FMMu-Net) for makeup-invariant face verification, which can simultaneously synthesize many diverse makeup faces through face morphology network (FM-Net) and effectively learn cosmetics-robust face representations using attention-based multi-branch learning network (AttM-Net). For challenges (1) and (2), FM-Net (two stacked auto-encoders) can synthesize realistic makeup face images by transferring specific regions of cosmetics via cycle consistent loss. For challenge (3), AttM-Net, consisting of one global and three local (task-driven on two eyes and mouth) branches, can effectively capture the complementary holistic and detailed information. Unlike DeepID2 which uses simple concatenation fusion, we introduce a heuristic method AttM-FM, attached to AttM-Net, to adaptively weight the features of different branches guided by the holistic information. We conduct extensive experiments on makeup face verification benchmarks (M-501, M-203, and FAM) and general face recognition datasets (LFW and IJB-A). Our framework FMMu-Net achieves state-of-the-art performances. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wang_FM2u-Net_Face_Morphological_Multi-Branch_Network_for_Makeup-Invariant_Face_Verification_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=hApLT8HLnjQ | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_FM2u-Net_Face_Morphological_Multi-Branch_Network_for_Makeup-Invariant_Face_Verification_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_FM2u-Net_Face_Morphological_Multi-Branch_Network_for_Makeup-Invariant_Face_Verification_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Deep Semantic Clustering by Partition Confidence Maximisation | Jiabo Huang, Shaogang Gong, Xiatian Zhu | By simultaneously learning visual features and data grouping, deep clustering has shown impressive ability to deal with unsupervised learning for structure analysis of high-dimensional visual data. Existing deep clustering methods typically rely on local learning constraints based on inter-sample relations and/or self-estimated pseudo labels. This is susceptible to the inevitable errors distributed in the neighbourhoods and suffers from error-propagation during training. In this work, we propose to solve this problem by learning the most confident clustering solution from all the possible separations, based on the observation that assigning samples from the same semantic categories into different clusters will reduce both the intra-cluster compactness and inter-cluster diversity, i.e. lower partition confidence. Specifically, we introduce a novel deep clustering method named PartItion Confidence mAximisation (PICA). It is established on the idea of learning the most semantically plausible data separation, in which all clusters can be mapped to the ground-truth classes one-to-one, by maximising the "global" partition confidence of clustering solution. This is realised by introducing a differentiable partition uncertainty index and its stochastic approximation as well as a principled objective loss function that minimises such index, all of which together enables a direct adoption of the conventional deep networks and mini-batch based model training. Extensive experiments on six widely-adopted clustering benchmarks demonstrate our model's performance superiority over a wide range of the state-of-the-art approaches. The code is available online. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Huang_Deep_Semantic_Clustering_by_Partition_Confidence_Maximisation_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Huang_Deep_Semantic_Clustering_by_Partition_Confidence_Maximisation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Huang_Deep_Semantic_Clustering_by_Partition_Confidence_Maximisation_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
A Transductive Approach for Video Object Segmentation | Yizhuo Zhang, Zhirong Wu, Houwen Peng, Stephen Lin | Semi-supervised video object segmentation aims to separate a target object from a video sequence, given the mask in the first frame. Most of current prevailing methods utilize information from additional modules trained in other domains like optical flow and instance segmentation, and as a result they do not compete with other methods on common ground. To address this issue, we propose a simple yet strong transductive method, in which additional modules, datasets, and dedicated architectural designs are not needed. Our method takes a label propagation approach where pixel labels are passed forward based on feature similarity in an embedding space. Different from other propagation methods, ours diffuses temporal information in a holistic manner which take accounts of long-term object appearance. In addition, our method requires few additional computational overhead, and runs at a fast 37 fps speed. Our single model with a vanilla ResNet50 backbone achieves an overall score of 72.3% on the DAVIS 2017 validation set and 63.1% on the test set. This simple yet high performing and efficient method can serve as a solid baseline that facilitates future research. Code and models are available at https://github.com/ microsoft/transductive-vos.pytorch. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zhang_A_Transductive_Approach_for_Video_Object_Segmentation_CVPR_2020_paper.pdf | http://arxiv.org/abs/2004.07193 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_A_Transductive_Approach_for_Video_Object_Segmentation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_A_Transductive_Approach_for_Video_Object_Segmentation_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Uncertainty-Aware Mesh Decoder for High Fidelity 3D Face Reconstruction | Gun-Hee Lee, Seong-Whan Lee | 3D Morphable Model (3DMM) is a statistical model of facial shape and texture using a set of linear basis functions. Most of the recent 3D face reconstruction methods aim to embed the 3D morphable basis functions into Deep Convolutional Neural Network (DCNN). However, balancing the requirements of strong regularization for global shape and weak regularization for high level details is still ill-posed. To address this problem, we properly control generality and specificity in terms of regularization by harnessing the power of uncertainty. Additionally, we focus on the concept of nonlinearity and find out that Graph Convolutional Neural Network (Graph CNN) and Generative Adversarial Network (GAN) are effective in reconstructing high quality 3D shapes and textures respectively. In this paper, we propose to employ (i) an uncertainty-aware encoder that presents face features as distributions and (ii) a fully nonlinear decoder model combining Graph CNN with GAN. We demonstrate how our method builds excellent high quality results and outperforms previous state-of-the-art methods on 3D face reconstruction tasks for both constrained and in-the-wild images. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Lee_Uncertainty-Aware_Mesh_Decoder_for_High_Fidelity_3D_Face_Reconstruction_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Lee_Uncertainty-Aware_Mesh_Decoder_for_High_Fidelity_3D_Face_Reconstruction_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Lee_Uncertainty-Aware_Mesh_Decoder_for_High_Fidelity_3D_Face_Reconstruction_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
Object-Occluded Human Shape and Pose Estimation From a Single Color Image | Tianshu Zhang, Buzhen Huang, Yangang Wang | Occlusions between human and objects, especially for the activities of human-object interactions, are very common in practical applications. However, most of the existing approaches for 3D human shape and pose estimation require human bodies are well captured without occlusions or with minor self-occlusions. In this paper, we focus on the problem of directly estimating the object-occluded human shape and pose from single color images. Our key idea is to utilize a partial UV map to represent an object-occluded human body, and the full 3D human shape estimation is ultimately converted as an image inpainting problem. We propose a novel two-branch network architecture to train an end-to-end regressor via the latent feature supervision, which also includes a novel saliency map sub-net to extract the human information from object-occluded color images. To supervise the network training, we further build a novel dataset named as 3DOH50K. Several experiments are conducted to reveal the effectiveness of the proposed method. Experimental results demonstrate that the proposed method achieves the state-of-the-art comparing with previous methods. The dataset, codes are publicly available at https://www.yangangwang.com. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Zhang_Object-Occluded_Human_Shape_and_Pose_Estimation_From_a_Single_Color_CVPR_2020_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_Object-Occluded_Human_Shape_and_Pose_Estimation_From_a_Single_Color_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_Object-Occluded_Human_Shape_and_Pose_Estimation_From_a_Single_Color_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Zhang_Object-Occluded_Human_Shape_CVPR_2020_supplemental.zip | null | null |
MAST: A Memory-Augmented Self-Supervised Tracker | Zihang Lai, Erika Lu, Weidi Xie | Recent interest in self-supervised dense tracking has yielded rapid progress, but performance still remains far from supervised methods. We propose a dense tracking model trained on videos without any annotations that surpasses previous self-supervised methods on existing benchmarks by a significant margin (+15%), and achieves performance comparable to supervised methods. In this paper, we first reassess the traditional choices used for self-supervised training and reconstruction loss by conducting thorough experiments that finally elucidate the optimal choices. Second, we further improve on existing methods by augmenting our architecture with a crucial memory component. Third, we benchmark on large-scale semi-supervised video object segmentation (aka. dense tracking), and propose a new metric: generalizability. Our first two contributions yield a self-supervised network that for the first time is competitive with supervised methods on standard evaluation metrics of dense tracking. When measuring generalizability, we show self-supervised approaches are actually superior to the majority of supervised methods. We believe this new generalizability metric can better capture the real-world use-cases for dense tracking, and will spur new interest in this research direction. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Lai_MAST_A_Memory-Augmented_Self-Supervised_Tracker_CVPR_2020_paper.pdf | http://arxiv.org/abs/2002.07793 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Lai_MAST_A_Memory-Augmented_Self-Supervised_Tracker_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Lai_MAST_A_Memory-Augmented_Self-Supervised_Tracker_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Lai_MAST_A_Memory-Augmented_CVPR_2020_supplemental.pdf | null | null |
Wish You Were Here: Context-Aware Human Generation | Oran Gafni, Lior Wolf | We present a novel method for inserting objects, specifically humans, into existing images, such that they blend in a photorealistic manner, while respecting the semantic context of the scene. Our method involves three subnetworks: the first generates the semantic map of the new person, given the pose of the other persons in the scene and an optional bounding box specification. The second network renders the pixels of the novel person and its blending mask, based on specifications in the form of multiple appearance components. A third network refines the generated face in order to match those of the target person. Our experiments present convincing high-resolution outputs in this novel and challenging application domain. In addition, the three networks are evaluated individually, demonstrating for example, state of the art results in pose transfer benchmarks. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Gafni_Wish_You_Were_Here_Context-Aware_Human_Generation_CVPR_2020_paper.pdf | http://arxiv.org/abs/2005.10663 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Gafni_Wish_You_Were_Here_Context-Aware_Human_Generation_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Gafni_Wish_You_Were_Here_Context-Aware_Human_Generation_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Gafni_Wish_You_Were_CVPR_2020_supplemental.pdf | null | null |
Attention-Driven Cropping for Very High Resolution Facial Landmark Detection | Prashanth Chandran, Derek Bradley, Markus Gross, Thabo Beeler | Facial landmark detection is a fundamental task for many consumer and high-end applications and is almost entirely solved by machine learning methods today. Existing datasets used to train such algorithms are primarily made up of only low resolution images, and current algorithms are limited to inputs of comparable quality and resolution as the training dataset. On the other hand, high resolution imagery is becoming increasingly more common as consumer cameras improve in quality every year. Therefore, there is need for algorithms that can leverage the rich information available in high resolution imagery. Naively attempting to reuse existing network architectures on high resolution imagery is prohibitive due to memory bottlenecks on GPUs. The only current solution is to downsample the images, sacrificing resolution and quality. Building on top of recent progress in attention-based networks, we present a novel, fully convolutional regional architecture that is specially designed for predicting landmarks on very high resolution facial images without downsampling. We demonstrate the flexibility of our architecture by training the proposed model with images of resolutions ranging from 256 x 256 to 4K. In addition to being the first method for facial landmark detection on high resolution images, our approach achieves superior performance over traditional (holistic) state-of-the-art architectures across ALL resolutions, leading to a general-purpose, extremely flexible, high quality landmark detector. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Chandran_Attention-Driven_Cropping_for_Very_High_Resolution_Facial_Landmark_Detection_CVPR_2020_paper.pdf | null | https://www.youtube.com/watch?v=AwI92fqpOEg | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Chandran_Attention-Driven_Cropping_for_Very_High_Resolution_Facial_Landmark_Detection_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Chandran_Attention-Driven_Cropping_for_Very_High_Resolution_Facial_Landmark_Detection_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Chandran_Attention-Driven_Cropping_for_CVPR_2020_supplemental.zip | null | null |
Contextual Residual Aggregation for Ultra High-Resolution Image Inpainting | Zili Yi, Qiang Tang, Shekoofeh Azizi, Daesik Jang, Zhan Xu | Recently data-driven image inpainting methods have made inspiring progress, impacting fundamental image editing tasks such as object removal and damaged image repairing. These methods are more effective than classic approaches, however, due to memory limitations they can only handle low-resolution inputs, typically smaller than 1K. Meanwhile, the resolution of photos captured with mobile devices increases up to 8K. Naive up-sampling of the low-resolution inpainted result can merely yield a large yet blurry result. Whereas, adding a high-frequency residual image onto the large blurry image can generate a sharp result, rich in details and textures. Motivated by this, we propose a Contextual Residual Aggregation (CRA) mechanism that can produce high-frequency residuals for missing contents by weighted aggregating residuals from contextual patches, thus only requiring a low-resolution prediction from the network. Since convolutional layers of the neural network only need to operate on low-resolution inputs and outputs, the cost of memory and computing power is thus well suppressed. Moreover, the need for high-resolution training datasets is alleviated. In our experiments, we train the proposed model on small images with resolutions 512 x 512 and perform inference on high-resolution images, achieving compelling inpainting quality. Our model can inpaint images as large as 8K with considerable hole sizes, which is intractable with previous learning-based approaches. We further elaborate on the light-weight design of the network architecture, achieving real-time performance on 2K images on a GTX 1080 Ti GPU. Codes are available at: https://github. com/Ascend-Huawei/Ascend-Canada/tree/ master/Models/Research_HiFIll_Model | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Yi_Contextual_Residual_Aggregation_for_Ultra_High-Resolution_Image_Inpainting_CVPR_2020_paper.pdf | http://arxiv.org/abs/2005.09704 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Yi_Contextual_Residual_Aggregation_for_Ultra_High-Resolution_Image_Inpainting_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Yi_Contextual_Residual_Aggregation_for_Ultra_High-Resolution_Image_Inpainting_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Yi_Contextual_Residual_Aggregation_CVPR_2020_supplemental.pdf | null | null |
StructEdit: Learning Structural Shape Variations | Kaichun Mo, Paul Guerrero, Li Yi, Hao Su, Peter Wonka, Niloy J. Mitra, Leonidas J. Guibas | Learning to encode differences in the geometry and (topological) structure of the shapes of ordinary objects is key to generating semantically plausible variations of a given shape, transferring edits from one shape to another, and for many other applications in 3D content creation. The common approach of encoding shapes as points in a high-dimensional latent feature space suggests treating shape differences as vectors in that space. Instead, we treat shape differences as primary objects in their own right and propose to encode them in their own latent space. In a setting where the shapes themselves are encoded in terms of fine-grained part hierarchies, we demonstrate that a separate encoding of shape deltas or differences provides a principled way to deal with inhomogeneities in the shape space due to different combinatorial part structures, while also allowing for compactness in the representation, as well as edit abstraction and transfer. Our approach is based on a conditional variational autoencoder for encoding and decoding shape deltas, conditioned on a source shape. We demonstrate the effectiveness and robustness of our approach in multiple shape modification and generation tasks, and provide comparison and ablation studies on the PartNet dataset, one of the largest publicly available 3D datasets. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Mo_StructEdit_Learning_Structural_Shape_Variations_CVPR_2020_paper.pdf | http://arxiv.org/abs/1911.11098 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Mo_StructEdit_Learning_Structural_Shape_Variations_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Mo_StructEdit_Learning_Structural_Shape_Variations_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Mo_StructEdit_Learning_Structural_CVPR_2020_supplemental.pdf | null | null |
Hierarchical Human Parsing With Typed Part-Relation Reasoning | Wenguan Wang, Hailong Zhu, Jifeng Dai, Yanwei Pang, Jianbing Shen, Ling Shao | Human parsing is for pixel-wise human semantic understanding. As human bodies are underlying hierarchically structured, how to model human structures is the central theme in this task. Focusing on this, we seek to simultaneously exploit the representational capacity of deep graph networks and the hierarchical human structures. In particular, we provide following two contributions. First, three kinds of part relations, i.e., decomposition, composition, and dependency, are, for the first time, completely and precisely described by three distinct relation networks. This is in stark contrast to previous parsers, which only focus on a portion of the relations and adopt a type-agnostic relation modeling strategy. More expressive relation information can be captured by explicitly imposing the parameters in the relation networks to satisfy the specific characteristics of different relations. Second, previous parsers largely ignore the need for an approximation algorithm over the loopy human hierarchy, while we instead address an iterative reasoning process, by assimilating generic message-passing networks with their edge-typed, convolutional counterparts. With these efforts, our parser lays the foundation for more sophisticated and flexible human relation patterns of reasoning. Comprehensive experiments on five datasets demonstrate that our parser sets a new state-of-the-art on each. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Wang_Hierarchical_Human_Parsing_With_Typed_Part-Relation_Reasoning_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.04845 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Hierarchical_Human_Parsing_With_Typed_Part-Relation_Reasoning_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Hierarchical_Human_Parsing_With_Typed_Part-Relation_Reasoning_CVPR_2020_paper.html | CVPR 2020 | null | null | null |
High-Resolution Daytime Translation Without Domain Labels | Ivan Anokhin, Pavel Solovev, Denis Korzhenkov, Alexey Kharlamov, Taras Khakhulin, Aleksei Silvestrov, Sergey Nikolenko, Victor Lempitsky, Gleb Sterkin | Modeling daytime changes in high resolution photographs, e.g., re-rendering the same scene under different illuminations typical for day, night, or dawn, is a challenging image manipulation task. We present the high-resolution daytime translation (HiDT) model for this task. HiDT combines a generative image-to-image model and a new upsampling scheme that allows to apply image translation at high resolution. The model demonstrates competitive results in terms of both commonly used GAN metrics and human evaluation. Importantly, this good performance comes as a result of training on a dataset of still landscape images with no daytime labels available. | https://openaccess.thecvf.com../../content_CVPR_2020/papers/Anokhin_High-Resolution_Daytime_Translation_Without_Domain_Labels_CVPR_2020_paper.pdf | http://arxiv.org/abs/2003.08791 | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content_CVPR_2020/html/Anokhin_High-Resolution_Daytime_Translation_Without_Domain_Labels_CVPR_2020_paper.html | https://openaccess.thecvf.com/content_CVPR_2020/html/Anokhin_High-Resolution_Daytime_Translation_Without_Domain_Labels_CVPR_2020_paper.html | CVPR 2020 | https://openaccess.thecvf.com../../content_CVPR_2020/supplemental/Anokhin_High-Resolution_Daytime_Translation_CVPR_2020_supplemental.zip | null | null |
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