Search is not available for this dataset
title
string | authors
string | abstract
string | pdf
string | arXiv
string | bibtex
string | url
string | detail_url
string | tags
string | supp
string | string |
|---|---|---|---|---|---|---|---|---|---|---|
PhD Learning: Learning With Pompeiu-Hausdorff Distances for Video-Based Vehicle Re-Identification | Jianan Zhao, Fengliang Qi, Guangyu Ren, Lin Xu | Vehicle re-identification (re-ID) is of great significance to urban operation, management, security and has gained more attention in recent years. However, two critical challenges in vehicle re-ID have primarily been underestimated, i.e., 1): how to make full use of raw data, and 2): how to learn a robust re-ID model with noisy data. In this paper, we first create a video vehicle re-ID evaluation benchmark called VVeRI-901 and verify the performance of video-based re-ID is far better than static image-based one. Then we propose a new Pompeiu-hausdorff distance (PhD) learning method for video-to-video matching. It can alleviate the data noise problem caused by the occlusion in videos and thus improve re-ID performance significantly. Extensive empirical results on video-based vehicle and person re-ID datasets, i.e., VVeRI-901, MARS and PRID2011, demonstrate the superiority of the proposed method. The source code of our proposed method is available at https://github.com/emdata-ailab/PhD-Learning. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhao_PhD_Learning_Learning_With_Pompeiu-Hausdorff_Distances_for_Video-Based_Vehicle_Re-Identification_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhao_PhD_Learning_Learning_With_Pompeiu-Hausdorff_Distances_for_Video-Based_Vehicle_Re-Identification_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhao_PhD_Learning_Learning_With_Pompeiu-Hausdorff_Distances_for_Video-Based_Vehicle_Re-Identification_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhao_PhD_Learning_Learning_CVPR_2021_supplemental.pdf | null |
DeepVideoMVS: Multi-View Stereo on Video With Recurrent Spatio-Temporal Fusion | Arda Duzceker, Silvano Galliani, Christoph Vogel, Pablo Speciale, Mihai Dusmanu, Marc Pollefeys | We propose an online multi-view depth prediction approach on posed video streams, where the scene geometry information computed in the previous time steps is propagated to the current time step in an efficient and geometrically plausible way. The backbone of our approach is a real-time capable, lightweight encoder-decoder that relies on cost volumes computed from pairs of images. We extend it by placing a ConvLSTM cell at the bottleneck layer, which compresses an arbitrary amount of past information in its states. The novelty lies in propagating the hidden state of the cell by accounting for the viewpoint changes between time steps. At a given time step, we warp the previous hidden state into the current camera plane using the previous depth prediction. Our extension brings only a small overhead of computation time and memory consumption, while improving the depth predictions significantly. As a result, we outperform the existing state-of-the-art multi-view stereo methods on most of the evaluated metrics in hundreds of indoor scenes while maintaining a real-time performance. Code available: https://github.com/ardaduz/deep-video-mvs | https://openaccess.thecvf.com/content/CVPR2021/papers/Duzceker_DeepVideoMVS_Multi-View_Stereo_on_Video_With_Recurrent_Spatio-Temporal_Fusion_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.02177 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Duzceker_DeepVideoMVS_Multi-View_Stereo_on_Video_With_Recurrent_Spatio-Temporal_Fusion_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Duzceker_DeepVideoMVS_Multi-View_Stereo_on_Video_With_Recurrent_Spatio-Temporal_Fusion_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Duzceker_DeepVideoMVS_Multi-View_Stereo_CVPR_2021_supplemental.pdf | null |
Saliency-Guided Image Translation | Lai Jiang, Mai Xu, Xiaofei Wang, Leonid Sigal | In this paper, we propose a novel task for saliency-guided image translation, with the goal of image-to-image translation conditioned on the user specified saliency map. To address this problem, we develop a novel Generative Adversarial Network (GAN)-based model, called SalG-GAN. Given the original image and target saliency map, SalG-GAN can generate a translated image that satisfies the target saliency map. In SalG-GAN, a disentangled representation framework is proposed to encourage the model to learn diverse translations for the same target saliency condition. A saliency-based attention module is introduced as a special attention mechanism for facilitating the developed structures of saliency-guided generator, saliency cue encoder and saliency-guided global and local discriminators. Furthermore, we build a synthetic dataset and a real-world dataset with labeled visual attention for training and evaluating our SalG-GAN. The experimental results over both datasets verify the effectiveness of our model for saliency-guided image translation. | https://openaccess.thecvf.com/content/CVPR2021/papers/Jiang_Saliency-Guided_Image_Translation_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Jiang_Saliency-Guided_Image_Translation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Jiang_Saliency-Guided_Image_Translation_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Jiang_Saliency-Guided_Image_Translation_CVPR_2021_supplemental.pdf | null |
Weakly Supervised Learning of Rigid 3D Scene Flow | Zan Gojcic, Or Litany, Andreas Wieser, Leonidas J. Guibas, Tolga Birdal | We propose a data-driven scene flow estimation algorithm exploiting the observation that many 3D scenes can be explained by a collection of agents moving as rigid bodies. At the core of our method lies a deep architecture able to reason at the object-level by considering 3D scene flow in conjunction with other 3D tasks. This object level abstraction enables us to relax the requirement for dense scene flow supervision with simpler binary background segmentation mask and ego-motion annotations. Our mild supervision requirements make our method well suited for recently released massive data collections for autonomous driving, which do not contain dense scene flow annotations. As output, our model provides low-level cues like pointwise flow and higher-level cues such as holistic scene understanding at the level of rigid objects. We further propose a test-time optimization refining the predicted rigid scene flow. We showcase the effectiveness and generalization capacity of our method on four different autonomous driving datasets. We release our source code and pre-trained models under github.com/zgojcic/Rigid3DSceneFlow. | https://openaccess.thecvf.com/content/CVPR2021/papers/Gojcic_Weakly_Supervised_Learning_of_Rigid_3D_Scene_Flow_CVPR_2021_paper.pdf | http://arxiv.org/abs/2102.08945 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Gojcic_Weakly_Supervised_Learning_of_Rigid_3D_Scene_Flow_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Gojcic_Weakly_Supervised_Learning_of_Rigid_3D_Scene_Flow_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Gojcic_Weakly_Supervised_Learning_CVPR_2021_supplemental.pdf | null |
InverseForm: A Loss Function for Structured Boundary-Aware Segmentation | Shubhankar Borse, Ying Wang, Yizhe Zhang, Fatih Porikli | We present a novel boundary-aware loss term for semantic segmentation using an inverse-transformation network, which efficiently learns the degree of parametric transformations between estimated and target boundaries. This plug-in loss term complements the cross-entropy loss in capturing boundary transformations and allows consistent and significant performance improvement on segmentation backbone models without increasing their size and computational complexity. We analyze the quantitative and qualitative effects of our loss function on three indoor and outdoor segmentation benchmarks, including Cityscapes, NYU-Depth-v2, and PASCAL, integrating it into the training phase of several backbone networks in both single-task and multi-task settings. Our extensive experiments show that the proposed method consistently outperforms baselines, and even sets the new state-of-the-art on two datasets. | https://openaccess.thecvf.com/content/CVPR2021/papers/Borse_InverseForm_A_Loss_Function_for_Structured_Boundary-Aware_Segmentation_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.02745 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Borse_InverseForm_A_Loss_Function_for_Structured_Boundary-Aware_Segmentation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Borse_InverseForm_A_Loss_Function_for_Structured_Boundary-Aware_Segmentation_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Borse_InverseForm_A_Loss_CVPR_2021_supplemental.pdf | null |
Towards Accurate Text-Based Image Captioning With Content Diversity Exploration | Guanghui Xu, Shuaicheng Niu, Mingkui Tan, Yucheng Luo, Qing Du, Qi Wu | Text-based image captioning (TextCap) which aims to read and reason images with texts is crucial for a machine to understand a detailed and complex scene environment, considering that texts are omnipresent in daily life. This task, however, is very challenging because an image often contains complex texts and visual information that is hard to be described comprehensively. Existing methods attempt to extend the traditional image captioning methods to solve this task, which focus on describing the overall scene of images by one global caption. This is infeasible because the complex text and visual information cannot be described well within one caption. To resolve this difficulty, we seek to generate multiple captions that accurately describe different parts of an image in detail. To achieve this purpose, there are three key challenges: 1) it is hard to decide which parts of the texts of images to copy or paraphrase; 2) it is non-trivial to capture the complex relationship between diverse texts in an image; 3) how to generate multiple captions with diverse content is still an open problem. To conquer these, we propose a novel Anchor-Captioner method. Specifically, we first find the important tokens which are supposed to be paid more attention to and consider them as anchors. Then, for each chosen anchor, we group its relevant texts to construct the corresponding anchor-centred graph (ACG). Last, based on different ACGs, we conduct the multi-view caption generation to improve the content diversity of generated captions. Experimental results show that our method not only achieves SOTA performance but also generates diverse captions to describe images. | https://openaccess.thecvf.com/content/CVPR2021/papers/Xu_Towards_Accurate_Text-Based_Image_Captioning_With_Content_Diversity_Exploration_CVPR_2021_paper.pdf | http://arxiv.org/abs/2105.03236 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Xu_Towards_Accurate_Text-Based_Image_Captioning_With_Content_Diversity_Exploration_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Xu_Towards_Accurate_Text-Based_Image_Captioning_With_Content_Diversity_Exploration_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Xu_Towards_Accurate_Text-Based_CVPR_2021_supplemental.pdf | null |
Learning Placeholders for Open-Set Recognition | Da-Wei Zhou, Han-Jia Ye, De-Chuan Zhan | Traditional classifiers are deployed under closed-set setting, with both training and test classes belong to the same set. However, real-world applications probably face the input of unknown categories, and the model will recognize them as known ones. Under such circumstances, open-set recognition is proposed to maintain classification performance on known classes and reject unknowns. The closed-set models make overconfident predictions over familiar known class instances, so that calibration and thresholding across categories become essential issues when extending to an open-set environment. To this end, we proposed to learn PlaceholdeRs for Open-SEt Recognition (Proser), which prepares for the unknown classes by allocating placeholders for both data and classifier. In detail, learning data placeholders tries to anticipate open-set class data, thus transforms closed-set training into open-set training. Besides, to learn the invariant information between target and non-target classes, we reserve classifier placeholders as the class-specific boundary between known and unknown. The proposed Proser efficiently generates novel class by manifold mixup, and adaptively sets the value of reserved open-set classifier during training. Experiments on various datasets validate the effectiveness of our proposed method. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhou_Learning_Placeholders_for_Open-Set_Recognition_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.15086 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhou_Learning_Placeholders_for_Open-Set_Recognition_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhou_Learning_Placeholders_for_Open-Set_Recognition_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhou_Learning_Placeholders_for_CVPR_2021_supplemental.pdf | null |
CodedStereo: Learned Phase Masks for Large Depth-of-Field Stereo | Shiyu Tan, Yicheng Wu, Shoou-I Yu, Ashok Veeraraghavan | Conventional stereo suffers from a fundamental trade-off between imaging volume and signal-to-noise ratio (SNR) -- due to the conflicting impact of aperture size on both these variables. Inspired by the extended depth of field cameras, we propose a novel end-to-end learning-based technique to overcome this limitation, by introducing a phase mask at the aperture plane of the cameras in a stereo imaging system. The phase mask creates a depth-dependent point spread function, allowing us to recover sharp image texture and stereo correspondence over a significantly extended depth of field (EDOF) than conventional stereo. The phase mask pattern, the EDOF image reconstruction, and the stereo disparity estimation are all trained together using an end-to-end learned deep neural network. We perform theoretical analysis and characterization of the proposed approach and show a 6x increase in volume that can be imaged in simulation. We also build an experimental prototype and validate the approach using real-world results acquired using this prototype system. | https://openaccess.thecvf.com/content/CVPR2021/papers/Tan_CodedStereo_Learned_Phase_Masks_for_Large_Depth-of-Field_Stereo_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.04641 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Tan_CodedStereo_Learned_Phase_Masks_for_Large_Depth-of-Field_Stereo_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Tan_CodedStereo_Learned_Phase_Masks_for_Large_Depth-of-Field_Stereo_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Tan_CodedStereo_Learned_Phase_CVPR_2021_supplemental.pdf | null |
More Photos Are All You Need: Semi-Supervised Learning for Fine-Grained Sketch Based Image Retrieval | Ayan Kumar Bhunia, Pinaki Nath Chowdhury, Aneeshan Sain, Yongxin Yang, Tao Xiang, Yi-Zhe Song | A fundamental challenge faced by existing Fine-Grained Sketch-Based Image Retrieval (FG-SBIR) models is the data scarcity -- model performances are largely bottlenecked by the lack of sketch-photo pairs. Whilst the number of photos can be easily scaled, each corresponding sketch still needs to be individually produced. In this paper, we aim to mitigate such an upper-bound on sketch data, and study whether unlabelled photos alone (of which they are many) can be cultivated for performance gain. In particular, we introduce a novel semi-supervised framework for cross-modal retrieval that can additionally leverage large-scale unlabelled photos to account for data scarcity. At the center of our semi-supervision design is a sequential photo-to-sketch generation model that aims to generate paired sketches for unlabelled photos. Importantly, we further introduce a discriminator-guided mechanism to guide against unfaithful generation, together with a distillation loss-based regularizer to provide tolerance against noisy training samples. Last but not least, we treat generation and retrieval as two conjugate problems, where a joint learning procedure is devised for each module to mutually benefit from each other. Extensive experiments show that our semi-supervised model yields a significant performance boost over the state-of-the-art supervised alternatives, as well as existing methods that can exploit unlabelled photos for FG-SBIR. | https://openaccess.thecvf.com/content/CVPR2021/papers/Bhunia_More_Photos_Are_All_You_Need_Semi-Supervised_Learning_for_Fine-Grained_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.13990 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Bhunia_More_Photos_Are_All_You_Need_Semi-Supervised_Learning_for_Fine-Grained_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Bhunia_More_Photos_Are_All_You_Need_Semi-Supervised_Learning_for_Fine-Grained_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Bhunia_More_Photos_Are_CVPR_2021_supplemental.pdf | null |
Unsupervised Hyperbolic Representation Learning via Message Passing Auto-Encoders | Jiwoong Park, Junho Cho, Hyung Jin Chang, Jin Young Choi | Most of the existing literature regarding hyperbolic embedding concentrate upon supervised learning, whereas the use of unsupervised hyperbolic embedding is less well explored. In this paper, we analyze how unsupervised tasks can benefit from learned representations in hyperbolic space. To explore how well the hierarchical structure of unlabeled data can be represented in hyperbolic spaces, we design a novel hyperbolic message passing auto-encoder whose overall auto-encoding is performed in hyperbolic space. The proposed model conducts auto-encoding the networks via fully utilizing hyperbolic geometry in message passing. Through extensive quantitative and qualitative analyses, we validate the properties and benefits of the unsupervised hyperbolic representations. Codes are available at https://github.com/junhocho/HGCAE. | https://openaccess.thecvf.com/content/CVPR2021/papers/Park_Unsupervised_Hyperbolic_Representation_Learning_via_Message_Passing_Auto-Encoders_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.16046 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Park_Unsupervised_Hyperbolic_Representation_Learning_via_Message_Passing_Auto-Encoders_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Park_Unsupervised_Hyperbolic_Representation_Learning_via_Message_Passing_Auto-Encoders_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Park_Unsupervised_Hyperbolic_Representation_CVPR_2021_supplemental.pdf | null |
Retinex-Inspired Unrolling With Cooperative Prior Architecture Search for Low-Light Image Enhancement | Risheng Liu, Long Ma, Jiaao Zhang, Xin Fan, Zhongxuan Luo | Low-light image enhancement plays very important roles in low-level vision areas. Recent works have built a great deal of deep learning models to address this task. However, these approaches mostly rely on significant architecture engineering and suffer from high computational burden. In this paper, we propose a new method, named Retinex-inspired Unrolling with Architecture Search (RUAS), to construct lightweight yet effective enhancement network for low-light images in real-world scenario. Specifically, building upon Retinex rule, RUAS first establishes models to characterize the intrinsic underexposed structure of low-light images and unroll their optimization processes to construct our holistic propagation structure. Then by designing a cooperative reference-free learning strategy to discover low-light prior architectures from a compact search space, RUAS is able to obtain a top-performing image enhancement network, which is with fast speed and requires few computational resources. Extensive experiments verify the superiority of our RUAS framework against recently proposed state-of-the-art methods. The project page is available at http://dutmedia.org/RUAS/. | https://openaccess.thecvf.com/content/CVPR2021/papers/Liu_Retinex-Inspired_Unrolling_With_Cooperative_Prior_Architecture_Search_for_Low-Light_Image_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.05609 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_Retinex-Inspired_Unrolling_With_Cooperative_Prior_Architecture_Search_for_Low-Light_Image_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_Retinex-Inspired_Unrolling_With_Cooperative_Prior_Architecture_Search_for_Low-Light_Image_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Liu_Retinex-Inspired_Unrolling_With_CVPR_2021_supplemental.pdf | null |
Relevance-CAM: Your Model Already Knows Where To Look | Jeong Ryong Lee, Sewon Kim, Inyong Park, Taejoon Eo, Dosik Hwang | With increasing fields of application for neural networks and the development of neural networks, the ability to explain deep learning models is also becoming increasingly important. Especially, prior to practical applications, it is crucial to analyze a model's inference and the process of generating the results. A common explanation method is Class Activation Mapping(CAM) based method where it is often used to understand the last layer of the convolutional neural networks popular in the field of Computer Vision. In this paper, we propose a novel CAM method named Relevance-weighted Class Activation Mapping(Relevance-CAM) that utilizes Layer-wise Relevance Propagation to obtain the weighting components. This allows the explanation map to be faithful and robust to the shattered gradient problem, a shared problem of the gradient based CAM methods that causes noisy saliency maps for intermediate layers. Therefore, our proposed method can better explain a model by correctly analyzing the intermediate layers as well as the last convolutional layer. In this paper, we visualize how each layer of the popular image processing models extracts class specific features using Relevance-CAM, evaluate the localization ability, and show why the gradient based CAM cannot be used to explain the intermediate layers, proven by experimenting the weighting component. Relevance-CAM outperforms other CAM-based methods in recognition and localization evaluation in layers of any depth. The source code is available at: https://github.com/mongeoroo/Relevance-CAM | https://openaccess.thecvf.com/content/CVPR2021/papers/Lee_Relevance-CAM_Your_Model_Already_Knows_Where_To_Look_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Lee_Relevance-CAM_Your_Model_Already_Knows_Where_To_Look_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Lee_Relevance-CAM_Your_Model_Already_Knows_Where_To_Look_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Lee_Relevance-CAM_Your_Model_CVPR_2021_supplemental.pdf | null |
Boundary IoU: Improving Object-Centric Image Segmentation Evaluation | Bowen Cheng, Ross Girshick, Piotr Dollar, Alexander C. Berg, Alexander Kirillov | We present Boundary IoU (Intersection-over-Union), a new segmentation evaluation measure focused on boundary quality. We perform an extensive analysis across different error types and object sizes and show that Boundary IoU is significantly more sensitive than the standard Mask IoU measure to boundary errors for large objects and does not over-penalize errors on smaller objects. The new quality measure displays several desirable characteristics like symmetry w.r.t. prediction/ground truth pairs and balanced responsiveness across scales, which makes it more suitable for segmentation evaluation than other boundary-focused measures like Trimap IoU and F-measure. Based on Boundary IoU, we update the standard evaluation protocols for instance and panoptic segmentation tasks by proposing the Boundary AP (Average Precision) and Boundary PQ (Panoptic Quality) metrics, respectively. Our experiments show that the new evaluation metrics track boundary quality improvements that are generally overlooked by current Mask IoU-based evaluation metrics. We hope that the adoption of the new boundary-sensitive evaluation metrics will lead to rapid progress in segmentation methods that improve boundary quality. | https://openaccess.thecvf.com/content/CVPR2021/papers/Cheng_Boundary_IoU_Improving_Object-Centric_Image_Segmentation_Evaluation_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.16562 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Cheng_Boundary_IoU_Improving_Object-Centric_Image_Segmentation_Evaluation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Cheng_Boundary_IoU_Improving_Object-Centric_Image_Segmentation_Evaluation_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Cheng_Boundary_IoU_Improving_CVPR_2021_supplemental.pdf | null |
KeepAugment: A Simple Information-Preserving Data Augmentation Approach | Chengyue Gong, Dilin Wang, Meng Li, Vikas Chandra, Qiang Liu | Data augmentation (DA) is an essential technique for training state-of-the-art deep learning systems. In this paper, we empirically show data augmentation might introduce noisy augmented examples and consequently hurt the performance on unaugmented data during inference. To alleviate this issue, we propose a simple yet highly effective approach, dubbed KeepAugment, to increase augmented images fidelity. The idea is first to use the saliency map to detect important regions on the original images and then preserve these informative regions during augmentation. This information-preserving strategy allows us to generate more faithful training examples. Empirically, we demonstrate our method significantly improves on a number of prior art data augmentation schemes, e.g. AutoAugment, Cutout, random erasing, achieving promising results on image classification, semi-supervised image classification, multi-view multi-camera tracking and object detection. | https://openaccess.thecvf.com/content/CVPR2021/papers/Gong_KeepAugment_A_Simple_Information-Preserving_Data_Augmentation_Approach_CVPR_2021_paper.pdf | http://arxiv.org/abs/2011.11778 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Gong_KeepAugment_A_Simple_Information-Preserving_Data_Augmentation_Approach_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Gong_KeepAugment_A_Simple_Information-Preserving_Data_Augmentation_Approach_CVPR_2021_paper.html | CVPR 2021 | null | null |
On Robustness and Transferability of Convolutional Neural Networks | Josip Djolonga, Jessica Yung, Michael Tschannen, Rob Romijnders, Lucas Beyer, Alexander Kolesnikov, Joan Puigcerver, Matthias Minderer, Alexander D'Amour, Dan Moldovan, Sylvain Gelly, Neil Houlsby, Xiaohua Zhai, Mario Lucic | Modern deep convolutional networks (CNNs) are often criticized for not generalizing under distributional shifts. However, several recent breakthroughs in transfer learning suggest that these networks can cope with severe distribution shifts and successfully adapt to new tasks from a few training examples. In this work we study the interplay between out-of-distribution and transfer performance of modern image classification CNNs for the first time and investigate the impact of the pre-training data size, the model scale, and the data preprocessing pipeline. We find that increasing both the training set and model sizes significantly improve the distributional shift robustness. Furthermore, we show that, perhaps surprisingly, simple changes in the preprocessing such as modifying the image resolution can significantly mitigate robustness issues in some cases. Finally, we outline the shortcomings of existing robustness evaluation datasets and introduce a synthetic dataset SI-Score we use for a systematic analysis across factors of variation common in visual data such as object scale and position. | https://openaccess.thecvf.com/content/CVPR2021/papers/Djolonga_On_Robustness_and_Transferability_of_Convolutional_Neural_Networks_CVPR_2021_paper.pdf | http://arxiv.org/abs/2007.08558 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Djolonga_On_Robustness_and_Transferability_of_Convolutional_Neural_Networks_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Djolonga_On_Robustness_and_Transferability_of_Convolutional_Neural_Networks_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Djolonga_On_Robustness_and_CVPR_2021_supplemental.pdf | null |
POSEFusion: Pose-Guided Selective Fusion for Single-View Human Volumetric Capture | Zhe Li, Tao Yu, Zerong Zheng, Kaiwen Guo, Yebin Liu | We propose POse-guided SElective Fusion (POSEFusion), a single-view human volumetric capture method that leverages tracking-based methods and tracking-free inference to achieve high-fidelity and dynamic 3D reconstruction. By contributing a novel reconstruction framework which contains pose-guided keyframe selection and robust implicit surface fusion, our method fully utilizes the advantages of both tracking-based methods and tracking-free inference methods, and finally enables the high-fidelity reconstruction of dynamic surface details even in the invisible regions. We formulate the keyframe selection as a dynamic programming problem to guarantee the temporal continuity of the reconstructed sequence. Moreover, the novel robust implicit surface fusion involves an adaptive blending weight to preserve high-fidelity surface details and an automatic collision handling method to deal with the potential self-collisions. Overall, our method enables high-fidelity and dynamic capture in both visible and invisible regions from a single RGBD camera, and the results and experiments show that our method outperforms state-of-the-art methods. | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_POSEFusion_Pose-Guided_Selective_Fusion_for_Single-View_Human_Volumetric_Capture_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.15331 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Li_POSEFusion_Pose-Guided_Selective_Fusion_for_Single-View_Human_Volumetric_Capture_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Li_POSEFusion_Pose-Guided_Selective_Fusion_for_Single-View_Human_Volumetric_Capture_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Li_POSEFusion_Pose-Guided_Selective_CVPR_2021_supplemental.pdf | null |
Exploring Adversarial Fake Images on Face Manifold | Dongze Li, Wei Wang, Hongxing Fan, Jing Dong | Images synthesized by powerful generative adversarial network (GAN) based methods have drawn moral and privacy concerns. Although image forensic models have reached great performance in detecting fake images from real ones, these models can be easily fooled with a simple adversarial attack. But, the noise adding adversarial samples are also arousing suspicion. In this paper, instead of adding adversarial noise, we optimally search adversarial points on face manifold to generate anti-forensic fake face images. We iteratively do a gradient-descent with each small step in the latent space of a generative model, e.g. Style-GAN, to find an adversarial latent vector, which is similar to norm-based adversarial attack but in latent space. Then, the generated fake images driven by the adversarial latent vectors with the help of GANs can defeat main-stream forensic models. For examples, they make the accuracy of deepfake detection models based on Xception or EfficientNet drop from over 90% to nearly 0%, meanwhile maintaining high visual quality. In addition, we find manipulating noise vectors n at different levels have different impacts on attack success rate, and the generated adversarial images mainly have changes on facial texture or face attributes. | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_Exploring_Adversarial_Fake_Images_on_Face_Manifold_CVPR_2021_paper.pdf | http://arxiv.org/abs/2101.03272 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Exploring_Adversarial_Fake_Images_on_Face_Manifold_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Exploring_Adversarial_Fake_Images_on_Face_Manifold_CVPR_2021_paper.html | CVPR 2021 | null | null |
Reinforced Attention for Few-Shot Learning and Beyond | Jie Hong, Pengfei Fang, Weihao Li, Tong Zhang, Christian Simon, Mehrtash Harandi, Lars Petersson | Few-shot learning aims to correctly recognize query samples from unseen classes given a limited number of support samples, often by relying on global embeddings of images. In this paper, we propose to equip the backbone network with an attention agent, which is trained by reinforcement learning. The policy gradient algorithm is employed to train the agent towards adaptively localizing the representative regions on feature maps over time. We further design a reward function based on the prediction of the held-out data, thus helping the attention mechanism to generalize better across the unseen classes. The extensive experiments show, with the help of the reinforced attention, that our embedding network has the capability to progressively generate a more discriminative representation in few-shot learning. Moreover, experiments on the task of image classification also show the effectiveness of the proposed design. | https://openaccess.thecvf.com/content/CVPR2021/papers/Hong_Reinforced_Attention_for_Few-Shot_Learning_and_Beyond_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.04192 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Hong_Reinforced_Attention_for_Few-Shot_Learning_and_Beyond_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Hong_Reinforced_Attention_for_Few-Shot_Learning_and_Beyond_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Hong_Reinforced_Attention_for_CVPR_2021_supplemental.pdf | null |
HOTR: End-to-End Human-Object Interaction Detection With Transformers | Bumsoo Kim, Junhyun Lee, Jaewoo Kang, Eun-Sol Kim, Hyunwoo J. Kim | Human-Object Interaction (HOI) detection is a task of identifying "a set of interactions" in an image, which involves the i) localization of the subject (i.e., humans) and target (i.e., objects) of interaction, and ii) the classification of the interaction labels. Most existing methods have addressed this task in an indirect way by detecting human and object instances and individually inferring every pair of the detected instances. In this paper, we present a novel framework, referred by HOTR, which directly predicts a set of <human, object, interaction> triplets from an image based on a transformer encoder-decoder architecture. Through the set prediction, our method effectively exploits the inherent semantic relationships in an image and does not require time-consuming post-processing which is the main bottleneck of existing methods. Our proposed algorithm achieves the state-of-the-art performance in two HOI detection benchmarks with an inference time under 1 ms after object detection. | https://openaccess.thecvf.com/content/CVPR2021/papers/Kim_HOTR_End-to-End_Human-Object_Interaction_Detection_With_Transformers_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.13682 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Kim_HOTR_End-to-End_Human-Object_Interaction_Detection_With_Transformers_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Kim_HOTR_End-to-End_Human-Object_Interaction_Detection_With_Transformers_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Kim_HOTR_End-to-End_Human-Object_CVPR_2021_supplemental.pdf | null |
Deep Video Matting via Spatio-Temporal Alignment and Aggregation | Yanan Sun, Guanzhi Wang, Qiao Gu, Chi-Keung Tang, Yu-Wing Tai | Despite the significant progress made by deep learning in natural image matting, there has been so far no representative work on deep learning for video matting due to the inherent technical challenges in reasoning temporal domain and lack of large-scale video matting datasets. In this paper, we propose a deep learning-based video matting framework which employs a novel and effective spatio-temporal feature aggregation module (ST-FAM). As optical flow estimation can be very unreliable within matting regions, ST-FAM is designed to effectively align and aggregate information across different spatial scales and temporal frames within the network decoder. To eliminate frame-by-frame trimap annotations, a lightweight interactive trimap propagation network is also introduced. The other contribution consists of a large-scale video matting dataset with groundtruth alpha mattes for quantitative evaluation and real-world high-resolution videos with trimaps for qualitative evaluation. Quantitative and qualitative experimental results show that our framework significantly outperforms conventional video matting and deep image matting methods applied to video in presence of multi-frame temporal information. | https://openaccess.thecvf.com/content/CVPR2021/papers/Sun_Deep_Video_Matting_via_Spatio-Temporal_Alignment_and_Aggregation_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.11208 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Sun_Deep_Video_Matting_via_Spatio-Temporal_Alignment_and_Aggregation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Sun_Deep_Video_Matting_via_Spatio-Temporal_Alignment_and_Aggregation_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Sun_Deep_Video_Matting_CVPR_2021_supplemental.zip | null |
Triple-Cooperative Video Shadow Detection | Zhihao Chen, Liang Wan, Lei Zhu, Jia Shen, Huazhu Fu, Wennan Liu, Jing Qin | Shadow detection in single image has received signifi-cant research interests in recent years. However, much lessworks has been explored in shadow detection over dynamicscenes. The bottleneck is the lack of a well-establisheddataset with high-quality annotations for video shadow de-tection. In this work, we collect a new video shadow detec-tion dataset (ViSha), which contains120videos with11,685frames, covering 60 object categories, varying lengths, anddifferent motion/lighting conditions. All the frames are an-notated with a high-quality pixel-level shadow mask. Tothe best of our knowledge, this is the first learning-orienteddataset for video shadow detection. Furthermore, we de-velop a new baseline model, named triple-cooperative videoshadow detection network (TVSD-Net). It utilizes tripleparallel networks in a cooperative manner to learn discrim-inative representations at intra-video and inter-video lev-els. Within the network, a dual gated co-attention moduleis proposed to constrain features from neighboring framesin the same video, while an auxiliary similarity loss is in-troduced to mine semantic information between differentvideos. Finally, we conduct a comprehensive study on ViShadataset, systematically evaluating 10 state-of-the-art mod-els (including single image shadow detectors, video ob-ject and saliency detection methods). Experimental resultsdemonstrate that our model outperforms SOTA competitors. | https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_Triple-Cooperative_Video_Shadow_Detection_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.06533 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Triple-Cooperative_Video_Shadow_Detection_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Triple-Cooperative_Video_Shadow_Detection_CVPR_2021_paper.html | CVPR 2021 | null | null |
Scale-Aware Graph Neural Network for Few-Shot Semantic Segmentation | Guo-Sen Xie, Jie Liu, Huan Xiong, Ling Shao | Few-shot semantic segmentation (FSS) aims to segment unseen class objects given very few densely-annotated support images from the same class. Existing FSS methods find the query object by using support prototypes or by directly relying on heuristic multi-scale feature fusion. However, they fail to fully leverage the high-order appearance relationships between multi-scale features among the support-query image pairs, thus leading to an inaccurate localization of the query objects. To tackle the above challenge, we propose an end-to-end scale-aware graph neural network (SAGNN) by reasoning the cross-scale relations among the support-query images for FSS. Specifically, a scale-aware graph is first built by taking support-induced multi-scale query features as nodes and, meanwhile, each edge is modeled as the pairwise interaction of its connected nodes. By progressive message passing over this graph, SAGNN is capable of capturing cross-scale relations and overcoming object variations (e.g., appearance, scale and location), and can thus learn more precise node embeddings. This in turn enables it to predict more accurate foreground objects. Moreover, to make full use of the location relations across scales for the query image, a novel self-node collaboration mechanism is proposed to enrich the current node, which endows SAGNN the ability of perceiving different resolutions of the same objects. Extensive experiments on PASCAL-5i and COCO-20i show that SAGNN achieves state-of-the-art results. | https://openaccess.thecvf.com/content/CVPR2021/papers/Xie_Scale-Aware_Graph_Neural_Network_for_Few-Shot_Semantic_Segmentation_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Xie_Scale-Aware_Graph_Neural_Network_for_Few-Shot_Semantic_Segmentation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Xie_Scale-Aware_Graph_Neural_Network_for_Few-Shot_Semantic_Segmentation_CVPR_2021_paper.html | CVPR 2021 | null | null |
Continuous Face Aging via Self-Estimated Residual Age Embedding | Zeqi Li, Ruowei Jiang, Parham Aarabi | Face synthesis, including face aging, in particular, has been one of the major topics that witnessed a substantial improvement in image fidelity by using generative adversarial networks (GANs). Most existing face aging approaches divide the dataset into several age groups and leverage group-based training strategies, which lacks the ability to provide fine-controlled continuous aging synthesis in nature. In this work, we propose a unified network structure that embeds a linear age estimator into a GAN-based model, where the embedded age estimator is trained jointly with the encoder and decoder to estimate the age of a face image and provide a personalized target age embedding for age progression/regression. The personalized target age embedding is synthesized by incorporating both personalized residual age embedding of the current age and exemplar-face aging basis of the target age, where all preceding aging bases are derived from the learned weights of the linear age estimator. This formulation brings the unified perspective of estimating the age and generating personalized aged face, where self-estimated age embeddings can be learned for every single age. The qualitative and quantitative evaluations on different datasets further demonstrate the significant improvement in the continuous face aging aspect over the state-of-the-art. | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_Continuous_Face_Aging_via_Self-Estimated_Residual_Age_Embedding_CVPR_2021_paper.pdf | http://arxiv.org/abs/2105.00020 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Continuous_Face_Aging_via_Self-Estimated_Residual_Age_Embedding_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Continuous_Face_Aging_via_Self-Estimated_Residual_Age_Embedding_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Li_Continuous_Face_Aging_CVPR_2021_supplemental.pdf | null |
Towards Fast and Accurate Real-World Depth Super-Resolution: Benchmark Dataset and Baseline | Lingzhi He, Hongguang Zhu, Feng Li, Huihui Bai, Runmin Cong, Chunjie Zhang, Chunyu Lin, Meiqin Liu, Yao Zhao | Depth maps obtained by commercial depth sensors are always in low-resolution, making it difficult to be used in various computer vision tasks. Thus, depth map super-resolution (SR) is a practical and valuable task, which upscales the depth map into high-resolution (HR) space. However, limited by the lack of real-world paired low-resolution (LR) and HR depth maps, most existing methods use downsampling to obtain paired training samples. To this end, we first construct a large-scale dataset named "RGB-D-D", which can greatly promote the study of depth map SR and even more depth-related real-world tasks. The "D-D" in our dataset represents the paired LR and HR depth maps captured from mobile phone and Lucid Helios respectively ranging from indoor scenes to challenging outdoor scenes. Besides, we provide a fast depth map super-resolution (FDSR) baseline, in which the high-frequency component adaptively decomposed from RGB image to guide the depth map SR. Extensive experiments on existing public datasets demonstrate the effectiveness and efficiency of our network compared with the state-of-the-art methods. Moreover, for the real-world LR depth maps, our algorithm can produce more accurate HR depth maps with clearer boundaries and to some extent correct the depth value errors. | https://openaccess.thecvf.com/content/CVPR2021/papers/He_Towards_Fast_and_Accurate_Real-World_Depth_Super-Resolution_Benchmark_Dataset_and_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/He_Towards_Fast_and_Accurate_Real-World_Depth_Super-Resolution_Benchmark_Dataset_and_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/He_Towards_Fast_and_Accurate_Real-World_Depth_Super-Resolution_Benchmark_Dataset_and_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/He_Towards_Fast_and_CVPR_2021_supplemental.pdf | null |
Jigsaw Clustering for Unsupervised Visual Representation Learning | Pengguang Chen, Shu Liu, Jiaya Jia | Unsupervised representation learning with contrastive learning achieves great success recently. However, these methods have to duplicate each training batch to construct contrastive pairs, ie, each training batch and its augmented version should be forwarded simultaneously, leading to nearly double computation resource demand. We propose a novel Jigsaw Clustering pretext task in this paper, which only needs to forward each training batch itself, nearly reducing the training cost by a half. Our method makes use of information from both intra-image and inter-images, and outperforms previous single-batch based methods by a large margin, even comparable to the costly contrastive learning methods with only half the number of training batches. Our method shows that multiple batches during training are not necessary, and opens a new door for future research of single-batch based unsupervised methods. Our models trained on ImageNet datasets achieve state-of-the-art results with linear classification, outperform previous single-batch methods by 2.6%. Models transfer to COCO datasets outperforms MoCo v2 by 0.4% with only half the number of training samples. Our pretrained models outperform supervised ImageNet pretrained models on CIFAR-10 and CIFAR-100 datasets by 0.9% and 4.1% respectively. | https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_Jigsaw_Clustering_for_Unsupervised_Visual_Representation_Learning_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.00323 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Jigsaw_Clustering_for_Unsupervised_Visual_Representation_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Jigsaw_Clustering_for_Unsupervised_Visual_Representation_Learning_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Chen_Jigsaw_Clustering_for_CVPR_2021_supplemental.pdf | null |
DI-Fusion: Online Implicit 3D Reconstruction With Deep Priors | Jiahui Huang, Shi-Sheng Huang, Haoxuan Song, Shi-Min Hu | Previous online 3D dense reconstruction methods struggle to achieve the balance between memory storage and surface quality, largely due to the usage of stagnant underlying geometry representation, such as TSDF (truncated signed distance functions) or surfels, without any knowledge of the scene priors. In this paper, we present DI-Fusion (Deep Implicit Fusion), based on a novel 3D representation, i.e. Probabilistic Local Implicit Voxels (PLIVoxs), for online 3D reconstruction with a commodity RGB-D camera. Our PLIVox encodes scene priors considering both the local geometry and uncertainty parameterized by a deep neural network. With such deep priors, we are able to perform online implicit 3D reconstruction achieving state-of-the-art camera trajectory estimation accuracy and mapping quality, while achieving better storage efficiency compared with previous online 3D reconstruction approaches. | https://openaccess.thecvf.com/content/CVPR2021/papers/Huang_DI-Fusion_Online_Implicit_3D_Reconstruction_With_Deep_Priors_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Huang_DI-Fusion_Online_Implicit_3D_Reconstruction_With_Deep_Priors_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Huang_DI-Fusion_Online_Implicit_3D_Reconstruction_With_Deep_Priors_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Huang_DI-Fusion_Online_Implicit_CVPR_2021_supplemental.zip | null |
Square Root Bundle Adjustment for Large-Scale Reconstruction | Nikolaus Demmel, Christiane Sommer, Daniel Cremers, Vladyslav Usenko | We propose a new formulation for the bundle adjustment problem which relies on nullspace marginalization of landmark variables by QR decomposition. Our approach, which we call square root bundle adjustment, is algebraically equivalent to the commonly used Schur complement trick, improves the numeric stability of computations, and allows for solving large-scale bundle adjustment problems with single-precision floating-point numbers. We show in real-world experiments with the BAL datasets that even in single precision the proposed solver achieves on average equally accurate solutions compared to Schur complement solvers using double precision. It runs significantly faster, but can require larger amounts of memory on dense problems. The proposed formulation relies on simple linear algebra operations and opens the way for efficient implementations of bundle adjustment on hardware platforms optimized for single-precision linear algebra processing. | https://openaccess.thecvf.com/content/CVPR2021/papers/Demmel_Square_Root_Bundle_Adjustment_for_Large-Scale_Reconstruction_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.01843 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Demmel_Square_Root_Bundle_Adjustment_for_Large-Scale_Reconstruction_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Demmel_Square_Root_Bundle_Adjustment_for_Large-Scale_Reconstruction_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Demmel_Square_Root_Bundle_CVPR_2021_supplemental.pdf | null |
PatchMatch-Based Neighborhood Consensus for Semantic Correspondence | Jae Yong Lee, Joseph DeGol, Victor Fragoso, Sudipta N. Sinha | We address estimating dense correspondences between two images depicting different but semantically related scenes. End-to-end trainable deep neural networks incorporating neighborhood consensus cues are currently the best methods for this task. However, these architectures require exhaustive matching and 4D convolutions over matching costs for all pairs of feature map pixels. This makes them computationally expensive. We present a more efficient neighborhood consensus approach based on PatchMatch. For higher accuracy, we propose to use a learned local 4D scoring function for evaluating candidates during the PatchMatch iterations. We have devised an approach to jointly train the scoring function and the feature extraction modules by embedding them into a proxy model which is end-to-end differentiable. The modules are trained in a supervised setting using a cross-entropy loss to directly incorporate sparse keypoint supervision. Our evaluation on PF-Pascal and SPair-71K shows that our method significantly outperforms the state-of-the-art on both datasets while also being faster and using less memory. | https://openaccess.thecvf.com/content/CVPR2021/papers/Lee_PatchMatch-Based_Neighborhood_Consensus_for_Semantic_Correspondence_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Lee_PatchMatch-Based_Neighborhood_Consensus_for_Semantic_Correspondence_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Lee_PatchMatch-Based_Neighborhood_Consensus_for_Semantic_Correspondence_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Lee_PatchMatch-Based_Neighborhood_Consensus_CVPR_2021_supplemental.pdf | null |
Representative Forgery Mining for Fake Face Detection | Chengrui Wang, Weihong Deng | Although vanilla Convolutional Neural Network (CNN) based detectors can achieve satisfactory performance on fake face detection, we observe that the detectors tend to seek forgeries on a limited region of face, which reveals that the detectors is short of understanding of forgery. Therefore, we propose an attention-based data augmentation framework to guide detector refine and enlarge its attention. Specifically, our method tracks and occludes the Top-N sensitive facial regions, encouraging the detector to mine deeper into the regions ignored before for more representative forgery. Especially, our method is simple-to-use and can be easily integrated with various CNN models. Extensive experiments show that the detector trained with our method is capable to separately point out the representative forgery of fake faces generated by different manipulation techniques, and our method enables a vanilla CNN-based detector to achieve state-of-the-art performance without structure modification. | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_Representative_Forgery_Mining_for_Fake_Face_Detection_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.06609 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Representative_Forgery_Mining_for_Fake_Face_Detection_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Representative_Forgery_Mining_for_Fake_Face_Detection_CVPR_2021_paper.html | CVPR 2021 | null | null |
Look Closer To Segment Better: Boundary Patch Refinement for Instance Segmentation | Chufeng Tang, Hang Chen, Xiao Li, Jianmin Li, Zhaoxiang Zhang, Xiaolin Hu | Tremendous efforts have been made on instance segmentation but the mask quality is still not satisfactory. The boundaries of predicted instance masks are usually imprecise due to the low spatial resolution of feature maps and the imbalance problem caused by the extremely low proportion of boundary pixels. To address these issues, we propose a conceptually simple yet effective post-processing refinement framework to improve the boundary quality based on the results of any instance segmentation model, termed BPR. Following the idea of looking closer to segment boundaries better, we extract and refine a series of small boundary patches along the predicted instance boundaries. The refinement is accomplished by a boundary patch refinement network at higher resolution. The proposed BPR framework yields significant improvements over the Mask R-CNN baseline on Cityscapes benchmark, especially on the boundary-aware metrics. Moreover, by applying the BPR framework to the PolyTransform + SegFix baseline, we reached 1st place on the Cityscapes leaderboard. | https://openaccess.thecvf.com/content/CVPR2021/papers/Tang_Look_Closer_To_Segment_Better_Boundary_Patch_Refinement_for_Instance_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.05239 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Tang_Look_Closer_To_Segment_Better_Boundary_Patch_Refinement_for_Instance_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Tang_Look_Closer_To_Segment_Better_Boundary_Patch_Refinement_for_Instance_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Tang_Look_Closer_To_CVPR_2021_supplemental.pdf | null |
Adaptive Class Suppression Loss for Long-Tail Object Detection | Tong Wang, Yousong Zhu, Chaoyang Zhao, Wei Zeng, Jinqiao Wang, Ming Tang | To address the problem of long-tail distribution for the large vocabulary object detection task, existing methods usually divide the whole categories into several groups and treat each group with different strategies. These methods bring the following two problems. One is the training inconsistency between adjacent categories of similar sizes, and the other is that the learned model is lack of discrimination for tail categories which are semantically similar to some of the head categories. In this paper, we devise a novel Adaptive Class Suppression Loss (ACSL) to effectively tackle the above problems and improve the detection performance of tail categories. Specifically, we introduce a statistic-free perspective to analyze the long-tail distribution, breaking the limitation of manual grouping. According to this perspective, our ACSL adjusts the suppression gradients for each sample of each class adaptively, ensuring the training consistency and boosting the discrimination for rare categories. Extensive experiments on long-tail datasets LVIS and Open Images show that the our ACSL achieves 5.18% and 5.2% improvements with ResNet50-FPN, and sets a new state of the art. Code and models are available at https://github.com/CASIA-IVA-Lab/ACSL. | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_Adaptive_Class_Suppression_Loss_for_Long-Tail_Object_Detection_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.00885 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Adaptive_Class_Suppression_Loss_for_Long-Tail_Object_Detection_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Adaptive_Class_Suppression_Loss_for_Long-Tail_Object_Detection_CVPR_2021_paper.html | CVPR 2021 | null | null |
ChallenCap: Monocular 3D Capture of Challenging Human Performances Using Multi-Modal References | Yannan He, Anqi Pang, Xin Chen, Han Liang, Minye Wu, Yuexin Ma, Lan Xu | Capturing challenging human motions is critical for numerous applications, but it suffers from complex motion patterns and severe self-occlusion under the monocular setting. In this paper, we propose ChallenCap --- a template-based approach to capture challenging 3D human motions using a single RGB camera in a novel learning-and-optimization framework, with the aid of multi-modal references. We propose a hybrid motion inference stage with a generation network, which utilizes a temporal encoder-decoder to extract the motion details from the pair-wise sparse-view reference, as well as a motion discriminator to utilize the unpaired marker-based references to extract specific challenging motion characteristics in a data-driven manner. We further adopt a robust motion optimization stage to increase the tracking accuracy, by jointly utilizing the learned motion details from the supervised multi-modal references as well as the reliable motion hints from the input image reference. Extensive experiments on our new challenging motion dataset demonstrate the effectiveness and robustness of our approach to capture challenging human motions. | https://openaccess.thecvf.com/content/CVPR2021/papers/He_ChallenCap_Monocular_3D_Capture_of_Challenging_Human_Performances_Using_Multi-Modal_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.06747 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/He_ChallenCap_Monocular_3D_Capture_of_Challenging_Human_Performances_Using_Multi-Modal_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/He_ChallenCap_Monocular_3D_Capture_of_Challenging_Human_Performances_Using_Multi-Modal_CVPR_2021_paper.html | CVPR 2021 | null | null |
Automated Log-Scale Quantization for Low-Cost Deep Neural Networks | Sangyun Oh, Hyeonuk Sim, Sugil Lee, Jongeun Lee | Quantization plays an important role in deep neural network (DNN) hardware. In particular, logarithmic quantization has multiple advantages for DNN hardware implementations, and its weakness in terms of lower performance at high precision compared with linear quantization has been recently remedied by what we call selective two-word logarithmic quantization (STLQ). However, there is a lack of training methods designed for STLQ or even logarithmic quantization in general. In this paper we propose a novel STLQ-aware training method, which significantly outperforms the previous state-of-the-art training method for STLQ. Moreover, our training results demonstrate that with our new training method, STLQ applied to weight parameters of ResNet-18 can achieve the same level of performance as state-of-the-art quantization method, APoT, at 3-bit precision. We also apply our method to various DNNs in image enhancement and semantic segmentation, showing competitive results. | https://openaccess.thecvf.com/content/CVPR2021/papers/Oh_Automated_Log-Scale_Quantization_for_Low-Cost_Deep_Neural_Networks_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Oh_Automated_Log-Scale_Quantization_for_Low-Cost_Deep_Neural_Networks_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Oh_Automated_Log-Scale_Quantization_for_Low-Cost_Deep_Neural_Networks_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Oh_Automated_Log-Scale_Quantization_CVPR_2021_supplemental.pdf | null |
Hallucination Improves Few-Shot Object Detection | Weilin Zhang, Yu-Xiong Wang | Learning to detect novel objects with a few instances is challenging. A particularly challenging but practical regime is the extremely-low-shot regime (less than three training examples). One critical factor in improving few-shot detection is to handle the lack of variation in training data. The classifier relies on high intersection-over-union (IOU) boxes reported by the RPN to build a model of the category's variation in appearance. With only a few training examples, the variations are insufficient to train the classifier in novel classes. We propose to build a better model of variation in novel classes by transferring the shared within-class variation from base classes. We introduce a hallucinator network and insert it into a modern object detector model, which learns to generate additional training examples in the Region of Interest (ROI's) feature space. Our approach yields significant performance improvements on two state-of-the-art few-shot detectors with different proposal generation processes. We achieve new state-of-the-art in very low-shot regimes on widely used benchmarks PASCAL VOC and COCO. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_Hallucination_Improves_Few-Shot_Object_Detection_CVPR_2021_paper.pdf | http://arxiv.org/abs/2105.01294 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Hallucination_Improves_Few-Shot_Object_Detection_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Hallucination_Improves_Few-Shot_Object_Detection_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhang_Hallucination_Improves_Few-Shot_CVPR_2021_supplemental.pdf | null |
Efficient Conditional GAN Transfer With Knowledge Propagation Across Classes | Mohamad Shahbazi, Zhiwu Huang, Danda Pani Paudel, Ajad Chhatkuli, Luc Van Gool | Generative adversarial networks (GANs) have shown impressive results in both unconditional and conditional image generation. In recent literature, it is shown that pre-trained GANs, on a different dataset, can be transferred to improve the image generation from a small target data. The same, however, has not been well-studied in the case of conditional GANs (cGANs), which provides new opportunities for knowledge transfer compared to unconditional setup. In particular, the new classes may borrow knowledge from the related old classes, or share knowledge among themselves to improve the training. This motivates us to study the problem of efficient conditional GAN transfer with knowledge propagation across classes. To address this problem, we introduce a new GAN transfer method to explicitly propagate the knowledge from the old classes to the new classes. The key idea is to enforce the popularly used conditional batch normalization (BN) to learn the class-specific information of the new classes from that of the old classes, with implicit knowledge sharing among the new ones. This allows for an efficient knowledge propagation from the old classes to the new ones, with the BN parameters increasing linearly with the number of new classes. The extensive evaluation demonstrates the clear superiority of the proposed method over state-of-the-art competitors for efficient conditional GAN transfer tasks. The code is available at: https://github.com/mshahbazi72/cGANTransfer | https://openaccess.thecvf.com/content/CVPR2021/papers/Shahbazi_Efficient_Conditional_GAN_Transfer_With_Knowledge_Propagation_Across_Classes_CVPR_2021_paper.pdf | http://arxiv.org/abs/2102.06696 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Shahbazi_Efficient_Conditional_GAN_Transfer_With_Knowledge_Propagation_Across_Classes_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Shahbazi_Efficient_Conditional_GAN_Transfer_With_Knowledge_Propagation_Across_Classes_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Shahbazi_Efficient_Conditional_GAN_CVPR_2021_supplemental.pdf | null |
Fully Convolutional Scene Graph Generation | Hengyue Liu, Ning Yan, Masood Mortazavi, Bir Bhanu | This paper presents a fully convolutional scene graph generation (FCSGG) model that detects objects and relations simultaneously. Most of the scene graph generation frameworks use a pre-trained two-stage object detector, like Faster R-CNN, and build scene graphs using bounding box features. Such pipeline usually has a large number of parameters and low inference speed. Unlike these approaches, FCSGG is a conceptually elegant and efficient bottom-up approach that encodes objects as bounding box center points, and relationships as 2D vector fields which are named as Relation Affinity Fields (RAFs). RAFs encode both semantic and spatial features, and explicitly represent the relationship between a pair of objects by the integral on a sub-region that points from subject to object. FCSGG only utilizes visual features and still generates strong results for scene graph generation. Comprehensive experiments on the Visual Genome dataset demonstrate the efficacy, efficiency, and generalizability of the proposed method. FCSGG achieves highly competitive results on recall and zero-shot recall with significantly reduced inference time. | https://openaccess.thecvf.com/content/CVPR2021/papers/Liu_Fully_Convolutional_Scene_Graph_Generation_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.16083 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_Fully_Convolutional_Scene_Graph_Generation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_Fully_Convolutional_Scene_Graph_Generation_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Liu_Fully_Convolutional_Scene_CVPR_2021_supplemental.pdf | null |
Crossing Cuts Polygonal Puzzles: Models and Solvers | Peleg Harel, Ohad Ben-Shahar | Jigsaw puzzle solving, the problem of constructing a coherent whole from a set of non-overlapping unordered fragments, is fundamental to numerous applications, and yet most of the literature has focused thus far on less realistic puzzles whose pieces are identical squares. Here we formalize a new type of jigsaw puzzle where the pieces are general convex polygons generated by cutting through a global polygonal shape with an arbitrary number of straight cuts. We analyze the theoretical properties of such puzzles, including the inherent challenges in solving them once pieces are contaminated with geometrical noise. To cope with such difficulties and obtain tractable solutions, we abstract the problem as a multi-body spring-mass dynamical system endowed with hierarchical loop constraints and a layered reconstruction process that is guided by the pictorial content of the pieces. We define evaluation metrics and present experimental results on both apictorial and pictorial puzzles to indicate that they are solvable completely automatically. | https://openaccess.thecvf.com/content/CVPR2021/papers/Harel_Crossing_Cuts_Polygonal_Puzzles_Models_and_Solvers_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Harel_Crossing_Cuts_Polygonal_Puzzles_Models_and_Solvers_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Harel_Crossing_Cuts_Polygonal_Puzzles_Models_and_Solvers_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Harel_Crossing_Cuts_Polygonal_CVPR_2021_supplemental.zip | null |
Graph-Based High-Order Relation Modeling for Long-Term Action Recognition | Jiaming Zhou, Kun-Yu Lin, Haoxin Li, Wei-Shi Zheng | Long-term actions involve many important visual concepts, e.g., objects, motions, and sub-actions, and there are various relations among these concepts, which we call basic relations. These basic relations will jointly affect each other during the temporal evolution of long-term actions, which forms the high-order relations that are essential for long-term action recognition. In this paper, we propose a Graph-based High-order Relation Modeling (GHRM) module to exploit the high-order relations in the long-term actions for long-term action recognition. In GHRM, each basic relation in the long-term actions will be modeled by a graph, where each node represents a segment in a long video. Moreover, when modeling each basic relation, the information from all the other basic relations will be incorporated by GHRM, and thus the high-order relations in the long-term actions can be well exploited. To better exploit the high-order relations along the time dimension, we design a GHRM-layer consisting of a Temporal-GHRM branch and a Semantic-GHRM branch, which aims to model the local temporal high-order relations and global semantic high-order relations. The experimental results on three long-term action recognition datasets, namely, Breakfast, Charades, and MultiThumos, demonstrate the effectiveness of our model. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhou_Graph-Based_High-Order_Relation_Modeling_for_Long-Term_Action_Recognition_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhou_Graph-Based_High-Order_Relation_Modeling_for_Long-Term_Action_Recognition_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhou_Graph-Based_High-Order_Relation_Modeling_for_Long-Term_Action_Recognition_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhou_Graph-Based_High-Order_Relation_CVPR_2021_supplemental.pdf | null |
Positive-Unlabeled Data Purification in the Wild for Object Detection | Jianyuan Guo, Kai Han, Han Wu, Chao Zhang, Xinghao Chen, Chunjing Xu, Chang Xu, Yunhe Wang | Deep learning based object detection approaches have achieved great progress with the benefit from large amount of labeled images. However, image annotation remains a laborious, time-consuming and error-prone process. To further improve the performance of detectors, we seek to exploit all available labeled data and excavate useful samples from massive unlabeled images in the wild, which is rarely discussed before. In this paper, we present a positive-unlabeled learning based scheme to expand training data by purifying valuable images from massive unlabeled ones, where the original training data are viewed as positive data and the unlabeled images in the wild are unlabeled data. To effectively utilized these purified data, we propose a self-distillation algorithm based on hint learning and ground truth bounded knowledge distillation. Experimental results verify that the proposed positive-unlabeled data purification can strengthen the original detector by mining the massive unlabeled data. In particular, our method boosts the mAP of FPN by +2.0% on COCO benchmark. | https://openaccess.thecvf.com/content/CVPR2021/papers/Guo_Positive-Unlabeled_Data_Purification_in_the_Wild_for_Object_Detection_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Guo_Positive-Unlabeled_Data_Purification_in_the_Wild_for_Object_Detection_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Guo_Positive-Unlabeled_Data_Purification_in_the_Wild_for_Object_Detection_CVPR_2021_paper.html | CVPR 2021 | null | null |
ArtFlow: Unbiased Image Style Transfer via Reversible Neural Flows | Jie An, Siyu Huang, Yibing Song, Dejing Dou, Wei Liu, Jiebo Luo | Universal style transfer retains styles from reference images in content images. While existing methods have achieved state-of-the-art style transfer performance, they are not aware of the content leak phenomenon that the image content may corrupt after several rounds of stylization process. In this paper, we propose ArtFlow to prevent content leak during universal style transfer. ArtFlow consists of reversible neural flows and an unbiased feature transfer module. It supports both forward and backward inferences and operates in a projection-transfer-reversion scheme. The forward inference projects input images into deep features, while the backward inference remaps deep features back to input images in a lossless and unbiased way. Extensive experiments demonstrate that ArtFlow achieves comparable performance to state-of-the-art style transfer methods while avoiding content leak. | https://openaccess.thecvf.com/content/CVPR2021/papers/An_ArtFlow_Unbiased_Image_Style_Transfer_via_Reversible_Neural_Flows_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.16877 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/An_ArtFlow_Unbiased_Image_Style_Transfer_via_Reversible_Neural_Flows_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/An_ArtFlow_Unbiased_Image_Style_Transfer_via_Reversible_Neural_Flows_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/An_ArtFlow_Unbiased_Image_CVPR_2021_supplemental.pdf | null |
Network Quantization With Element-Wise Gradient Scaling | Junghyup Lee, Dohyung Kim, Bumsub Ham | Network quantization aims at reducing bit-widths of weights and/or activations, particularly important for implementing deep neural networks with limited hardware resources. Most methods use the straight-through estimator (STE) to train quantized networks, which avoids a zero-gradient problem by replacing a derivative of a discretizer (i.e., a round function) with that of an identity function. Although quantized networks exploiting the STE have shown decent performance, the STE is sub-optimal in that it simply propagates the same gradient without considering discretization errors between inputs and outputs of the discretizer. In this paper, we propose an element-wise gradient scaling (EWGS), a simple yet effective alternative to the STE, training a quantized network better than the STE in terms of stability and accuracy. Given a gradient of the discretizer output, EWGS adaptively scales up or down each gradient element, and uses the scaled gradient as the one for the discretizer input to train quantized networks via backpropagation. The scaling is performed depending on both the sign of each gradient element and an error between the continuous input and discrete output of the discretizer. We adjust a scaling factor adaptively using Hessian information of a network. We show extensive experimental results on the image classification datasets, including CIFAR-10 and ImageNet, with diverse network architectures under a wide range of bit-width settings, demonstrating the effectiveness of our method. | https://openaccess.thecvf.com/content/CVPR2021/papers/Lee_Network_Quantization_With_Element-Wise_Gradient_Scaling_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.00903 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Lee_Network_Quantization_With_Element-Wise_Gradient_Scaling_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Lee_Network_Quantization_With_Element-Wise_Gradient_Scaling_CVPR_2021_paper.html | CVPR 2021 | null | null |
img2pose: Face Alignment and Detection via 6DoF, Face Pose Estimation | Vitor Albiero, Xingyu Chen, Xi Yin, Guan Pang, Tal Hassner | We propose real-time, six degrees of freedom (6DoF), 3D face pose estimation without face detection or landmark localization. We observe that estimating the 6DoF rigid transformation of a face is a simpler problem than facial landmark detection, often used for 3D face alignment. In addition, 6DoF offers more information than face bounding box labels. We leverage these observations to make multiple contributions: (a) We describe an easily trained, efficient, Faster R-CNN-based model which regresses 6DoF pose for all faces in the photo, without preliminary face detection. (b) We explain how pose is converted and kept consistent between the input photo and arbitrary crops created while training and evaluating our model. (c) Finally, we show how face poses can replace detection bounding box training labels. Tests on AFLW2000-3D and BIWI show that our method runs at real-time and outperforms state of the art (SotA) face pose estimators. Remarkably, our method also surpasses SotA models of comparable complexity on the WIDER FACE detection benchmark, despite not been optimized on bounding box labels. | https://openaccess.thecvf.com/content/CVPR2021/papers/Albiero_img2pose_Face_Alignment_and_Detection_via_6DoF_Face_Pose_Estimation_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.07791 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Albiero_img2pose_Face_Alignment_and_Detection_via_6DoF_Face_Pose_Estimation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Albiero_img2pose_Face_Alignment_and_Detection_via_6DoF_Face_Pose_Estimation_CVPR_2021_paper.html | CVPR 2021 | null | null |
Sparse Multi-Path Corrections in Fringe Projection Profilometry | Yu Zhang, Daniel Lau, David Wipf | Three-dimensional scanning by means of structured light illumination is an active imaging technique involving projecting and capturing a series of striped patterns and then using the observed warping of stripes to reconstruct the target object's surface through triangulating each pixel in the camera to a unique projector coordinate corresponding to a particular feature in the projected patterns. The undesirable phenomenon of multi-path occurs when a camera pixel simultaneously sees features from multiple projector coordinates. Bimodal multi-path is a particularly common situation found along step edges, where the camera pixel sees both a foreground and background surface. Generalized from bimodal multi-path, this paper looks at sparse or N modal multi-path as a more general case, where the camera pixel sees no less than two reflective surfaces, resulting in decoding errors. Using fringe projection profilometry, our proposed solution is to treat each camera pixel as an underdetermined linear system of equations and to find the sparsest (least number of paths) solution using an application-specific Bayesian learning approach. We validate this algorithm with both simulations and a number of challenging real-world scenarios, outperforming the state-of-the-art techniques. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_Sparse_Multi-Path_Corrections_in_Fringe_Projection_Profilometry_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Sparse_Multi-Path_Corrections_in_Fringe_Projection_Profilometry_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Sparse_Multi-Path_Corrections_in_Fringe_Projection_Profilometry_CVPR_2021_paper.html | CVPR 2021 | null | null |
NeuroMorph: Unsupervised Shape Interpolation and Correspondence in One Go | Marvin Eisenberger, David Novotny, Gael Kerchenbaum, Patrick Labatut, Natalia Neverova, Daniel Cremers, Andrea Vedaldi | We present NeuroMorph, a new neural network architecture that takes as input two 3D shapes and produces in one go, i.e. in a single feed forward pass, a smooth interpolation and point-to-point correspondences between them. The interpolation, expressed as a deformation field, changes the pose of the source shape to resemble the target, but leaves the object identity unchanged. NeuroMorph uses an elegant architecture combining graph convolutions with global feature pooling to extract local features. During training, the model is incentivized to create realistic deformations by approximating geodesics on the underlying shape space manifold. This strong geometric prior allows to train our model end-to-end and in a fully unsupervised manner without requiring any manual correspondence annotations. NeuroMorph works well for a large variety of input shapes, including non-isometric pairs from different object categories. It obtains state-of-the-art results for both shape correspondence and interpolation tasks, matching or surpassing the performance of recent unsupervised and supervised methods on multiple benchmarks. | https://openaccess.thecvf.com/content/CVPR2021/papers/Eisenberger_NeuroMorph_Unsupervised_Shape_Interpolation_and_Correspondence_in_One_Go_CVPR_2021_paper.pdf | http://arxiv.org/abs/2106.09431 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Eisenberger_NeuroMorph_Unsupervised_Shape_Interpolation_and_Correspondence_in_One_Go_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Eisenberger_NeuroMorph_Unsupervised_Shape_Interpolation_and_Correspondence_in_One_Go_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Eisenberger_NeuroMorph_Unsupervised_Shape_CVPR_2021_supplemental.pdf | null |
Soft-IntroVAE: Analyzing and Improving the Introspective Variational Autoencoder | Tal Daniel, Aviv Tamar | The recently introduced introspective variational autoencoder (IntroVAE) exhibits outstanding image generations, and allows for amortized inference using an image encoder. The main idea in IntroVAE is to train a VAE adversarially, using the VAE encoder to discriminate between generated and real data samples. However, the original IntroVAE loss function relied on a particular hinge-loss formulation that is very hard to stabilize in practice, and its theoretical convergence analysis ignored important terms in the loss. In this work, we take a step towards better understanding of the IntroVAE model, its practical implementation, and its applications. We propose the Soft-IntroVAE, a modified IntroVAE that replaces the hinge-loss terms with a smooth exponential loss on generated samples. This change significantly improves training stability, and also enables theoretical analysis of the complete algorithm. Interestingly, we show that the IntroVAE converges to a distribution that minimizes a sum of KL distance from the data distribution and an entropy term. We discuss the implications of this result, and demonstrate that it induces competitive image generation and reconstruction. Finally, we describe an application of Soft-IntroVAE to unsupervised image translation, and demonstrate compelling results. Code and additional information is available on the project website - taldatech.github.io/soft-intro-vae-web | https://openaccess.thecvf.com/content/CVPR2021/papers/Daniel_Soft-IntroVAE_Analyzing_and_Improving_the_Introspective_Variational_Autoencoder_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Daniel_Soft-IntroVAE_Analyzing_and_Improving_the_Introspective_Variational_Autoencoder_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Daniel_Soft-IntroVAE_Analyzing_and_Improving_the_Introspective_Variational_Autoencoder_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Daniel_Soft-IntroVAE_Analyzing_and_CVPR_2021_supplemental.zip | null |
Energy-Based Learning for Scene Graph Generation | Mohammed Suhail, Abhay Mittal, Behjat Siddiquie, Chris Broaddus, Jayan Eledath, Gerard Medioni, Leonid Sigal | Traditional scene graph generation methods are trained using cross-entropy losses that treat objects and relationships as independent entities. Such a formulation, however, ignores structure in the output space, in an inherently structured prediction problem. In this work, we introduce a novel energy-based learning framework for generating scene graphs. The proposed formulation allows for efficiently incorporating the structure of scene graphs in the output space. This additional constraint in the learning framework acts as an inductive bias and allows models to learn efficiently from a small number of labels. We use the proposed energy-based framework to train existing state-of-the-art models and show a significant performance improvement, of up to 21% and 27%, on the Visual Genome and GQA benchmark datasets, respectively. Further, we showcase the learning efficiency of the proposed framework by demonstrating superior performance in the zero- and few-shot settings where data is scarce. | https://openaccess.thecvf.com/content/CVPR2021/papers/Suhail_Energy-Based_Learning_for_Scene_Graph_Generation_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.02221 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Suhail_Energy-Based_Learning_for_Scene_Graph_Generation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Suhail_Energy-Based_Learning_for_Scene_Graph_Generation_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Suhail_Energy-Based_Learning_for_CVPR_2021_supplemental.pdf | null |
Zillow Indoor Dataset: Annotated Floor Plans With 360deg Panoramas and 3D Room Layouts | Steve Cruz, Will Hutchcroft, Yuguang Li, Naji Khosravan, Ivaylo Boyadzhiev, Sing Bing Kang | We present Zillow Indoor Dataset (ZInD): A large indoor dataset with 71,474 panoramas from 1,524 real unfurnished homes. ZInD provides annotations of 3D room layouts, 2D and 3D floor plans, panorama location in the floor plan, and locations of windows and doors. The ground truth construction took over 1,500 hours of annotation work. To the best of our knowledge, ZInD is the largest real dataset with layout annotations. A unique property is the room layout data, which follows a real world distribution (cuboid, more general Manhattan, and non-Manhattan layouts) as opposed to the mostly cuboid or Manhattan layouts in current publicly available datasets. Also, the scale and annotations provided are valuable for effective research related to room layout and floor plan analysis. To demonstrate ZInD's benefits, we benchmark on room layout estimation from single panoramas and multi-view registration. | https://openaccess.thecvf.com/content/CVPR2021/papers/Cruz_Zillow_Indoor_Dataset_Annotated_Floor_Plans_With_360deg_Panoramas_and_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Cruz_Zillow_Indoor_Dataset_Annotated_Floor_Plans_With_360deg_Panoramas_and_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Cruz_Zillow_Indoor_Dataset_Annotated_Floor_Plans_With_360deg_Panoramas_and_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Cruz_Zillow_Indoor_Dataset_CVPR_2021_supplemental.pdf | null |
Progressive Contour Regression for Arbitrary-Shape Scene Text Detection | Pengwen Dai, Sanyi Zhang, Hua Zhang, Xiaochun Cao | State-of-the-art scene text detection methods usually model the text instance with local pixels or components from the bottom-up perspective and, therefore, are sensitive to noises and dependent on the complicated heuristic post-processing especially for arbitrary-shape texts. To relieve these two issues, instead, we propose to progressively evolve the initial text proposal to arbitrarily shaped text contours in a top-down manner. The initial horizontal text proposals are generated by estimating the center and size of texts. To reduce the range of regression, the first stage of the evolution predicts the corner points of oriented text proposals from the initial horizontal ones. In the second stage, the contours of the oriented text proposals are iteratively regressed to arbitrarily shaped ones. In the last iteration of this stage, we rescore the confidence of the final localized text by utilizing the cues from multiple contour points, rather than the single cue from the initial horizontal proposal center that may be out of arbitrary-shape text regions. Moreover, to facilitate the progressive contour evolution, we design a contour information aggregation mechanism to enrich the feature representation on text contours by considering both the circular topology and semantic context. Experiments conducted on CTW1500, Total-Text, ArT, and TD500 have demonstrated that the proposed method especially excels in line-level arbitrary-shape texts. Code is available at http://github.com/dpengwen/PCR. | https://openaccess.thecvf.com/content/CVPR2021/papers/Dai_Progressive_Contour_Regression_for_Arbitrary-Shape_Scene_Text_Detection_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Dai_Progressive_Contour_Regression_for_Arbitrary-Shape_Scene_Text_Detection_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Dai_Progressive_Contour_Regression_for_Arbitrary-Shape_Scene_Text_Detection_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Dai_Progressive_Contour_Regression_CVPR_2021_supplemental.pdf | null |
UV-Net: Learning From Boundary Representations | Pradeep Kumar Jayaraman, Aditya Sanghi, Joseph G. Lambourne, Karl D.D. Willis, Thomas Davies, Hooman Shayani, Nigel Morris | We introduce UV-Net, a novel neural network architecture and representation designed to operate directly on Boundary representation (B-rep) data from 3D CAD models. The B-rep format is widely used in the design, simulation and manufacturing industries to enable sophisticated and precise CAD modeling operations. However, B-rep data presents some unique challenges when used with modern machine learning due to the complexity of the data structure and its support for both continuous non-Euclidean geometric entities and discrete topological entities. In this paper, we propose a unified representation for B-rep data that exploits the U and V parameter domain of curves and surfaces to model geometry, and an adjacency graph to explicitly model topology. This leads to a unique and efficient network architecture, UV-Net, that couples image and graph convolutional neural networks in a compute and memory-efficient manner. To aid in future research we present a synthetic labelled B-rep dataset, SolidLetters, derived from human designed fonts with variations in both geometry and topology. Finally we demonstrate that UV-Net can generalize to supervised and unsupervised tasks on five datasets, while outperforming alternate 3D shape representations such as point clouds, voxels, and meshes. | https://openaccess.thecvf.com/content/CVPR2021/papers/Jayaraman_UV-Net_Learning_From_Boundary_Representations_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Jayaraman_UV-Net_Learning_From_Boundary_Representations_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Jayaraman_UV-Net_Learning_From_Boundary_Representations_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Jayaraman_UV-Net_Learning_From_CVPR_2021_supplemental.pdf | null |
MAZE: Data-Free Model Stealing Attack Using Zeroth-Order Gradient Estimation | Sanjay Kariyappa, Atul Prakash, Moinuddin K Qureshi | High quality Machine Learning (ML) models are often considered valuable intellectual property by companies. Model Stealing (MS) attacks allow an adversary with black-box access to a ML model to replicate its functionality by training a clone model using the predictions of the target model for different inputs. However, best available existing MS attacks fail to produce a high-accuracy clone without access to the target dataset or a representative dataset necessary to query the target model. In this paper, we show that preventing access to the target dataset is not an adequate defense to protect a model. We propose MAZE -- a data-free model stealing attack using zeroth-order gradient estimation that produces high-accuracy clones. In contrast to prior works, MAZE uses only synthetic data created using a generative model to perform MS. Our evaluation with four image classification models shows that MAZE provides a normalized clone accuracy in the range of 0.90x to 0.99x, and outperforms even the recent attacks that rely on partial data (JBDA, clone accuracy 0.13x to 0.69x) and on surrogate data (KnockoffNets, clone accuracy 0.52x to 0.97x). We also study an extension of MAZE in the partial-data setting and develop MAZE-PD, which generates synthetic data closer to the target distribution. MAZE-PD further improves the clone accuracy 0.97x to 1.0x) and reduces the query budget required for the attack by 2x-24x. | https://openaccess.thecvf.com/content/CVPR2021/papers/Kariyappa_MAZE_Data-Free_Model_Stealing_Attack_Using_Zeroth-Order_Gradient_Estimation_CVPR_2021_paper.pdf | http://arxiv.org/abs/2005.03161 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Kariyappa_MAZE_Data-Free_Model_Stealing_Attack_Using_Zeroth-Order_Gradient_Estimation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Kariyappa_MAZE_Data-Free_Model_Stealing_Attack_Using_Zeroth-Order_Gradient_Estimation_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Kariyappa_MAZE_Data-Free_Model_CVPR_2021_supplemental.pdf | null |
Universal Spectral Adversarial Attacks for Deformable Shapes | Arianna Rampini, Franco Pestarini, Luca Cosmo, Simone Melzi, Emanuele Rodola | Machine learning models are known to be vulnerable to adversarial attacks, namely perturbations of the data that lead to wrong predictions despite being imperceptible. However, the existence of "universal" attacks (i.e., unique perturbations that transfer across different data points) has only been demonstrated for images to date. Part of the reason lies in the lack of a common domain, for geometric data such as graphs, meshes, and point clouds, where a universal perturbation can be defined. In this paper, we offer a change in perspective and demonstrate the existence of universal attacks for geometric data (shapes). We introduce a computational procedure that operates entirely in the spectral domain, where the attacks take the form of small perturbations to short eigenvalue sequences; the resulting geometry is then synthesized via shape-from-spectrum recovery. Our attacks are universal, in that they transfer across different shapes, different representations (meshes and point clouds), and generalize to previously unseen data. | https://openaccess.thecvf.com/content/CVPR2021/papers/Rampini_Universal_Spectral_Adversarial_Attacks_for_Deformable_Shapes_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.03356 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Rampini_Universal_Spectral_Adversarial_Attacks_for_Deformable_Shapes_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Rampini_Universal_Spectral_Adversarial_Attacks_for_Deformable_Shapes_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Rampini_Universal_Spectral_Adversarial_CVPR_2021_supplemental.pdf | null |
Prototypical Cross-Domain Self-Supervised Learning for Few-Shot Unsupervised Domain Adaptation | Xiangyu Yue, Zangwei Zheng, Shanghang Zhang, Yang Gao, Trevor Darrell, Kurt Keutzer, Alberto Sangiovanni Vincentelli | Unsupervised Domain Adaptation (UDA) transfers predictive models from a fully-labeled source domain to an unlabeled target domain. In some applications, however, it is expensive even to collect labels in the source domain, making most previous works impractical. To cope with this problem, recent work performed instance-wise cross-domain self-supervised learning, followed by an additional fine-tuning stage. However, the instance-wise self-supervised learning only learns and aligns low-level discriminative features. In this paper, we propose an end-to-end Prototypical Cross-domain Self-Supervised Learning (PCS) framework for Few-shot Unsupervised Domain Adaptation (FUDA). PCS not only performs cross-domain low-level feature alignment, but it also encodes and aligns semantic structures in the shared embedding space across domains. Our framework captures category-wise semantic structures of the data by in-domain prototypical contrastive learning; and performs feature alignment through cross-domain prototypical self-supervision. Compared with state-of-the-art methods, PCS improves the mean classification accuracy over different domain pairs on FUDA by 10.5%, 3.5%, 9.0%, and 13.2% on Office, Office-Home, VisDA-2017, and DomainNet, respectively. | https://openaccess.thecvf.com/content/CVPR2021/papers/Yue_Prototypical_Cross-Domain_Self-Supervised_Learning_for_Few-Shot_Unsupervised_Domain_Adaptation_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.16765 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Yue_Prototypical_Cross-Domain_Self-Supervised_Learning_for_Few-Shot_Unsupervised_Domain_Adaptation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Yue_Prototypical_Cross-Domain_Self-Supervised_Learning_for_Few-Shot_Unsupervised_Domain_Adaptation_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yue_Prototypical_Cross-Domain_Self-Supervised_CVPR_2021_supplemental.pdf | null |
HybrIK: A Hybrid Analytical-Neural Inverse Kinematics Solution for 3D Human Pose and Shape Estimation | Jiefeng Li, Chao Xu, Zhicun Chen, Siyuan Bian, Lixin Yang, Cewu Lu | Model-based 3D pose and shape estimation methods reconstruct a full 3D mesh for the human body by estimating several parameters. However, learning the abstract parameters is a highly non-linear process and suffers from image-model misalignment, leading to mediocre model performance. In contrast, 3D keypoint estimation methods combine deep CNN network with the volumetric representation to achieve pixel-level localization accuracy but may predict unrealistic body structure. In this paper, we address the above issues by bridging the gap between body mesh estimation and 3D keypoint estimation. We propose a novel hybrid inverse kinematics solution (HybrIK). HybrIK directly transforms accurate 3D joints to relative body-part rotations for 3D body mesh reconstruction, via the twist-and-swing decomposition. The swing rotation is analytically solved with 3D joints, and the twist rotation is derived from the visual cues through the neural network. We show that HybrIK preserves both the accuracy of 3D pose and the realistic body structure of the parametric human model, leading to a pixel-aligned 3D body mesh and a more accurate 3D pose than the pure 3D keypoint estimation methods. Without bells and whistles, the proposed method surpasses the state-of-the-art methods by a large margin on various 3D human pose and shape benchmarks. As an illustrative example, HybrIK outperforms all the previous methods by 13.2 mm MPJPE and 21.9 mm PVE on 3DPW dataset. Our code is available at https://github.com/Jeff-sjtu/HybrIK. | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_HybrIK_A_Hybrid_Analytical-Neural_Inverse_Kinematics_Solution_for_3D_Human_CVPR_2021_paper.pdf | http://arxiv.org/abs/2011.14672 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Li_HybrIK_A_Hybrid_Analytical-Neural_Inverse_Kinematics_Solution_for_3D_Human_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Li_HybrIK_A_Hybrid_Analytical-Neural_Inverse_Kinematics_Solution_for_3D_Human_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Li_HybrIK_A_Hybrid_CVPR_2021_supplemental.zip | null |
Human De-Occlusion: Invisible Perception and Recovery for Humans | Qiang Zhou, Shiyin Wang, Yitong Wang, Zilong Huang, Xinggang Wang | In this paper, we tackle the problem of human de-occlusion which reasons about occluded segmentation masks and invisible appearance content of humans. In particular, a two-stage framework is proposed to estimate the invisible portions and recover the content inside. For the stage of mask completion, a stacked network structure is devised to refine inaccurate masks from a general instance segmentation model and predict integrated masks simultaneously. Additionally, the guidance from human parsing and typical pose masks are leveraged to bring prior information. For the stage of content recovery, a novel parsing guided attention module is applied to isolate body parts and capture context information across multiple scales. Besides, an Amodal Human Perception dataset (AHP) is collected to settle the task of human de-occlusion. AHP has advantages of providing annotations from real-world scenes and the number of humans is comparatively larger than other amodal perception datasets. Based on this dataset, experiments demonstrate that our method performs over the state-of-the-art techniques in both tasks of mask completion and content recovery. Our AHP dataset is available at https://sydney0zq.github.io/ahp/. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhou_Human_De-Occlusion_Invisible_Perception_and_Recovery_for_Humans_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhou_Human_De-Occlusion_Invisible_Perception_and_Recovery_for_Humans_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhou_Human_De-Occlusion_Invisible_Perception_and_Recovery_for_Humans_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhou_Human_De-Occlusion_Invisible_CVPR_2021_supplemental.pdf | null |
The Neural Tangent Link Between CNN Denoisers and Non-Local Filters | Julian Tachella, Junqi Tang, Mike Davies | Convolutional Neural Networks (CNNs) are now a well-established tool for solving computational imaging problems. Modern CNN-based algorithms obtain state-of-the-art performance in diverse image restoration problems. Furthermore, it has been recently shown that, despite being highly overparameterized, networks trained with a single corrupted image can still perform as well as fully trained networks. We introduce a formal link between such networks through their neural tangent kernel (NTK), and well-known non-local filtering techniques, such as non-local means or BM3D. The filtering function associated with a given network architecture can be obtained in closed form without need to train the network, being fully characterized by the random initialization of the network weights. While the NTK theory accurately predicts the filter associated with networks trained using standard gradient descent, our analysis shows that it falls short to explain the behaviour of networks trained using the popular Adam optimizer. The latter achieves a larger change of weights in hidden layers, adapting the non-local filtering function during training. We evaluate our findings via extensive image denoising experiments. | https://openaccess.thecvf.com/content/CVPR2021/papers/Tachella_The_Neural_Tangent_Link_Between_CNN_Denoisers_and_Non-Local_Filters_CVPR_2021_paper.pdf | http://arxiv.org/abs/2006.02379 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Tachella_The_Neural_Tangent_Link_Between_CNN_Denoisers_and_Non-Local_Filters_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Tachella_The_Neural_Tangent_Link_Between_CNN_Denoisers_and_Non-Local_Filters_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Tachella_The_Neural_Tangent_CVPR_2021_supplemental.pdf | null |
Achieving Robustness in Classification Using Optimal Transport With Hinge Regularization | Mathieu Serrurier, Franck Mamalet, Alberto Gonzalez-Sanz, Thibaut Boissin, Jean-Michel Loubes, Eustasio del Barrio | Adversarial examples have pointed out Deep Neural Network's vulnerability to small local noise. It has been shown that constraining their Lipschitz constant should enhance robustness, but make them harder to learn with classical loss functions. We propose a new framework for binary classification, based on optimal transport, which integrates this Lipschitz constraint as a theoretical requirement. We propose to learn 1-Lipschitz networks using a new loss that is an hinge regularized version of the Kantorovich-Rubinstein dual formulation for the Wasserstein distance estimation. This loss function has a direct interpretation in terms of adversarial robustness together with certifiable robustness bound. We also prove that this hinge regularized version is still the dual formulation of an optimal transportation problem, and has a solution. We also establish several geometrical properties of this optimal solution, and extend the approach to multi-class problems. Experiments show that the proposed approach provides the expected guarantees in terms of robustness without any significant accuracy drop. The adversarial examples, on the proposed models, visibly and meaningfully change the input providing an explanation for the classification. | https://openaccess.thecvf.com/content/CVPR2021/papers/Serrurier_Achieving_Robustness_in_Classification_Using_Optimal_Transport_With_Hinge_Regularization_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Serrurier_Achieving_Robustness_in_Classification_Using_Optimal_Transport_With_Hinge_Regularization_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Serrurier_Achieving_Robustness_in_Classification_Using_Optimal_Transport_With_Hinge_Regularization_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Serrurier_Achieving_Robustness_in_CVPR_2021_supplemental.pdf | null |
Stochastic Image-to-Video Synthesis Using cINNs | Michael Dorkenwald, Timo Milbich, Andreas Blattmann, Robin Rombach, Konstantinos G. Derpanis, Bjorn Ommer | Video understanding calls for a model to learn the characteristic interplay between static scene content and its dynamics: Given an image, the model must be able to predict a future progression of the portrayed scene and, conversely, a video should be explained in terms of its static image content and all the remaining characteristics not present in the initial frame. This naturally suggests a bijective mapping between the video domain and the static content as well as residual information. In contrast to common stochastic image-to-video synthesis, such a model does not merely generate arbitrary videos progressing the initial image. Given this image, it rather provides a one-to-one mapping between the residual vectors and the video with stochastic outcomes when sampling. The approach is naturally implemented using a conditional invertible neural network (cINN) that can explain videos by independently modelling static and other video characteristics, thus laying the basis for controlled video synthesis. Experiments on diverse video datasets demonstrate the effectiveness of our approach in terms of both the quality and diversity of the synthesized results. Our project page is available at https://bit.ly/3dg90fV. | https://openaccess.thecvf.com/content/CVPR2021/papers/Dorkenwald_Stochastic_Image-to-Video_Synthesis_Using_cINNs_CVPR_2021_paper.pdf | http://arxiv.org/abs/2105.04551 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Dorkenwald_Stochastic_Image-to-Video_Synthesis_Using_cINNs_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Dorkenwald_Stochastic_Image-to-Video_Synthesis_Using_cINNs_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Dorkenwald_Stochastic_Image-to-Video_Synthesis_CVPR_2021_supplemental.pdf | null |
Ego-Exo: Transferring Visual Representations From Third-Person to First-Person Videos | Yanghao Li, Tushar Nagarajan, Bo Xiong, Kristen Grauman | We introduce an approach for pre-training egocentric video models using large-scale third-person video datasets. Learning from purely egocentric data is limited by low dataset scale and diversity, while using purely exocentric (third-person) data introduces a large domain mismatch. Our idea is to discover latent signals in third-person video that are predictive of key egocentric-specific properties. Incorporating these signals as knowledge distillation losses during pre-training results in models that benefit from both the scale and diversity of third-person video data, as well as representations that capture salient egocentric properties. Our experiments show that our Ego-Exo framework can be seamlessly integrated into standard video models; it outperforms all baselines when fine-tuned for egocentric activity recognition, achieving state-of-the-art results on Charades-Ego and EPIC-Kitchens-100. | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_Ego-Exo_Transferring_Visual_Representations_From_Third-Person_to_First-Person_Videos_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Ego-Exo_Transferring_Visual_Representations_From_Third-Person_to_First-Person_Videos_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Ego-Exo_Transferring_Visual_Representations_From_Third-Person_to_First-Person_Videos_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Li_Ego-Exo_Transferring_Visual_CVPR_2021_supplemental.zip | null |
Dynamic Slimmable Network | Changlin Li, Guangrun Wang, Bing Wang, Xiaodan Liang, Zhihui Li, Xiaojun Chang | Current dynamic networks and dynamic pruning methods have shown their promising capability in reducing theoretical computation complexity. However, dynamic sparse patterns on convolutional filters fail to achieve actual acceleration in real-world implementation, due to the extra burden of indexing, weight-copying, or zero-masking. Here, we explore a dynamic network slimming regime, named Dynamic Slimmable Network (DS-Net), which aims to achieve good hardware-efficiency via dynamically adjusting filter numbers of networks at test time with respect to different inputs, while keeping filters stored statically and contiguously in hardware to prevent the extra burden. Our DS-Net is empowered with the ability of dynamic inference by the proposed double-headed dynamic gate that comprises an attention head and a slimming head to predictively adjust network width with negligible extra computation cost. To ensure generality of each candidate architecture and the fairness of gate, we propose a disentangled two-stage training scheme inspired by one-shot NAS. In the first stage, a novel training technique for weight-sharing networks named In-place Ensemble Bootstrapping is proposed to improve the supernet training efficacy. In the second stage, Sandwich Gate Sparsification is proposed to assist the gate training by identifying easy and hard samples in an online way. Extensive experiments demonstrate our DS-Net consistently outperforms its static counterparts as well as state-of-the-art static and dynamic model compression methods by a large margin (up to 5.9%). Typically, DS-Net achieves 2-4x computation reduction and 1.62x real-world acceleration over ResNet-50 and MobileNet with minimal accuracy drops on ImageNet. | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_Dynamic_Slimmable_Network_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.13258 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Dynamic_Slimmable_Network_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Dynamic_Slimmable_Network_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Li_Dynamic_Slimmable_Network_CVPR_2021_supplemental.pdf | null |
Jo-SRC: A Contrastive Approach for Combating Noisy Labels | Yazhou Yao, Zeren Sun, Chuanyi Zhang, Fumin Shen, Qi Wu, Jian Zhang, Zhenmin Tang | Due to the memorization effect in Deep Neural Networks (DNNs), training with noisy labels usually results in inferior model performance. Existing state-of-the-art methods primarily adopt a sample selection strategy, which selects small-loss samples for subsequent training. However, prior literature tends to perform sample selection within each mini-batch, neglecting the imbalance of noise ratios in different mini-batches. Moreover, valuable knowledge within high-loss samples is wasted. To this end, we propose a noise-robust approach named Jo-SRC (Joint Sample Selection and Model Regularization based on Consistency). Specifically, we train the network in a contrastive learning manner. Predictions from two different views of each sample are used to estimate its "likelihood" of being clean or out-of-distribution. Furthermore, we propose a joint loss to advance the model generalization performance by introducing consistency regularization. Extensive experiments and ablation studies have validated the superiority of our approach over existing state-of-the-art methods. | https://openaccess.thecvf.com/content/CVPR2021/papers/Yao_Jo-SRC_A_Contrastive_Approach_for_Combating_Noisy_Labels_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Yao_Jo-SRC_A_Contrastive_Approach_for_Combating_Noisy_Labels_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Yao_Jo-SRC_A_Contrastive_Approach_for_Combating_Noisy_Labels_CVPR_2021_paper.html | CVPR 2021 | null | null |
Deep Lucas-Kanade Homography for Multimodal Image Alignment | Yiming Zhao, Xinming Huang, Ziming Zhang | Estimating homography to align image pairs captured by different sensors or image pairs with large appearance changes is an important and general challenge for many computer vision applications. In contrast to others, we propose a generic solution to pixel-wise align multimodal image pairs by extending the traditional Lucas-Kanade algorithm with networks. The key contribution in our method is how we construct feature maps, named as deep Lucas-Kanade feature map (DLKFM). The learned DLKFM can spontaneously recognize invariant features under various appearance-changing conditions. It also has two nice properties for the Lucas-Kanade algorithm: (1) The template feature map keeps brightness consistency with the input feature map, thus the color difference is very small while they are well-aligned. (2) The Lucas-Kanade objective function built on DLKFM has a smooth landscape around ground truth homography parameters, so the iterative solution of the Lucas-Kanade can easily converge to the ground truth. With those properties, directly updating the Lucas-Kanade algorithm on our feature maps will precisely align image pairs with large appearance changes. We share the dataset, code, and demo video online. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhao_Deep_Lucas-Kanade_Homography_for_Multimodal_Image_Alignment_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.11693 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhao_Deep_Lucas-Kanade_Homography_for_Multimodal_Image_Alignment_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhao_Deep_Lucas-Kanade_Homography_for_Multimodal_Image_Alignment_CVPR_2021_paper.html | CVPR 2021 | null | null |
clDice - A Novel Topology-Preserving Loss Function for Tubular Structure Segmentation | Suprosanna Shit, Johannes C. Paetzold, Anjany Sekuboyina, Ivan Ezhov, Alexander Unger, Andrey Zhylka, Josien P. W. Pluim, Ulrich Bauer, Bjoern H. Menze | Accurate segmentation of tubular, network-like structures, such as vessels, neurons, or roads, is relevant to many fields of research. For such structures, the topology is their most important characteristic; particularly preserving connectedness: in the case of vascular networks, missing a connected vessel entirely alters the blood-flow dynamics. We introduce a novel similarity measure termed centerlineDice (short clDice), which is calculated on the intersection of the segmentation masks and their (morphological) skeleta. We theoretically prove that clDice guarantees topology preservation up to homotopy equivalence for binary 2D and 3D segmentation. Extending this, we propose a computationally efficient, differentiable loss function (soft-clDice) for training arbitrary neural segmentation networks. We benchmark the soft-clDice loss on five public datasets, including vessels, roads and neurons (2D and 3D). Training on soft-clDice leads to segmentation with more accurate connectivity information, higher graph similarity, and better volumetric scores. | https://openaccess.thecvf.com/content/CVPR2021/papers/Shit_clDice_-_A_Novel_Topology-Preserving_Loss_Function_for_Tubular_Structure_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Shit_clDice_-_A_Novel_Topology-Preserving_Loss_Function_for_Tubular_Structure_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Shit_clDice_-_A_Novel_Topology-Preserving_Loss_Function_for_Tubular_Structure_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Shit_clDice_-_A_CVPR_2021_supplemental.pdf | null |
Hyper-LifelongGAN: Scalable Lifelong Learning for Image Conditioned Generation | Mengyao Zhai, Lei Chen, Greg Mori | Deep neural networks are susceptible to catastrophic forgetting: when encountering a new task, they can only remember the new task and fail to preserve its ability to accomplish previously learned tasks. In this paper, we study the problem of lifelong learning for generative models and propose a novel and generic continual learning framework Hyper-LifelongGAN which is more scalable compared with state-of-the-art approaches. Given a sequence of tasks, the conventional convolutional filters are factorized into the dynamic base filters which are generated using task specific filter generators, and deterministic weight matrix which linearly combines the base filters and is shared across different tasks. Moreover, the shared weight matrix is multiplied by task specific coefficients to introduce more flexibility in combining task specific base filters differently for different tasks. Attributed to the novel architecture, the proposed method can preserve or even improve the generation quality at a low cost of parameters. We validate Hyper-LifelongGAN on diverse image-conditioned generation tasks, extensive ablation studies and comparisons with state-of-the-art models are carried out to show that the proposed approach can address catastrophic forgetting effectively. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhai_Hyper-LifelongGAN_Scalable_Lifelong_Learning_for_Image_Conditioned_Generation_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhai_Hyper-LifelongGAN_Scalable_Lifelong_Learning_for_Image_Conditioned_Generation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhai_Hyper-LifelongGAN_Scalable_Lifelong_Learning_for_Image_Conditioned_Generation_CVPR_2021_paper.html | CVPR 2021 | null | null |
Semi-Supervised Synthesis of High-Resolution Editable Textures for 3D Humans | Bindita Chaudhuri, Nikolaos Sarafianos, Linda Shapiro, Tony Tung | We introduce a novel approach to generate diverse high fidelity texture maps for 3D human meshes in a semi-supervised setup. Given a segmentation mask defining the layout of the semantic regions in the texture map, our network generates high-resolution textures with a variety of styles, that are then used for rendering purposes. To accomplish this task, we propose a Region-adaptive Adversarial Variational AutoEncoder (ReAVAE) that learns the probability distribution of the style of each region individually so that the style of the generated texture can be controlled by sampling from the region-specific distributions. In addition, we introduce a data generation technique to augment our training set with data lifted from single-view RGB inputs. Our training strategy allows the mixing of reference image styles with arbitrary styles for different regions, a property which can be valuable for virtual try-on AR/VR applications. Experimental results show that our method synthesizes better texture maps compared to prior work while enabling independent layout and style controllability. | https://openaccess.thecvf.com/content/CVPR2021/papers/Chaudhuri_Semi-Supervised_Synthesis_of_High-Resolution_Editable_Textures_for_3D_Humans_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.17266 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Chaudhuri_Semi-Supervised_Synthesis_of_High-Resolution_Editable_Textures_for_3D_Humans_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Chaudhuri_Semi-Supervised_Synthesis_of_High-Resolution_Editable_Textures_for_3D_Humans_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Chaudhuri_Semi-Supervised_Synthesis_of_CVPR_2021_supplemental.pdf | null |
CoSMo: Content-Style Modulation for Image Retrieval With Text Feedback | Seungmin Lee, Dongwan Kim, Bohyung Han | We tackle the task of image retrieval with text feedback, where a reference image and modifier text are combined to identify the desired target image. We focus on designing an image-text compositor, i.e., integrating multi-modal inputs to produce a representation similar to that of the target image. In our algorithm, Content-Style Modulation (CoSMo), we approach this challenge by introducing two modules based on deep neural networks: the content and style modulators. The content modulator performs local updates to the reference image feature after normalizing the style of the image, where a disentangled multi-modal non-local block is employed to achieve the desired content modifications. Then, the style modulator reintroduces global style information to the updated feature. We provide an in-depth view of our algorithm and its design choices, and show that it accomplishes outstanding performance on multiple image-text retrieval benchmarks. Our code can be found at: https://github.com/postBG/CosMo.pytorch | https://openaccess.thecvf.com/content/CVPR2021/papers/Lee_CoSMo_Content-Style_Modulation_for_Image_Retrieval_With_Text_Feedback_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Lee_CoSMo_Content-Style_Modulation_for_Image_Retrieval_With_Text_Feedback_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Lee_CoSMo_Content-Style_Modulation_for_Image_Retrieval_With_Text_Feedback_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Lee_CoSMo_Content-Style_Modulation_CVPR_2021_supplemental.pdf | null |
Thinking Fast and Slow: Efficient Text-to-Visual Retrieval With Transformers | Antoine Miech, Jean-Baptiste Alayrac, Ivan Laptev, Josef Sivic, Andrew Zisserman | Our objective is language-based search of large-scale image and video datasets. For this task, the approach that consists of independently mapping text and vision to a joint embedding space, a.k.a. dual encoders, is attractive as retrieval scales and is efficient for billions of images using approximate nearest neighbour search. An alternative approach of using vision-text transformers with cross-attention gives considerable improvements in accuracy over the joint embeddings, but is often inapplicable in practice for large-scale retrieval given the cost of the cross-attention mechanisms required for each sample at test time. This work combines the best of both worlds. We make the following three contributions. First, we equip transformer-based models with a new fine-grained cross-attention architecture, providing significant improvements in retrieval accuracy whilst preserving scalability. Second, we introduce a generic approach for combining a Fast dual encoder model with our Slow but accurate transformer-based model via distillation and re-ranking. Finally, we validate our approach on the Flickr30K image dataset where we show an increase in inference speed by several orders of magnitude while having results competitive to the state of the art. We also extend our method to the video domain, improving the state of the art on the VATEX dataset. | https://openaccess.thecvf.com/content/CVPR2021/papers/Miech_Thinking_Fast_and_Slow_Efficient_Text-to-Visual_Retrieval_With_Transformers_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.16553 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Miech_Thinking_Fast_and_Slow_Efficient_Text-to-Visual_Retrieval_With_Transformers_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Miech_Thinking_Fast_and_Slow_Efficient_Text-to-Visual_Retrieval_With_Transformers_CVPR_2021_paper.html | CVPR 2021 | null | null |
RGB-D Local Implicit Function for Depth Completion of Transparent Objects | Luyang Zhu, Arsalan Mousavian, Yu Xiang, Hammad Mazhar, Jozef van Eenbergen, Shoubhik Debnath, Dieter Fox | Majority of the perception methods in robotics require depth information provided by RGB-D cameras. However, standard 3D sensors fail to capture depth of transparent objects due to refraction and absorption of light. In this paper, we introduce a new approach for depth completion of transparent objects from a single RGB-D image. Key to our approach is a local implicit neural representation built on ray-voxel pairs that allows our method to generalize to unseen objects and achieve fast inference speed. Based on this representation, we present a novel framework that can complete missing depth given noisy RGB-D input. We further improve the depth estimation iteratively using a self-correcting refinement model. To train the whole pipeline, we build a large scale synthetic dataset with transparent objects. Experiments demonstrate that our method performs significantly better than the current state-of-the-art methods on both synthetic and real world data. In addition, our approach improves the inference speed by a factor of 20 compared to the previous best method, ClearGrasp. Code will be released at https://research.nvidia.com/publication/2021-03_RGB-D-Local-Implicit. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhu_RGB-D_Local_Implicit_Function_for_Depth_Completion_of_Transparent_Objects_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhu_RGB-D_Local_Implicit_Function_for_Depth_Completion_of_Transparent_Objects_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhu_RGB-D_Local_Implicit_Function_for_Depth_Completion_of_Transparent_Objects_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhu_RGB-D_Local_Implicit_CVPR_2021_supplemental.pdf | null |
Fingerspelling Detection in American Sign Language | Bowen Shi, Diane Brentari, Greg Shakhnarovich, Karen Livescu | Fingerspelling, in which words are signed letter by letter, is an important component of American Sign Language. Most previous work on automatic fingerspelling recognition has assumed that the boundaries of fingerspelling regions in signing videos are known beforehand. In this paper, we consider the task of fingerspelling detection in raw, untrimmed sign language videos. This is an important step towards building real-world fingerspelling recognition systems. We propose a benchmark and a suite of evaluation metrics, some of which reflect the effect of detection on the downstream fingerspelling recognition task. In addition, we propose a new model that learns to detect fingerspelling via multi-task training, incorporating pose estimation and fingerspelling recognition (transcription) along with detection, and compare this model to several alternatives. The model outperforms all alternative approaches across all metrics, establishing a state of the art on the benchmark. | https://openaccess.thecvf.com/content/CVPR2021/papers/Shi_Fingerspelling_Detection_in_American_Sign_Language_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.01291 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Shi_Fingerspelling_Detection_in_American_Sign_Language_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Shi_Fingerspelling_Detection_in_American_Sign_Language_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Shi_Fingerspelling_Detection_in_CVPR_2021_supplemental.pdf | null |
Uncertainty Reduction for Model Adaptation in Semantic Segmentation | Prabhu Teja S, Francois Fleuret | Traditional methods for Unsupervised Domain Adaptation (UDA) targeting semantic segmentation exploit information common to the source and target domains, using both labeled source data and unlabeled target data. In this paper, we investigate a setting where the source data is unavailable, but the classifier trained on the source data is; hence named ""model adaptation"". Such a scenario arises when data sharing is prohibited, for instance, because of privacy, or Intellectual Property (IP) issues. To tackle this problem, we propose a method that reduces the uncertainty of predictions on the target domain data. We accomplish this in two ways: minimizing the entropy of the predicted posterior, and maximizing the noise robustness of the feature representation. We show the efficacy of our method on the transfer of segmentation from computer generated images to real-world driving images, and transfer between data collected in different cities, and surprisingly reach performance competitive with that of the methods that have access to source data. | https://openaccess.thecvf.com/content/CVPR2021/papers/S_Uncertainty_Reduction_for_Model_Adaptation_in_Semantic_Segmentation_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/S_Uncertainty_Reduction_for_Model_Adaptation_in_Semantic_Segmentation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/S_Uncertainty_Reduction_for_Model_Adaptation_in_Semantic_Segmentation_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/S_Uncertainty_Reduction_for_CVPR_2021_supplemental.pdf | null |
Learning Triadic Belief Dynamics in Nonverbal Communication From Videos | Lifeng Fan, Shuwen Qiu, Zilong Zheng, Tao Gao, Song-Chun Zhu, Yixin Zhu | Humans possess a unique social cognition capability; nonverbal communication can convey rich social information among agents. In contrast, such crucial social characteristics are mostly missing in the existing scene understanding literature. In this paper, we incorporate different nonverbal communication cues (e.g., gaze, human poses, and gestures) to represent, model, learn, and infer agents' mental states from pure visual inputs. Crucially, such a mental representation takes the agent's belief into account so that it represents what the true world state is and infers the beliefs in each agent's mental state, which may differ from the true world states. By aggregating different beliefs and true world states, our model essentially forms "five minds" during the interactions between two agents. This "five minds" model differs from prior works that infer beliefs in an infinite recursion; instead, agents' beliefs are converged into a "common mind". Based on this representation, we further devise a hierarchical energy-based model that jointly tracks and predicts all five minds. From this new perspective, a social event is interpreted by a series of nonverbal communication and belief dynamics, which transcends the classic keyframe video summary. In the experiments, we demonstrate that using such a social account provides a better video summary on videos with rich social interactions compared with state-of-the-art keyframe video summary methods. | https://openaccess.thecvf.com/content/CVPR2021/papers/Fan_Learning_Triadic_Belief_Dynamics_in_Nonverbal_Communication_From_Videos_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.02841 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Fan_Learning_Triadic_Belief_Dynamics_in_Nonverbal_Communication_From_Videos_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Fan_Learning_Triadic_Belief_Dynamics_in_Nonverbal_Communication_From_Videos_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Fan_Learning_Triadic_Belief_CVPR_2021_supplemental.pdf | null |
Temporal Modulation Network for Controllable Space-Time Video Super-Resolution | Gang Xu, Jun Xu, Zhen Li, Liang Wang, Xing Sun, Ming-Ming Cheng | Space-time video super-resolution (STVSR) aims to increase the spatial and temporal resolutions of low-resolution and low-frame-rate videos. Recently, deformable convolution based methods have achieved promising STVSR performance, but they could only infer the intermediate frame pre-defined in the training stage. Besides, these methods undervalued the short-term motion cues among adjacent frames. In this paper, we propose a Temporal Modulation Network (TMNet) to interpolate arbitrary intermediate frame(s) with accurate high-resolution reconstruction. Specifically, we propose a Temporal Modulation Block (TMB) to modulate deformable convolution kernels for controllable feature interpolation. To well exploit the temporal information, we propose a Locally-temporal Feature Comparison (LFC) module, along with the Bi-directional Deformable ConvLSTM, to extract short-term and long-term motion cues in videos. Experiments on three benchmark datasets demonstrate that our TMNet outperforms previous STVSR methods. The code is available at https://github.com/CS-GangXu/TMNet. | https://openaccess.thecvf.com/content/CVPR2021/papers/Xu_Temporal_Modulation_Network_for_Controllable_Space-Time_Video_Super-Resolution_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.10642 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Xu_Temporal_Modulation_Network_for_Controllable_Space-Time_Video_Super-Resolution_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Xu_Temporal_Modulation_Network_for_Controllable_Space-Time_Video_Super-Resolution_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Xu_Temporal_Modulation_Network_CVPR_2021_supplemental.pdf | null |
Zero-Shot Single Image Restoration Through Controlled Perturbation of Koschmieder's Model | Aupendu Kar, Sobhan Kanti Dhara, Debashis Sen, Prabir Kumar Biswas | Real-world image degradation due to light scattering can be described based on the Koschmieder's model. Training deep models to restore such degraded images is challenging as real-world paired data is scarcely available and synthetic paired data may suffer from domain-shift issues. In this paper, a zero-shot single real-world image restoration model is proposed leveraging a theoretically deduced property of degradation through the Koschmieder's model. Our zero-shot network estimates the parameters of the Koschmieder's model, which describes the degradation in the input image, to perform image restoration. We show that a suitable degradation of the input image amounts to a controlled perturbation of the Koschmieder's model that describes the image's formation. The optimization of the zero-shot network is achieved by seeking to maintain the relation between its estimates of Koschmieder's model parameters before and after the controlled perturbation, along with the use of a few no-reference losses. Image dehazing and underwater image restoration are carried out using the proposed zero-shot framework, which in general outperforms the state-of-the-art quantitatively and subjectively on multiple standard real-world image datasets. Additionally, the application of our zero-shot framework for low-light image enhancement is also demonstrated. | https://openaccess.thecvf.com/content/CVPR2021/papers/Kar_Zero-Shot_Single_Image_Restoration_Through_Controlled_Perturbation_of_Koschmieders_Model_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Kar_Zero-Shot_Single_Image_Restoration_Through_Controlled_Perturbation_of_Koschmieders_Model_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Kar_Zero-Shot_Single_Image_Restoration_Through_Controlled_Perturbation_of_Koschmieders_Model_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Kar_Zero-Shot_Single_Image_CVPR_2021_supplemental.pdf | null |
Uncertainty-Aware Camera Pose Estimation From Points and Lines | Alexander Vakhitov, Luis Ferraz, Antonio Agudo, Francesc Moreno-Noguer | Perspective-n-Point-and-Line (PnPL) algorithms aim at fast, accurate, and robust camera localization with respect to a 3D model from 2D-3D feature correspondences, being a major part of modern robotic and AR/VR systems. Current point-based pose estimation methods use only 2D feature detection uncertainties, and the line-based methods do not take uncertainties into account. In our setup, both 3D coordinates and 2D projections of the features are considered uncertain. We propose PnP(L) solvers based on EPnP[20] and DLS[14] for the uncertainty-aware pose estimation. We also modify motion-only bundle adjustment to take 3D uncertainties into account. We perform exhaustive synthetic and real experiments on two different visual odometry datasets. The new PnP(L) methods outperform the state-of-the-art on real data in isolation, showing an increase in mean translation accuracy by 18% on a representative subset of KITTI, while the new uncertain refinement improves pose accuracy for most of the solvers, e.g. decreasing mean translation error for the EPnP by 16% compared to the standard refinement on the same dataset. The code is available at https://alexandervakhitov.github.io/uncertain-pnp/. | https://openaccess.thecvf.com/content/CVPR2021/papers/Vakhitov_Uncertainty-Aware_Camera_Pose_Estimation_From_Points_and_Lines_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Vakhitov_Uncertainty-Aware_Camera_Pose_Estimation_From_Points_and_Lines_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Vakhitov_Uncertainty-Aware_Camera_Pose_Estimation_From_Points_and_Lines_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Vakhitov_Uncertainty-Aware_Camera_Pose_CVPR_2021_supplemental.pdf | null |
Temporal Context Aggregation Network for Temporal Action Proposal Refinement | Zhiwu Qing, Haisheng Su, Weihao Gan, Dongliang Wang, Wei Wu, Xiang Wang, Yu Qiao, Junjie Yan, Changxin Gao, Nong Sang | Temporal action proposal generation aims to estimate temporal intervals of actions in untrimmed videos, which is a challenging yet important task in the video understanding field. The proposals generated by current methods still suffer from inaccurate temporal boundaries and inferior confidence used for retrieval owing to the lack of efficient temporal modeling and effective boundary context utilization. In this paper, we propose Temporal Context Aggregation Network (TCANet) to generate high-quality action proposals through local and global temporal context aggregation and complementary as well as progressive boundary refinement. Specifically, we first design a Local-Global Temporal Encoder (LGTE), which adopts the channel grouping strategy to efficiently encode both local and global temporal inter-dependencies. Furthermore, both the boundary and internal context of proposals are adopted for frame-level and segment-level boundary regressions, respectively. Temporal Boundary Regressor (TBR) is designed to combine these two regression granularities in an end-to-end fashion, which achieves the precise boundaries and reliable confidence of proposals through progressive refinement. Extensive experiments are conducted on three challenging datasets: HACS, ActivityNet-v1.3, and THUMOS-14, where TCANet can generate proposals with high precision and recall. By combining with the existing action classifier, TCANet can obtain remarkable temporal action detection performance compared with other methods. Not surprisingly, the proposed TCANet won the 1st place in the CVPR 2020 - HACS challenge leaderboard on temporal action localization task. | https://openaccess.thecvf.com/content/CVPR2021/papers/Qing_Temporal_Context_Aggregation_Network_for_Temporal_Action_Proposal_Refinement_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.13141 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Qing_Temporal_Context_Aggregation_Network_for_Temporal_Action_Proposal_Refinement_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Qing_Temporal_Context_Aggregation_Network_for_Temporal_Action_Proposal_Refinement_CVPR_2021_paper.html | CVPR 2021 | null | null |
Information-Theoretic Segmentation by Inpainting Error Maximization | Pedro Savarese, Sunnie S. Y. Kim, Michael Maire, Greg Shakhnarovich, David McAllester | We study image segmentation from an information-theoretic perspective, proposing a novel adversarial method that performs unsupervised segmentation by partitioning images into maximally independent sets. More specifically, we group image pixels into foreground and background, with the goal of minimizing predictability of one set from the other. An easily computed loss drives a greedy search process to maximize inpainting error over these partitions. Our method does not involve training deep networks, is computationally cheap, class-agnostic, and even applicable in isolation to a single unlabeled image. Experiments demonstrate that it achieves a new state-of-the-art in unsupervised segmentation quality, while being substantially faster and more general than competing approaches. | https://openaccess.thecvf.com/content/CVPR2021/papers/Savarese_Information-Theoretic_Segmentation_by_Inpainting_Error_Maximization_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.07287 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Savarese_Information-Theoretic_Segmentation_by_Inpainting_Error_Maximization_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Savarese_Information-Theoretic_Segmentation_by_Inpainting_Error_Maximization_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Savarese_Information-Theoretic_Segmentation_by_CVPR_2021_supplemental.pdf | https://openaccess.thecvf.com |
Adaptive Prototype Learning and Allocation for Few-Shot Segmentation | Gen Li, Varun Jampani, Laura Sevilla-Lara, Deqing Sun, Jonghyun Kim, Joongkyu Kim | Prototype learning is extensively used for few-shot segmentation. Typically, a single prototype is obtained from the support feature by averaging the global object information. However, using one prototype to represent all the information may lead to ambiguities. In this paper, we propose two novel modules, named superpixel-guided clustering (SGC) and guided prototype allocation (GPA), for multiple prototype extraction and allocation. Specifically, SGC is a parameter-free and training-free approach, which extracts more representative prototypes by aggregating similar feature vectors, while GPA is able to select matched prototypes to provide more accurate guidance. By integrating the SGC and GPA together, we propose the Adaptive Superpixel-guided Network (ASGNet), which is a lightweight model and adapts to object scale and shape variation. In addition, our network can easily generalize to k-shot segmentation with substantial improvement and no additional computational cost. In particular, our evaluations on COCO demonstrate that ASGNet surpasses the state-of-the-art method by 5% in 5-shot segmentation. | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_Adaptive_Prototype_Learning_and_Allocation_for_Few-Shot_Segmentation_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Adaptive_Prototype_Learning_and_Allocation_for_Few-Shot_Segmentation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Adaptive_Prototype_Learning_and_Allocation_for_Few-Shot_Segmentation_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Li_Adaptive_Prototype_Learning_CVPR_2021_supplemental.pdf | null |
RefineMask: Towards High-Quality Instance Segmentation With Fine-Grained Features | Gang Zhang, Xin Lu, Jingru Tan, Jianmin Li, Zhaoxiang Zhang, Quanquan Li, Xiaolin Hu | The two-stage methods for instance segmentation, e.g. Mask R-CNN, have achieved excellent performance recently. However, the segmented masks are still very coarse due to the downsampling operations in both the feature pyramid and the instance-wise pooling process, especially for large objects. In this work, we propose a new method called RefineMask for high-quality instance segmentation of objects and scenes, which incorporates fine-grained features during the instance-wise segmenting process in a multi-stage manner. Through fusing more detailed information stage by stage, RefineMask is able to refine high-quality masks consistently. RefineMask succeeds in segmenting hard cases such as bent parts of objects that are over-smoothed by most previous methods and outputs accurate boundaries. Without bells and whistles, RefineMask yields significant gains of 2.6, 3.4, 3.8 AP over Mask R-CNN on COCO, LVIS, and Cityscapes benchmarks respectively at a small amount of additional computational cost. Furthermore, our single-model result outperforms the winner of the LVIS Challenge 2020 by 1.3 points on the LVIS test-dev set and establishes a new state-of-the-art. Code will be available at https://github.com/zhanggang001/RefineMask. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_RefineMask_Towards_High-Quality_Instance_Segmentation_With_Fine-Grained_Features_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.08569 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_RefineMask_Towards_High-Quality_Instance_Segmentation_With_Fine-Grained_Features_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_RefineMask_Towards_High-Quality_Instance_Segmentation_With_Fine-Grained_Features_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhang_RefineMask_Towards_High-Quality_CVPR_2021_supplemental.pdf | null |
DCNAS: Densely Connected Neural Architecture Search for Semantic Image Segmentation | Xiong Zhang, Hongmin Xu, Hong Mo, Jianchao Tan, Cheng Yang, Lei Wang, Wenqi Ren | Existing NAS methods for dense image prediction tasks usually compromise on restricted search space or search on proxy task to meet the achievable computational demands. To allow as wide as possible network architectures and avoid the gap between realistic and proxy setting, we propose a novel Densely Connected NAS (DCNAS) framework, which directly searches the optimal network structures for the multi-scale representations of visual information, over a large-scale target dataset without proxy. Specifically, by connecting cells with each other using learnable weights, we introduce a densely connected search space to cover an abundance of mainstream network designs. Moreover, by combining both path-level and channel-level sampling strategies, we design a fusion module and mixture layer to reduce the memory consumption of ample search space, hence favoring the proxyless searching. Compared with contemporary works, experiments reveal that the proxyless searching scheme is capable of bridging the gap between searching and training environments. Further, DCNAS achieves new state-of-the-art performances on public semantic image segmentation benchmarks, including 84.3% on Cityscapes, and 86.9% on PASCAL VOC 2012. We also retain leading performances when evaluating the architecture on the more challenging ADE20K and PASCAL-Context dataset. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_DCNAS_Densely_Connected_Neural_Architecture_Search_for_Semantic_Image_Segmentation_CVPR_2021_paper.pdf | http://arxiv.org/abs/2003.11883 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_DCNAS_Densely_Connected_Neural_Architecture_Search_for_Semantic_Image_Segmentation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_DCNAS_Densely_Connected_Neural_Architecture_Search_for_Semantic_Image_Segmentation_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhang_DCNAS_Densely_Connected_CVPR_2021_supplemental.zip | null |
Tackling the Ill-Posedness of Super-Resolution Through Adaptive Target Generation | Younghyun Jo, Seoung Wug Oh, Peter Vajda, Seon Joo Kim | By the one-to-many nature of the super-resolution (SR) problem, a single low-resolution (LR) image can be mapped to many high-resolution (HR) images. However, learning based SR algorithms are trained to map an LR image to the corresponding ground truth (GT) HR image in the training dataset. The training loss will increase and penalize the algorithm when the output does not exactly match the GT target, even when the outputs are mathematically valid candidates according to the SR framework. This becomes more problematic for the blind SR, as diverse unknown blur kernels exacerbate the ill-posedness of the problem. To this end, we propose a fundamentally different approach for the SR by introducing the concept of the adaptive target. The adaptive target is generated from the original GT target by a transformation to match the output of the SR network. The adaptive target provides an effective way for the SR algorithm to deal with the ill-posed nature of the SR, by providing the algorithm with the flexibility of accepting a variety of valid solutions. Experimental results show the effectiveness of our algorithm, especially for improving the perceptual quality of HR outputs. | https://openaccess.thecvf.com/content/CVPR2021/papers/Jo_Tackling_the_Ill-Posedness_of_Super-Resolution_Through_Adaptive_Target_Generation_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Jo_Tackling_the_Ill-Posedness_of_Super-Resolution_Through_Adaptive_Target_Generation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Jo_Tackling_the_Ill-Posedness_of_Super-Resolution_Through_Adaptive_Target_Generation_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Jo_Tackling_the_Ill-Posedness_CVPR_2021_supplemental.pdf | null |
DiNTS: Differentiable Neural Network Topology Search for 3D Medical Image Segmentation | Yufan He, Dong Yang, Holger Roth, Can Zhao, Daguang Xu | Recently, neural architecture search(NAS) has been applied to automatically search high-performance networks for medical image segmentation. The NAS search space usually contains a network topology level(controlling connections among cells with different spatial scales) and a cell level(operations within each cell). Existing methods either require long searching time for large-scale 3D image datasets, or are limited to pre-defined topologies (such as U-shaped or single-path). In this work, we focus on three important aspects of NAS in 3D medical image segmentation: flexible multi-path network topology, high search efficiency, and budgeted GPU memory usage. A novel differentiable search framework is proposed to support fast gradient-based search within a highly flexible network topology search space. The discretization of the searched optimal continuous model in differentiable scheme may produce a sub-optimal final discrete model (discretization gap). Therefore, we propose a topology loss to alleviate this problem. In addition, the GPU memory usage for the searched 3D model is limited with budget constraints during search. Our Differentiable Network Topology Search scheme(DiNTS) is evaluated on the Medical Segmentation Decathlon (MSD) challenge, which contains ten challenging segmentation tasks. Our method achieves the state-of-the-art performance and the top ranking on the MSD challenge leaderboard. | https://openaccess.thecvf.com/content/CVPR2021/papers/He_DiNTS_Differentiable_Neural_Network_Topology_Search_for_3D_Medical_Image_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.15954 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/He_DiNTS_Differentiable_Neural_Network_Topology_Search_for_3D_Medical_Image_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/He_DiNTS_Differentiable_Neural_Network_Topology_Search_for_3D_Medical_Image_CVPR_2021_paper.html | CVPR 2021 | null | null |
Im2Vec: Synthesizing Vector Graphics Without Vector Supervision | Pradyumna Reddy, Michael Gharbi, Michal Lukac, Niloy J. Mitra | Vector graphics are widely used to represent fonts, logos, digital artworks, and graphic designs. But, while a vast body of work has focused on generative algorithms for raster images, only a handful of options exists for vector graphics. One can always rasterize the input graphic and resort to image-based generative approaches, but this negates the advantages of the vector representation. The current alternative is to use specialized models that require explicit supervision on the vector graphics representation at training time. This is not ideal because large-scale high-quality vector-graphics datasets are difficult to obtain. Furthermore, the vector representation for a given design is not unique, so models that supervise on the vector representation are unnecessarily constrained. Instead, we propose a new neural network that can generate complex vector graphics with varying topologies, and only requires in-direct supervision from readily-available raster training images (i.e., with no vector counterparts). To enable this, we use a differentiable rasterization pipeline that renders the generated vector shapes and composites them together onto a raster canvas. We demonstrate our method on a range of datasets, and provide comparison with state-of-the-art SVG-VAE and DeepSVG, both of which require explicit vector graphics supervision. Finally, we also demonstrate our approach on the MNIST dataset, for which no groundtruth vector representation is available. | https://openaccess.thecvf.com/content/CVPR2021/papers/Reddy_Im2Vec_Synthesizing_Vector_Graphics_Without_Vector_Supervision_CVPR_2021_paper.pdf | http://arxiv.org/abs/2102.02798 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Reddy_Im2Vec_Synthesizing_Vector_Graphics_Without_Vector_Supervision_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Reddy_Im2Vec_Synthesizing_Vector_Graphics_Without_Vector_Supervision_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Reddy_Im2Vec_Synthesizing_Vector_CVPR_2021_supplemental.zip | null |
Perception Matters: Detecting Perception Failures of VQA Models Using Metamorphic Testing | Yuanyuan Yuan, Shuai Wang, Mingyue Jiang, Tsong Yueh Chen | Visual question answering (VQA) takes an image and a natural-language question as input and returns a natural-language answer. To date, VQA models are primarily assessed by their accuracy on high-level reasoning questions. Nevertheless, Given that perception tasks (e.g., recognizing objects) are the building blocks in the compositional process required by high-level reasoning, there is a demanding need to gain insights into how much of a problem low-level perception is. Inspired by the principles of software metamorphic testing, we introduce MetaVQA, a model-agnostic framework for benchmarking perception capability of VQA models. Given an image i, MetaVQA is able to synthesize a low level perception question q. It then jointly transforms (i, q) to one or a set of sub-questions and sub-images. MetaVQA checks whether the answer to (i, q) satisfies metamorphic relationships (MRs), denoting perception consistency, with the composed answers of transformed questions and images. Violating MRs denotes a failure of answering perception questions. MetaVQA successfully detects over 4.9 million perception failures made by popular VQA models with metamorphic testing. The state-of-the-art VQA models (e.g., the champion of VQA 2020 Challenge) suffer from perception consistency problems. In contrast, the Oscar VQA models, by using anchor points to align questions and images, show generally better consistency in perception tasks. We hope MetaVQA will revitalize interest in enhancing the low-level perceptual abilities of VQA models, a cornerstone of high-level reasoning. | https://openaccess.thecvf.com/content/CVPR2021/papers/Yuan_Perception_Matters_Detecting_Perception_Failures_of_VQA_Models_Using_Metamorphic_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Yuan_Perception_Matters_Detecting_Perception_Failures_of_VQA_Models_Using_Metamorphic_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Yuan_Perception_Matters_Detecting_Perception_Failures_of_VQA_Models_Using_Metamorphic_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yuan_Perception_Matters_Detecting_CVPR_2021_supplemental.pdf | null |
Unsupervised Part Segmentation Through Disentangling Appearance and Shape | Shilong Liu, Lei Zhang, Xiao Yang, Hang Su, Jun Zhu | We study the problem of unsupervised discovery and segmentation of object parts, which, as an intermediate local representation, are capable of finding intrinsic object structure and providing more explainable recognition results. Recent unsupervised methods have greatly relaxed the dependency on annotated data which are costly to obtain, but still rely on additional information such as object segmentation masks or saliency map. To remove such a dependency and further improve the part segmentation performance, we develop a novel approach by disentangling the appearance and shape representations of object parts followed with reconstruction losses without using additional object mask information. To avoid degenerated solutions, a bottleneck block is designed to squeeze and expand the appearance representation, leading to a more effective disentanglement between geometry and appearance. Combined with a self-supervised part classification loss and an improved geometry concentration constraint, we can segment more consistent parts with semantic meanings. Comprehensive experiments on a wide variety of objects such as face, bird, and PASCAL VOC objects demonstrate the effectiveness of the proposed method. | https://openaccess.thecvf.com/content/CVPR2021/papers/Liu_Unsupervised_Part_Segmentation_Through_Disentangling_Appearance_and_Shape_CVPR_2021_paper.pdf | http://arxiv.org/abs/2105.12405 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_Unsupervised_Part_Segmentation_Through_Disentangling_Appearance_and_Shape_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_Unsupervised_Part_Segmentation_Through_Disentangling_Appearance_and_Shape_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Liu_Unsupervised_Part_Segmentation_CVPR_2021_supplemental.pdf | null |
Adversarial Imaging Pipelines | Buu Phan, Fahim Mannan, Felix Heide | Adversarial attacks play a critical role in understanding deep neural network predictions and improving their robustness. Existing attack methods aim to deceive convolutional neural network (CNN)-based classifiers by manipulating RGB images that are fed directly to the classifiers. However, these approaches typically neglect the influence of the camera optics and image processing pipeline (ISP) that produce the network inputs. ISPs transform RAW measurements to RGB images and traditionally are assumed to preserve adversarial patterns. In fact, these low-level pipelines can destroy, introduce or amplify adversarial patterns that can deceive a downstream detector. As a result, optimized patterns can become adversarial for the classifier after being transformed by a certain camera ISP or optical lens system but not for others. In this work, we examine and develop such an attack that deceives a specific camera ISP while leaving others intact, using the same downstream classifier. We frame this camera-specific attack as a multi-task optimization problem, relying on a differentiable approximation for the ISP itself. We validate the proposed method using recent state-of-the-art automotive hardware ISPs, achieving 92% fooling rate when attacking a specific ISP. We demonstrate physical optics attacks with 90% fooling rate for a specific camera lens. | https://openaccess.thecvf.com/content/CVPR2021/papers/Phan_Adversarial_Imaging_Pipelines_CVPR_2021_paper.pdf | http://arxiv.org/abs/2102.03728 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Phan_Adversarial_Imaging_Pipelines_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Phan_Adversarial_Imaging_Pipelines_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Phan_Adversarial_Imaging_Pipelines_CVPR_2021_supplemental.pdf | null |
Adaptive Consistency Regularization for Semi-Supervised Transfer Learning | Abulikemu Abuduweili, Xingjian Li, Humphrey Shi, Cheng-Zhong Xu, Dejing Dou | While recent studies on semi-supervised learning have shown remarkable progress in leveraging both labeled and unlabeled data, most of them presume a basic setting of the model is randomly initialized. In this work, we consider semi-supervised learning and transfer learning jointly, leading to a more practical and competitive paradigm that can utilize both powerful pre-trained models from the source domain as well as labeled/unlabeled data in the target domain. To better exploit the value of both pre-trained weights and unlabeled target examples, we introduce adaptive consistency regularization that consists of two complementary components: Adaptive Knowledge Consistency (AKC) on the examples between the source and target model, and Adaptive Representation Consistency (ARC) on the target model between labeled and unlabeled examples. Examples involved in the consistency regularization are adaptively selected according to their potential contributions to the target task. We conduct extensive experiments on popular benchmarks including CIFAR-10, CUB-200, and MURA, by fine-tuning the ImageNet pre-trained ResNet-50 model. Results show that our proposed adaptive consistency regularization outperforms state-of-the-art semi-supervised learning techniques such as Pseudo Label, Mean Teacher, and FixMatch. Moreover, our algorithm is orthogonal to existing methods and thus able to gain additional improvements on top of MixMatch and FixMatch. Our code is available at https://github.com/Walleclipse/Semi-Supervised-Transfer-Learning-Paddle. | https://openaccess.thecvf.com/content/CVPR2021/papers/Abuduweili_Adaptive_Consistency_Regularization_for_Semi-Supervised_Transfer_Learning_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.02193 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Abuduweili_Adaptive_Consistency_Regularization_for_Semi-Supervised_Transfer_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Abuduweili_Adaptive_Consistency_Regularization_for_Semi-Supervised_Transfer_Learning_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Abuduweili_Adaptive_Consistency_Regularization_CVPR_2021_supplemental.pdf | null |
GANmut: Learning Interpretable Conditional Space for Gamut of Emotions | Stefano d'Apolito, Danda Pani Paudel, Zhiwu Huang, Andres Romero, Luc Van Gool | Humans can communicate emotions through a plethora of facial expressions, each with its own intensity, nuances and ambiguities. The generation of such variety by means of conditional GANs is limited to the expressions encoded in the used label system. These limitations are caused either due to burdensome labeling demand or the confounded label space. On the other hand, learning from inexpensive and intuitive basic categorical emotion labels leads to limited emotion variability. In this paper, we propose a novel GAN-based framework which learns an expressive and interpretable conditional space (usable as a label space) of emotions, instead of conditioning on handcrafted labels. Our framework only uses the categorical labels of basic emotions to jointly learn the conditional space as well as the emotion manipulation. Such learning can benefit from the image variability within discrete labels, especially when the intrinsic labels reside beyond the discrete space of the defined. Our experiments demonstrate the effectiveness of the proposed framework, by allowing us to control and generate a gamut of complex and compound emotions, while using only the basic categorical emotion labels during training. | https://openaccess.thecvf.com/content/CVPR2021/papers/dApolito_GANmut_Learning_Interpretable_Conditional_Space_for_Gamut_of_Emotions_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/dApolito_GANmut_Learning_Interpretable_Conditional_Space_for_Gamut_of_Emotions_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/dApolito_GANmut_Learning_Interpretable_Conditional_Space_for_Gamut_of_Emotions_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/dApolito_GANmut_Learning_Interpretable_CVPR_2021_supplemental.zip | null |
StyleSpace Analysis: Disentangled Controls for StyleGAN Image Generation | Zongze Wu, Dani Lischinski, Eli Shechtman | We explore and analyze the latent style space of StyleGAN2, a state-of-the-art architecture for image generation, using models pretrained on several different datasets. We first show that StyleSpace, the space of channel-wise style parameters, is significantly more disentangled than the other intermediate latent spaces explored by previous works. Next, we describe a method for discovering a large collection of style channels, each of which is shown to control a distinct visual attribute in a highly localized and disentangled manner. Third, we propose a simple method for identifying style channels that control a specific attribute, using a pretrained classifier or a small number of example images. Manipulation of visual attributes via these StyleSpace controls is shown to be better disentangled than via those proposed in previous works. To show this, we make use of a newly proposed Attribute Dependency metric. Finally, we demonstrate the applicability of StyleSpace controls to the manipulation of real images. Our findings pave the way to semantically meaningful and well-disentangled image manipulations via simple and intuitive interfaces. | https://openaccess.thecvf.com/content/CVPR2021/papers/Wu_StyleSpace_Analysis_Disentangled_Controls_for_StyleGAN_Image_Generation_CVPR_2021_paper.pdf | http://arxiv.org/abs/2011.12799 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wu_StyleSpace_Analysis_Disentangled_Controls_for_StyleGAN_Image_Generation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wu_StyleSpace_Analysis_Disentangled_Controls_for_StyleGAN_Image_Generation_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wu_StyleSpace_Analysis_Disentangled_CVPR_2021_supplemental.pdf | null |
Rethinking the Heatmap Regression for Bottom-Up Human Pose Estimation | Zhengxiong Luo, Zhicheng Wang, Yan Huang, Liang Wang, Tieniu Tan, Erjin Zhou | Heatmap regression has become the most prevalent choice for nowadays human pose estimation methods. The ground-truth heatmaps are usually constructed by covering all skeletal keypoints by 2D gaussian kernels. The standard deviations of these kernels are fixed. However, for bottom-up methods, which need to handle a large variance of human scales and labeling ambiguities, the current practice seems unreasonable. To better cope with these problems, we propose the scale-adaptive heatmap regression (SAHR) method, which can adaptively adjust the standard deviation for each keypoint. In this way, SAHR is more tolerant of various human scales and labeling ambiguities. However, SAHR may aggravate the imbalance between fore-background samples, which potentially hurts the improvement of SAHR. Thus, we further introduce the weight-adaptive heatmap regression (WAHR) to help balance the fore-background samples. Extensive experiments show that SAHR together with WAHR largely improves the accuracy of bottom-up human pose estimation. As a result, we finally outperform the state-of-the-art model by +1.5AP and achieve 72.0 AP on COCO test-dev2017, which is comparable with the performances of most top-down methods. Source codes are available at https://github.com/greatlog/SWAHR-HumanPose. | https://openaccess.thecvf.com/content/CVPR2021/papers/Luo_Rethinking_the_Heatmap_Regression_for_Bottom-Up_Human_Pose_Estimation_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.15175 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Luo_Rethinking_the_Heatmap_Regression_for_Bottom-Up_Human_Pose_Estimation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Luo_Rethinking_the_Heatmap_Regression_for_Bottom-Up_Human_Pose_Estimation_CVPR_2021_paper.html | CVPR 2021 | null | null |
From Semantic Categories to Fixations: A Novel Weakly-Supervised Visual-Auditory Saliency Detection Approach | Guotao Wang, Chenglizhao Chen, Deng-Ping Fan, Aimin Hao, Hong Qin | Thanks to the rapid advances in the deep learning techniques and the wide availability of large-scale training sets, the performances of video saliency detection models have been improving steadily and significantly. However, the deep learning based visual-audio fixation prediction is still in its infancy. At present, only a few visual-audio sequences have been furnished with real fixations being recorded in the real visual-audio environment. Hence, it would be neither efficiency nor necessary to re-collect real fixations under the same visual-audio circumstance. To address the problem, this paper advocate a novel approach in a weakly-supervised manner to alleviating the demand of large-scale training sets for visual-audio model training. By using the video category tags only, we propose the selective class activation mapping (SCAM), which follows a coarse-to-fine strategy to select the most discriminative regions in the spatial-temporal-audio circumstance. Moreover, these regions exhibit high consistency with the real human-eye fixations, which could subsequently be employed as the pseudo GTs to train a new spatial-temporal-audio (STA) network. Without resorting to any real fixation, the performance of our STA network is comparable to that of the fully supervised ones. | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_From_Semantic_Categories_to_Fixations_A_Novel_Weakly-Supervised_Visual-Auditory_Saliency_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_From_Semantic_Categories_to_Fixations_A_Novel_Weakly-Supervised_Visual-Auditory_Saliency_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_From_Semantic_Categories_to_Fixations_A_Novel_Weakly-Supervised_Visual-Auditory_Saliency_CVPR_2021_paper.html | CVPR 2021 | null | null |
High-Fidelity Face Tracking for AR/VR via Deep Lighting Adaptation | Lele Chen, Chen Cao, Fernando De la Torre, Jason Saragih, Chenliang Xu, Yaser Sheikh | 3D video avatars can empower virtual communications by providing compression, privacy, entertainment, and a sense of presence in AR/VR. Best 3D photo-realistic AR/VR avatars driven by video, that can minimize uncanny effects, rely on person-specific models. However, existing person-specific photo-realistic 3D models are not robust to lighting, hence their results typically miss subtle facial behaviors and cause artifacts in the avatar. This is a major drawback for the scalability of these models in communication systems (e.g., Messenger, Skype, FaceTime) and AR/VR. This paper addresses previous limitations by learning a deep learning lighting model, that in combination with a high-quality 3D face tracking algorithm, provides a method for subtle and robust facial motion transfer from a regular video to a 3D photo-realistic avatar. Extensive experimental validation and comparisons to other state-of-the-art methods demonstrate the effectiveness of the proposed framework in real-world scenarios with variability in pose, expression, and illumination. Our project page can be found at https://www.cs.rochester.edu/ cxu22/r/wild-avatar/. | https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_High-Fidelity_Face_Tracking_for_ARVR_via_Deep_Lighting_Adaptation_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.15876 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_High-Fidelity_Face_Tracking_for_ARVR_via_Deep_Lighting_Adaptation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_High-Fidelity_Face_Tracking_for_ARVR_via_Deep_Lighting_Adaptation_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Chen_High-Fidelity_Face_Tracking_CVPR_2021_supplemental.pdf | null |
Mixed-Privacy Forgetting in Deep Networks | Aditya Golatkar, Alessandro Achille, Avinash Ravichandran, Marzia Polito, Stefano Soatto | We show that the influence of a subset of the training samples can be removed -- or "forgotten" -- from the weights of a network trained on large-scale image classification tasks, and we provide strong computable bounds on the amount of remaining information after forgetting. Inspired by real-world applications of forgetting techniques, we introduce a novel notion of forgetting in mixed-privacy setting, where we know that a "core" subset of the training samples does not need to be forgotten. While this variation of the problem is conceptually simple, we show that working in this setting significantly improves the accuracy and guarantees of forgetting methods applied to vision classification tasks. Moreover, our method allows efficient removal of all information contained in non-core data by simply setting to zero a subset of the weights with minimal loss in performance. We achieve these results by replacing a standard deep network with a suitable linear approximation. With opportune changes to the network architecture and training procedure, we show that such linear approximation achieves comparable performance to the original network and that the forgetting problem becomes quadratic and can be solved efficiently even for large models. Unlike previous forgetting methods on deep networks, ours can achieve close to the state-of-the-art accuracy on large scale vision tasks. In particular, we show that our method allows forgetting without having to trade off the model accuracy. | https://openaccess.thecvf.com/content/CVPR2021/papers/Golatkar_Mixed-Privacy_Forgetting_in_Deep_Networks_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.13431 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Golatkar_Mixed-Privacy_Forgetting_in_Deep_Networks_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Golatkar_Mixed-Privacy_Forgetting_in_Deep_Networks_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Golatkar_Mixed-Privacy_Forgetting_in_CVPR_2021_supplemental.pdf | null |
TediGAN: Text-Guided Diverse Face Image Generation and Manipulation | Weihao Xia, Yujiu Yang, Jing-Hao Xue, Baoyuan Wu | In this work, we propose TediGAN, a novel framework for multi-modal image generation and manipulation with textual descriptions. The proposed method consists of three components: StyleGAN inversion module, visual-linguistic similarity learning, and instance-level optimization. The inversion module maps real images to the latent space of a well-trained StyleGAN. The visual-linguistic similarity learns the text-image matching by mapping the image and text into a common embedding space. The instance-level optimization is for identity preservation in manipulation. Our model can produce diverse and high-quality images with an unprecedented resolution at 1024 x 1024. Using a control mechanism based on style-mixing, our TediGAN inherently supports image synthesis with multi-modal inputs, such as sketches or semantic labels, with or without instance guidance. To facilitate text-guided multi-modal synthesis, we propose the Multi-Modal CelebA-HQ, a large-scale dataset consisting of real face images and corresponding semantic segmentation map, sketch, and textual descriptions. Extensive experiments on the introduced dataset demonstrate the superior performance of our proposed method. Code and data are available at https://github.com/weihaox/TediGAN. | https://openaccess.thecvf.com/content/CVPR2021/papers/Xia_TediGAN_Text-Guided_Diverse_Face_Image_Generation_and_Manipulation_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.03308 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Xia_TediGAN_Text-Guided_Diverse_Face_Image_Generation_and_Manipulation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Xia_TediGAN_Text-Guided_Diverse_Face_Image_Generation_and_Manipulation_CVPR_2021_paper.html | CVPR 2021 | null | null |
Affective Processes: Stochastic Modelling of Temporal Context for Emotion and Facial Expression Recognition | Enrique Sanchez, Mani Kumar Tellamekala, Michel Valstar, Georgios Tzimiropoulos | Temporal context is key to the recognition of expressions of emotion. Existing methods, that rely on recurrent or self-attention models to enforce temporal consistency, work on the feature level, ignoring the task-specific temporal dependencies, and fail to model context uncertainty. To alleviate these issues, we build upon the framework of Neural Processes to propose a method for apparent emotion recognition with three key novel components: (a) probabilistic contextual representation with a global latent variable model; (b) temporal context modelling using task-specific predictions in addition to features; and (c) smart temporal context selection. We validate our approach on four databases, two for Valence and Arousal estimation (SEWA and AffWild2), and two for Action Unit intensity estimation (DISFA and BP4D). Results show a consistent improvement over a series of strong baselines as well as over state-of-the-art methods. | https://openaccess.thecvf.com/content/CVPR2021/papers/Sanchez_Affective_Processes_Stochastic_Modelling_of_Temporal_Context_for_Emotion_and_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.13372 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Sanchez_Affective_Processes_Stochastic_Modelling_of_Temporal_Context_for_Emotion_and_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Sanchez_Affective_Processes_Stochastic_Modelling_of_Temporal_Context_for_Emotion_and_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Sanchez_Affective_Processes_Stochastic_CVPR_2021_supplemental.zip | null |
ID-Unet: Iterative Soft and Hard Deformation for View Synthesis | Mingyu Yin, Li Sun, Qingli Li | View synthesis is usually done by an autoencoder, in which the encoder maps a source view image into a latent content code, and the decoder transforms it into a target view image according to the condition. However, the source contents are often not well kept in this setting, which leads to unnecessary changes during the view translation. Although adding skipped connections, like Unet, alleviates the problem, but it often causes the failure on the view conformity. This paper proposes a new architecture by performing the source-to-target deformation in an iterative way. Instead of simply incorporating the features from multiple layers of the encoder, we design soft and hard deformation modules, which warp the encoder features to the target view at different resolutions, and give results to the decoder to complement the details. Particularly, the current warping flow is not only used to align the feature of the same resolution, but also as an approximation to coarsely deform the high resolution feature. Then the residual flow is estimated and applied in the high resolution, so that the deformation is built up in the coarse-to-fine fashion. To better constrain the model, we synthesize a rough target view image based on the intermediate flows and their warped features. The extensive ablation studies and the final results on two different data sets show the effectiveness of the proposed model. | https://openaccess.thecvf.com/content/CVPR2021/papers/Yin_ID-Unet_Iterative_Soft_and_Hard_Deformation_for_View_Synthesis_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Yin_ID-Unet_Iterative_Soft_and_Hard_Deformation_for_View_Synthesis_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Yin_ID-Unet_Iterative_Soft_and_Hard_Deformation_for_View_Synthesis_CVPR_2021_paper.html | CVPR 2021 | null | null |
Positional Encoding As Spatial Inductive Bias in GANs | Rui Xu, Xintao Wang, Kai Chen, Bolei Zhou, Chen Change Loy | SinGAN shows impressive capability in learning internal patch distribution despite its limited effective receptive field. We are interested in knowing how such a translation-invariant convolutional generator could capture the global structure with just a spatially i.i.d. input. In this work, taking SinGAN and StyleGAN2 as examples, we show that such capability, to a large extent, is brought by the implicit positional encoding when using zero padding in the generators. Such positional encoding is indispensable for generating images with high fidelity. The same phenomenon is observed in other generative architectures such as DCGAN and PGGAN. We further show that zero padding leads to an unbalanced spatial bias with a vague relation between locations. To offer a better spatial inductive bias, we investigate alternative positional encodings and analyze their effects. Based on a more flexible positional encoding explicitly, we propose a new multi-scale training strategy and demonstrate its effectiveness in the state-of-the-art unconditional generator StyleGAN2. Besides, the explicit spatial inductive bias substantially improve SinGAN for more versatile image manipulation. | https://openaccess.thecvf.com/content/CVPR2021/papers/Xu_Positional_Encoding_As_Spatial_Inductive_Bias_in_GANs_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.05217 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Xu_Positional_Encoding_As_Spatial_Inductive_Bias_in_GANs_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Xu_Positional_Encoding_As_Spatial_Inductive_Bias_in_GANs_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Xu_Positional_Encoding_As_CVPR_2021_supplemental.pdf | null |
Mask-ToF: Learning Microlens Masks for Flying Pixel Correction in Time-of-Flight Imaging | Ilya Chugunov, Seung-Hwan Baek, Qiang Fu, Wolfgang Heidrich, Felix Heide | We introduce Mask-ToF, a method to reduce flying pixels (FP) in time-of-flight (ToF) depth captures. FPs are pervasive artifacts which occur around depth edges, where light paths from both an object and its background are integrated over the aperture. This light mixes at a sensor pixel to produce erroneous depth estimates, which can adversely affect downstream 3D vision tasks. Mask-ToF starts at the source of these FPs, learning a microlens-level occlusion mask which effectively creates a custom-shaped sub-aperture for each sensor pixel. This modulates the selection of foreground and background light mixtures on a per-pixel basis and thereby encodes scene geometric information directly into the ToF measurements. We develop a differentiable ToF simulator to jointly train a convolutional neural network to decode this information and produce high-fidelity, low-FP depth reconstructions. We test the effectiveness of Mask-ToF on a simulated light field dataset and validate the method with an experimental prototype. To this end, we manufacture the learned amplitude mask and design an optical relay system to virtually place it on a high-resolution ToF sensor. We find that Mask-ToF generalizes well to real data without retraining, cutting FP counts in half. | https://openaccess.thecvf.com/content/CVPR2021/papers/Chugunov_Mask-ToF_Learning_Microlens_Masks_for_Flying_Pixel_Correction_in_Time-of-Flight_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Chugunov_Mask-ToF_Learning_Microlens_Masks_for_Flying_Pixel_Correction_in_Time-of-Flight_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Chugunov_Mask-ToF_Learning_Microlens_Masks_for_Flying_Pixel_Correction_in_Time-of-Flight_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Chugunov_Mask-ToF_Learning_Microlens_CVPR_2021_supplemental.pdf | null |
QPP: Real-Time Quantization Parameter Prediction for Deep Neural Networks | Vladimir Kryzhanovskiy, Gleb Balitskiy, Nikolay Kozyrskiy, Aleksandr Zuruev | Modern deep neural networks (DNNs) cannot be effectively used in mobile and embedded devices due to strict requirements for computational complexity, memory, and power consumption. The quantization of weights and feature maps (activations) is a popular approach to solve this problem. Training-aware quantization often shows excellent results but requires a full dataset, which is not always available. Post-training quantization methods, in turn, are applied without fine-tuning but still work well for many classes of tasks like classification, segmentation, and so on. However, they either imply a big overhead for quantization parameters (QPs) calculation at runtime (dynamic methods) or lead to an accuracy drop if pre-computed static QPs are used (static methods). Moreover, most inference frameworks don't support dynamic quantization. Thus we propose a novel quantization approach called QPP: quantization parameter prediction. With a small subset of a training dataset or unlabeled data from the same domain, we find the predictor that can accurately estimate QPs of activations given only the NN's input data. Such a predictor allows us to avoid complex calculation of precise values of QPs while maintaining the quality of the model. To illustrate our method's efficiency, we added QPP into two dynamic approaches: 1) Dense+Sparse quantization, where the predetermined percentage of activations are not quantized, 2) standard quantization with equal quantization steps. We provide experiments on a wide set of tasks including super-resolution, facial landmark, segmentation, and classification. | https://openaccess.thecvf.com/content/CVPR2021/papers/Kryzhanovskiy_QPP_Real-Time_Quantization_Parameter_Prediction_for_Deep_Neural_Networks_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Kryzhanovskiy_QPP_Real-Time_Quantization_Parameter_Prediction_for_Deep_Neural_Networks_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Kryzhanovskiy_QPP_Real-Time_Quantization_Parameter_Prediction_for_Deep_Neural_Networks_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Kryzhanovskiy_QPP_Real-Time_Quantization_CVPR_2021_supplemental.zip | null |
Nighttime Visibility Enhancement by Increasing the Dynamic Range and Suppression of Light Effects | Aashish Sharma, Robby T. Tan | Most existing nighttime visibility enhancement methods focus on low light. Night images, however, do not only suffer from low light, but also from man-made light effects such as glow, glare, floodlight, etc. Hence, when the existing nighttime visibility enhancement methods are applied to these images, they intensify the effects, degrading the visibility even further. High dynamic range (HDR) imaging methods can address the low light and over-exposed regions, however they cannot remove the light effects, and thus cannot enhance the visibility in the affected regions. In this paper, given a single nighttime image as input, our goal is to enhance its visibility by increasing the dynamic range of the intensity, and thus can boost the intensity of the low light regions, and at the same time, suppress the light effects (glow, glare) simultaneously. First, we use a network to estimate the camera response function (CRF) from the input image to linearise the image. Second, we decompose the linearised image into low-frequency (LF) and high-frequency (HF) feature maps that are processed separately through two networks for light effects suppression and noise removal respectively. Third, we use a network to increase the dynamic range of the processed LF feature maps, which are then combined with the processed HF feature maps to generate the final output that has increased dynamic range and suppressed light effects. Our experiments show the effectiveness of our method in comparison with the state-of-the-art nighttime visibility enhancement methods. | https://openaccess.thecvf.com/content/CVPR2021/papers/Sharma_Nighttime_Visibility_Enhancement_by_Increasing_the_Dynamic_Range_and_Suppression_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Sharma_Nighttime_Visibility_Enhancement_by_Increasing_the_Dynamic_Range_and_Suppression_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Sharma_Nighttime_Visibility_Enhancement_by_Increasing_the_Dynamic_Range_and_Suppression_CVPR_2021_paper.html | CVPR 2021 | null | null |
Self-Supervised Augmentation Consistency for Adapting Semantic Segmentation | Nikita Araslanov, Stefan Roth | We propose an approach to domain adaptation for semantic segmentation that is both practical and highly accurate. In contrast to previous work, we abandon the use of computationally involved adversarial objectives, network ensembles and style transfer. Instead, we employ standard data augmentation techniques - photometric noise, flipping and scaling - and ensure consistency of the semantic predictions across these image transformations. We develop this principle in a lightweight self-supervised framework trained on co-evolving pseudo labels without the need for cumbersome extra training rounds. Simple in training from a practitioner's standpoint, our approach is remarkably effective. We achieve significant improvements of the state-of-the-art segmentation accuracy after adaptation, consistent both across different choices of the backbone architecture and adaptation scenarios. | https://openaccess.thecvf.com/content/CVPR2021/papers/Araslanov_Self-Supervised_Augmentation_Consistency_for_Adapting_Semantic_Segmentation_CVPR_2021_paper.pdf | http://arxiv.org/abs/2105.00097 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Araslanov_Self-Supervised_Augmentation_Consistency_for_Adapting_Semantic_Segmentation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Araslanov_Self-Supervised_Augmentation_Consistency_for_Adapting_Semantic_Segmentation_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Araslanov_Self-Supervised_Augmentation_Consistency_CVPR_2021_supplemental.pdf | null |
Patch-VQ: 'Patching Up' the Video Quality Problem | Zhenqiang Ying, Maniratnam Mandal, Deepti Ghadiyaram, Alan Bovik | No-reference (NR) perceptual video quality assessment (VQA) is a complex, unsolved, and important problem for social and streaming media applications. Efficient and accurate video quality predictors are needed to monitor and guide the processing of billions of shared, often imperfect, user-generated content (UGC). Unfortunately, current NR models are limited in their prediction capabilities on real-world, "in-the-wild" UGC video data. To advance progress on this problem, we created the largest (by far) subjective video quality dataset, containing 38,811 real-world distorted videos and 116,433 space-time localized video patches ('v-patches'), and 5.5M human perceptual quality annotations. Using this, we created two unique NR-VQA models: (a) a local-to-global region-based NR VQA architecture (called PVQ) that learns to predict global video quality and achieves state-of-the-art performance on 3 UGC datasets, and (b) a first-of-a-kind space-time video quality mapping engine (called PVQ Mapper) that helps localize and visualize perceptual distortions in space and time. The entire dataset and prediction models are freely available at https://live.ece.utexas.edu/research.php. | https://openaccess.thecvf.com/content/CVPR2021/papers/Ying_Patch-VQ_Patching_Up_the_Video_Quality_Problem_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Ying_Patch-VQ_Patching_Up_the_Video_Quality_Problem_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Ying_Patch-VQ_Patching_Up_the_Video_Quality_Problem_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Ying_Patch-VQ_Patching_Up_CVPR_2021_supplemental.pdf | null |
Subsets and Splits