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 |
|---|---|---|---|---|---|---|---|---|---|---|
SMD-Nets: Stereo Mixture Density Networks | Fabio Tosi, Yiyi Liao, Carolin Schmitt, Andreas Geiger | Despite stereo matching accuracy has greatly improved by deep learning in the last few years, recovering sharp boundaries and high-resolution outputs efficiently remains challenging. In this paper, we propose Stereo Mixture Density Networks (SMD-Nets), a simple yet effective learning framework compatible with a wide class of 2D and 3D architectures which ameliorates both issues. Specifically, we exploit bimodal mixture densities as output representation and show that this allows for sharp and precise disparity estimates near discontinuities while explicitly modeling the aleatoric uncertainty inherent in the observations. Moreover, we formulate disparity estimation as a continuous problem in the image domain, allowing our model to query disparities at arbitrary spatial precision. We carry out comprehensive experiments on a new high-resolution and highly realistic synthetic stereo dataset, consisting of stereo pairs at 8Mpx resolution, as well as on real-world stereo datasets. Our experiments demonstrate increased depth accuracy near object boundaries and prediction of ultra high-resolution disparity maps on standard GPUs. We demonstrate the flexibility of our technique by improving the performance of a variety of stereo backbones. | https://openaccess.thecvf.com/content/CVPR2021/papers/Tosi_SMD-Nets_Stereo_Mixture_Density_Networks_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Tosi_SMD-Nets_Stereo_Mixture_Density_Networks_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Tosi_SMD-Nets_Stereo_Mixture_Density_Networks_CVPR_2021_paper.html | CVPR 2021 | null | null |
Discover Cross-Modality Nuances for Visible-Infrared Person Re-Identification | Qiong Wu, Pingyang Dai, Jie Chen, Chia-Wen Lin, Yongjian Wu, Feiyue Huang, Bineng Zhong, Rongrong Ji | Visible-infrared person re-identification (Re-ID) aims to match the pedestrian images of the same identity from different modalities. Existing works mainly focus on alleviating the modality discrepancy by aligning the distributions of features from different modalities. However, nuanced but discriminative information, such as glasses, shoes, and the length of clothes, has not been fully explored, especially in the infrared modality. Without discovering nuances, it is challenging to match pedestrians across modalities using modality alignment solely, which inevitably reduces feature distinctiveness. In this paper, we propose a joint Modality and Pattern Alignment Network (MPANet) to discover cross-modality nuances in different patterns for visible-infrared person Re-ID, which introduces a modality alleviation module and a pattern alignment module to jointly extract discriminative features. Specifically, we first propose a modality alleviation module to dislodge the modality information from the extracted feature maps. Then, We devise a pattern alignment module, which generates multiple pattern maps for the diverse patterns of a person, to discover nuances. Finally, we introduce a mutual mean learning fashion to alleviate the modality discrepancy and propose a center cluster loss to guide both identity learning and nuances discovering. Extensive experiments on the public SYSU-MM01 and RegDB datasets demonstrate the superiority of MPANet over state-of-the-arts. | https://openaccess.thecvf.com/content/CVPR2021/papers/Wu_Discover_Cross-Modality_Nuances_for_Visible-Infrared_Person_Re-Identification_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wu_Discover_Cross-Modality_Nuances_for_Visible-Infrared_Person_Re-Identification_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wu_Discover_Cross-Modality_Nuances_for_Visible-Infrared_Person_Re-Identification_CVPR_2021_paper.html | CVPR 2021 | null | null |
Learning Progressive Point Embeddings for 3D Point Cloud Generation | Cheng Wen, Baosheng Yu, Dacheng Tao | Generative models for 3D point clouds are extremely important for scene/object reconstruction applications in autonomous driving and robotics. Despite recent success of deep learning-based representation learning, it remains a great challenge for deep neural networks to synthesize or reconstruct high-fidelity point clouds, because of the difficulties in 1) learning effective pointwise representations; and 2) generating realistic point clouds from complex distributions. In this paper, we devise a dual-generators framework for point cloud generation, which generalizes vanilla generative adversarial learning framework in a progressive manner. Specifically, the first generator aims to learn effective point embeddings in a breadth-first manner, while the second generator is used to refine the generated point cloud based on a depth-first point embedding to generate a robust and uniform point cloud. The proposed dual-generators framework thus is able to progressively learn effective point embeddings for accurate point cloud generation. Experimental results on a variety of object categories from the most popular point cloud generation dataset, ShapeNet, demonstrate the state-of-the-art performance of the proposed method for accurate point cloud generation. | https://openaccess.thecvf.com/content/CVPR2021/papers/Wen_Learning_Progressive_Point_Embeddings_for_3D_Point_Cloud_Generation_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wen_Learning_Progressive_Point_Embeddings_for_3D_Point_Cloud_Generation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wen_Learning_Progressive_Point_Embeddings_for_3D_Point_Cloud_Generation_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wen_Learning_Progressive_Point_CVPR_2021_supplemental.pdf | null |
Learnable Graph Matching: Incorporating Graph Partitioning With Deep Feature Learning for Multiple Object Tracking | Jiawei He, Zehao Huang, Naiyan Wang, Zhaoxiang Zhang | Data association across frames is at the core of Multiple Object Tracking (MOT) task. This problem is usually solved by a traditional graph-based optimization or directly learned via deep learning. Despite their popularity, we find some points worth studying in current paradigm: 1) Existing methods mostly ignore the context information among tracklets and intra-frame detections, which makes the tracker hard to survive in challenging cases like severe occlusion. 2) The end-to-end association methods solely rely on the data fitting power of deep neural networks, while they hardly utilize the advantage of optimization-based assignment methods. 3) The graph-based optimization methods mostly utilize a separate neural network to extract features, which brings the inconsistency between training and inference. Therefore, in this paper we propose a novel learnable graph matching method to address these issues. Briefly speaking, we model the relationships between tracklets and the intra-frame detections as a general undirected graph. Then the association problem turns into a general graph matching between tracklet graph and detection graph. Furthermore, to make the optimization end-to-end differentiable, we relax the original graph matching into continuous quadratic programming and then incorporate the training of it into a deep graph network with the help of the implicit function theorem. Lastly, our method GMTracker, achieves state-of-the-art performance on several standard MOT datasets. Our code is available at https://github.com/jiaweihe1996/GMTracker. | https://openaccess.thecvf.com/content/CVPR2021/papers/He_Learnable_Graph_Matching_Incorporating_Graph_Partitioning_With_Deep_Feature_Learning_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.16178 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/He_Learnable_Graph_Matching_Incorporating_Graph_Partitioning_With_Deep_Feature_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/He_Learnable_Graph_Matching_Incorporating_Graph_Partitioning_With_Deep_Feature_Learning_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/He_Learnable_Graph_Matching_CVPR_2021_supplemental.pdf | null |
A Decomposition Model for Stereo Matching | Chengtang Yao, Yunde Jia, Huijun Di, Pengxiang Li, Yuwei Wu | In this paper, we present a decomposition model for stereo matching to solve the problem of excessive growth in computational cost (time and memory cost) as the resolution increases. In order to reduce the huge cost of stereo matching at the original resolution, our model only runs dense matching at a very low resolution and uses sparse matching at different higher resolutions to recover the disparity of lost details scale-by-scale. After the decomposition of stereo matching, our model iteratively fuses the sparse and dense disparity maps from adjacent scales with an occlusion-aware mask. A refinement network is also applied to improving the fusion result. Compared with high-performance methods like PSMNet and GANet, our method achieves 10-100x speed increase while obtaining comparable disparity estimation results. | https://openaccess.thecvf.com/content/CVPR2021/papers/Yao_A_Decomposition_Model_for_Stereo_Matching_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.07516 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Yao_A_Decomposition_Model_for_Stereo_Matching_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Yao_A_Decomposition_Model_for_Stereo_Matching_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yao_A_Decomposition_Model_CVPR_2021_supplemental.pdf | null |
RangeIoUDet: Range Image Based Real-Time 3D Object Detector Optimized by Intersection Over Union | Zhidong Liang, Zehan Zhang, Ming Zhang, Xian Zhao, Shiliang Pu | Real-time and high-performance 3D object detection is an attractive research direction in autonomous driving. Recent studies prefer point based or voxel based convolution for achieving high performance. However, these methods suffer from the unsatisfied efficiency or complex customized convolution, making them unsuitable for applications with real-time requirements. In this paper, we present an efficient and effective 3D object detection framework, named RangeIoUDet that uses the range image as input. Benefiting from the dense representation of the range image, RangeIoUDet is entirely constructed based on 2D convolution, making it possible to have a fast inference speed. This model learns pointwise features from the range image, which is then passed to a region proposal network for predicting 3D bounding boxes. We optimize the pointwise feature and the 3D box via the point-based IoU and box-based IoU supervision, respectively. The point-based IoU supervision is proposed to make the network better learn the implicit 3D information encoded in the range image. The 3D Hybrid GIoU loss is introduced to generate high-quality boxes while providing an accurate quality evaluation. Through the point-based IoU and the box-based IoU, RangeIoUDet outperforms all single-stage models on the KITTI dataset, while running at 45 FPS for inference. Experiments on the self-built dataset further prove its effectiveness on different LIDAR sensors and object categories. | https://openaccess.thecvf.com/content/CVPR2021/papers/Liang_RangeIoUDet_Range_Image_Based_Real-Time_3D_Object_Detector_Optimized_by_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Liang_RangeIoUDet_Range_Image_Based_Real-Time_3D_Object_Detector_Optimized_by_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Liang_RangeIoUDet_Range_Image_Based_Real-Time_3D_Object_Detector_Optimized_by_CVPR_2021_paper.html | CVPR 2021 | null | null |
Domain-Robust VQA With Diverse Datasets and Methods but No Target Labels | Mingda Zhang, Tristan Maidment, Ahmad Diab, Adriana Kovashka, Rebecca Hwa | The observation that computer vision methods overfit to dataset specifics has inspired diverse attempts to make object recognition models robust to domain shifts. However, similar work on domain-robust visual question answering methods is very limited. Domain adaptation for VQA differs from adaptation for object recognition due to additional complexity: VQA models handle multimodal inputs, methods contain multiple steps with diverse modules resulting in complex optimization, and answer spaces in different datasets are vastly different. To tackle these challenges, we first quantify domain shifts between popular VQA datasets, in both visual and textual space. To disentangle shifts between datasets arising from different modalities, we also construct synthetic shifts in the image and question domains separately. Second, we test the robustness of different families of VQA methods (classic two-stream, transformer, and neuro-symbolic methods) to these shifts. Third, we test the applicability of existing domain adaptation methods and devise a new one to bridge VQA domain gaps, adjusted to specific VQA models. To emulate the setting of real-world generalization, we focus on unsupervised domain adaptation and the open-ended classification task formulation. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_Domain-Robust_VQA_With_Diverse_Datasets_and_Methods_but_No_Target_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.15974 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Domain-Robust_VQA_With_Diverse_Datasets_and_Methods_but_No_Target_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Domain-Robust_VQA_With_Diverse_Datasets_and_Methods_but_No_Target_CVPR_2021_paper.html | CVPR 2021 | null | null |
(AF)2-S3Net: Attentive Feature Fusion With Adaptive Feature Selection for Sparse Semantic Segmentation Network | Ran Cheng, Ryan Razani, Ehsan Taghavi, Enxu Li, Bingbing Liu | Autonomous robotic systems and self driving cars rely on accurate perception of their surroundings as the safety of the passengers and pedestrians is the top priority. Semantic segmentation is one the essential components of environmental perception that provides semantic information of the scene. Recently, several methods have been introduced for 3D LiDAR semantic segmentation. While, they can lead to improved performance, they are either afflicted by high computational complexity, therefore are inefficient, or lack fine details of smaller instances. To alleviate this problem, we propose AF2-S3Net, an end-to-end encoder-decoder CNN network for 3D LiDAR semantic segmentation. We present a novel multi-branch attentive feature fusion module in the encoder and a unique adaptive feature selection module with feature map re-weighting in the decoder. Our AF2-S3Net fuses the voxel based learning and point-based learning into a single framework to effectively process the large 3D scene. Our experimental results show that the proposed method outperforms the state-of-the-art approaches on the large-scale nuScenes-lidarseg and SemanticKITTI benchmark, ranking 1st on both competitive public leaderboard competitions upon publication. | https://openaccess.thecvf.com/content/CVPR2021/papers/Cheng_AF2-S3Net_Attentive_Feature_Fusion_With_Adaptive_Feature_Selection_for_Sparse_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Cheng_AF2-S3Net_Attentive_Feature_Fusion_With_Adaptive_Feature_Selection_for_Sparse_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Cheng_AF2-S3Net_Attentive_Feature_Fusion_With_Adaptive_Feature_Selection_for_Sparse_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Cheng_AF2-S3Net_Attentive_Feature_CVPR_2021_supplemental.zip | null |
Towards Real-World Blind Face Restoration With Generative Facial Prior | Xintao Wang, Yu Li, Honglun Zhang, Ying Shan | Blind face restoration usually relies on facial priors, such as facial geometry prior or reference prior, to restore realistic and faithful details. However, very low-quality inputs cannot offer accurate geometric prior while high-quality references are inaccessible, limiting the applicability in real-world scenarios. In this work, we propose GFP-GAN that leverages rich and diverse priors encapsulated in a pretrained face GAN for blind face restoration. This Generative Facial Prior (GFP) is incorporated into the face restoration process via spatial feature transform layers, which allow our method to achieve a good balance of realness and fidelity. Thanks to the powerful generative facial prior and delicate designs, our GFP-GAN could jointly restore facial details and enhance colors with just a single forward pass, while GAN inversion methods require image-specific optimization at inference. Extensive experiments show that our method achieves superior performance to prior art on both synthetic and real-world datasets. | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_Towards_Real-World_Blind_Face_Restoration_With_Generative_Facial_Prior_CVPR_2021_paper.pdf | http://arxiv.org/abs/2101.04061 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Towards_Real-World_Blind_Face_Restoration_With_Generative_Facial_Prior_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Towards_Real-World_Blind_Face_Restoration_With_Generative_Facial_Prior_CVPR_2021_paper.html | CVPR 2021 | null | null |
Track To Detect and Segment: An Online Multi-Object Tracker | Jialian Wu, Jiale Cao, Liangchen Song, Yu Wang, Ming Yang, Junsong Yuan | Most online multi-object trackers perform object detection stand-alone in a neural net without any input from tracking. In this paper, we present a new online joint detection and tracking model, TraDeS (TRAck to DEtect and Segment), exploiting tracking clues to assist detection end-to-end. TraDeS infers object tracking offset by a cost volume, which is used to propagate previous object features for improving current object detection and segmentation. Effectiveness and superiority of TraDeS are shown on 4 datasets, including MOT (2D tracking), nuScenes (3D tracking), MOTS and Youtube-VIS (instance segmentation tracking). Project page: https://jialianwu.com/projects/TraDeS.html. | https://openaccess.thecvf.com/content/CVPR2021/papers/Wu_Track_To_Detect_and_Segment_An_Online_Multi-Object_Tracker_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.08808 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wu_Track_To_Detect_and_Segment_An_Online_Multi-Object_Tracker_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wu_Track_To_Detect_and_Segment_An_Online_Multi-Object_Tracker_CVPR_2021_paper.html | CVPR 2021 | null | null |
Look Before You Speak: Visually Contextualized Utterances | Paul Hongsuck Seo, Arsha Nagrani, Cordelia Schmid | While most conversational AI systems focus on textual dialogue only, conditioning utterances on visual context (when it's available) can lead to more realistic conversations. Unfortunately, a major challenge for incorporating visual context into conversational dialogue is the lack of large-scale labeled datasets. We provide a solution in the form of a new visually conditioned Future Utterance Prediction task. Our task involves predicting the next utterance in a video, using both visual frames and transcribed speech as context. By exploiting the large number of instructional videos online, we train a model to solve this task at scale, without the need for manual annotations. Leveraging recent advances in multimodal learning, our model consists of a novel co-attentional multimodal video transformer, and when trained on both textual and visual context, outperforms baselines that use textual inputs alone. Further, we demonstrate that our model trained for this task on unlabelled videos achieves state-of-the-art performance on a number of downstream VideoQA benchmarks such as MSRVTT-QA, MSVD-QA, ActivityNet-QA and How2QA. | https://openaccess.thecvf.com/content/CVPR2021/papers/Seo_Look_Before_You_Speak_Visually_Contextualized_Utterances_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.05710 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Seo_Look_Before_You_Speak_Visually_Contextualized_Utterances_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Seo_Look_Before_You_Speak_Visually_Contextualized_Utterances_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Seo_Look_Before_You_CVPR_2021_supplemental.pdf | null |
DivCo: Diverse Conditional Image Synthesis via Contrastive Generative Adversarial Network | Rui Liu, Yixiao Ge, Ching Lam Choi, Xiaogang Wang, Hongsheng Li | Conditional generative adversarial networks (cGANs) target at synthesizing diverse images given the input conditions and latent codes, but unfortunately, they usually suffer from the issue of mode collapse. Towards solving this issue, previous works mainly focused on encouraging the correlation between the latent codes and the generated images, while ignoring the relations between images generated from various latent codes. The recent MSGAN tried to encourage the diversity of the generated image but still only considers "negative" relations between the image pairs. In this paper, we propose a novel DivCo framework to properly constrain both "positive" and "negative" relations between the generated images specified in the latent space. To the best of our knowledge, this is the first attempt to use contrastive learning for diverse conditional image synthesis. A latent-augmented contrastive loss is introduced, which encourage images generated from adjacent latent codes to be similar and those generated from distinct latent codes to show low affinities. The proposed latent-augmented contrastive loss are well compatible with various cGAN architectures. Extensive experiments demonstrate the proposed DivCo could produce more diverse images than state-of-the-art methods without sacrificing visual quality in multiple settings. | https://openaccess.thecvf.com/content/CVPR2021/papers/Liu_DivCo_Diverse_Conditional_Image_Synthesis_via_Contrastive_Generative_Adversarial_Network_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.07893 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_DivCo_Diverse_Conditional_Image_Synthesis_via_Contrastive_Generative_Adversarial_Network_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_DivCo_Diverse_Conditional_Image_Synthesis_via_Contrastive_Generative_Adversarial_Network_CVPR_2021_paper.html | CVPR 2021 | null | null |
Effective Sparsification of Neural Networks With Global Sparsity Constraint | Xiao Zhou, Weizhong Zhang, Hang Xu, Tong Zhang | Weight pruning is an effective technique to reduce the model size and inference time for deep neural networks in real world deployments. However, since magnitudes and relative importance of weights are very different for different layers of a neural network, existing methods rely on either manual tuning or handcrafted heuristic rules to find appropriate pruning rates individually for each layer. This approach general leads to suboptimal performance. In this paper, by directly working on the probability space, we propose an effective network sparsification method called probabilistic masking (ProbMask), which solves a natural sparsification formulation under global sparsity constraint. The key idea is to use probability as a global criterion for all layers to measure the weight importance. An appealing feature of ProbMask is that the amounts of weight redundancy can be learned automatically via our constraint and thus we avoid the problem of tuning pruning rates individually for different layers in a network. Extensive experimental results on CIFAR-10/100 and ImageNet demonstrate that our method is highly effective, and can outperform previous state-of-the-art methods by a significant margin, especially in the high pruning rate situation. Notably, the gap of Top-1 accuracy between our ProbMask and existing methods can be up to 10%. As a by-product, we show ProbMask is also highly effective in identifying supermasks, which are subnetworks with high performance in a randomly weighted dense neural network. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhou_Effective_Sparsification_of_Neural_Networks_With_Global_Sparsity_Constraint_CVPR_2021_paper.pdf | http://arxiv.org/abs/2105.01571 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhou_Effective_Sparsification_of_Neural_Networks_With_Global_Sparsity_Constraint_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhou_Effective_Sparsification_of_Neural_Networks_With_Global_Sparsity_Constraint_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhou_Effective_Sparsification_of_CVPR_2021_supplemental.pdf | null |
Deep Gaussian Scale Mixture Prior for Spectral Compressive Imaging | Tao Huang, Weisheng Dong, Xin Yuan, Jinjian Wu, Guangming Shi | In coded aperture snapshot spectral imaging (CASSI) system, the real-world hyperspectral image (HSI) can be reconstructed from the captured compressive image in a snapshot. Model-based HSI reconstruction methods employed hand-crafted priors to solve the reconstruction problem, but most of which achieved limited success due to the poor representation capability of these hand-crafted priors. Deep learning based methods learning the mappings between the compressive images and the HSIs directly achieved much better results. Yet, it is nontrivial to design a powerful deep network heuristically for achieving satisfied results. In this paper, we propose a novel HSI reconstruction method based on the Maximum a Posterior (MAP) estimation framework using learned Gaussian Scale Mixture (GSM) prior. Different from existing GSM models using hand-crafted scale priors (e.g., the Jeffrey's prior), we propose to learn the scale prior through a deep convolutional neural network (DCNN). Furthermore, we also propose to estimate the local means of the GSM models by the DCNN. All the parameters of the MAP estimation algorithm and the DCNN parameters are jointly optimized through end-to-end training. Extensive experimental results on both synthetic and real datasets demonstrate that the proposed method outperforms existing state-of-the-art methods. The code is available at https://see.xidian.edu.cn/faculty/wsdong/Projects/DGSM-SCI.htm. | https://openaccess.thecvf.com/content/CVPR2021/papers/Huang_Deep_Gaussian_Scale_Mixture_Prior_for_Spectral_Compressive_Imaging_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.07152 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Huang_Deep_Gaussian_Scale_Mixture_Prior_for_Spectral_Compressive_Imaging_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Huang_Deep_Gaussian_Scale_Mixture_Prior_for_Spectral_Compressive_Imaging_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Huang_Deep_Gaussian_Scale_CVPR_2021_supplemental.pdf | null |
Cross-Domain Gradient Discrepancy Minimization for Unsupervised Domain Adaptation | Zhekai Du, Jingjing Li, Hongzu Su, Lei Zhu, Ke Lu | Unsupervised Domain Adaptation (UDA) aims to generalize the knowledge learned from a well-labeled source domain to an unlabled target domain. Recently, adversarial domain adaptation with two distinct classifiers (bi-classifier) has been introduced into UDA which is effective to align distributions between different domains. Previous bi-classifier adversarial learning methods only focus on the similarity between the outputs of two distinct classifiers. However, the similarity of the outputs cannot guarantee the accuracy of target samples, i.e., traget samples may match to wrong categories even if the discrepancy between two classifiers is small. To challenge this issue, in this paper, we propose a cross-domain gradient discrepancy minimization (CGDM) method which explicitly minimizes the discrepancy of gradients generated by source samples and target samples. Specifically, the gradient gives a cue for the semantic information of target samples so it can be used as a good supervision to improve the accuracy of target samples. In order to compute the gradient signal of target smaples, we further obtain target pseudo labels through a clustering-based self-supervised learning. Extensive experiments on three widely used UDA datasets show that our method surpasses many previous state-of-the-arts. | https://openaccess.thecvf.com/content/CVPR2021/papers/Du_Cross-Domain_Gradient_Discrepancy_Minimization_for_Unsupervised_Domain_Adaptation_CVPR_2021_paper.pdf | http://arxiv.org/abs/2106.04151 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Du_Cross-Domain_Gradient_Discrepancy_Minimization_for_Unsupervised_Domain_Adaptation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Du_Cross-Domain_Gradient_Discrepancy_Minimization_for_Unsupervised_Domain_Adaptation_CVPR_2021_paper.html | CVPR 2021 | null | null |
DISCO: Dynamic and Invariant Sensitive Channel Obfuscation for Deep Neural Networks | Abhishek Singh, Ayush Chopra, Ethan Garza, Emily Zhang, Praneeth Vepakomma, Vivek Sharma, Ramesh Raskar | Recent deep learning models have shown remarkable performance in image classification. While these deep learning systems are getting closer to practical deployment, the common assumption made about data is that it does not carry any sensitive information. This assumption may not hold for many practical cases, especially in the domain where an individual's personal information is involved, like healthcare and facial recognition systems. We posit that selectively removing features in this latent space can protect the sensitive information and provide better privacy-utility trade-off. Consequently, we propose DISCO which learns a dynamic and data driven pruning filter to selectively obfuscate sensitive information in the feature space. We propose diverse attack schemes for sensitive inputs and attributes and demonstrate the effectiveness of DISCO against state-of-the-art methods through quantitative and qualitative evaluation. Finally, we also release an evaluation benchmark dataset of 1 million sensitive representations to encourage rigorous exploration of novel attack and defense schemes at https://github.com/splitlearning/InferenceBenchmark | https://openaccess.thecvf.com/content/CVPR2021/papers/Singh_DISCO_Dynamic_and_Invariant_Sensitive_Channel_Obfuscation_for_Deep_Neural_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.11025 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Singh_DISCO_Dynamic_and_Invariant_Sensitive_Channel_Obfuscation_for_Deep_Neural_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Singh_DISCO_Dynamic_and_Invariant_Sensitive_Channel_Obfuscation_for_Deep_Neural_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Singh_DISCO_Dynamic_and_CVPR_2021_supplemental.pdf | null |
Training Generative Adversarial Networks in One Stage | Chengchao Shen, Youtan Yin, Xinchao Wang, Xubin Li, Jie Song, Mingli Song | Generative Adversarial Networks (GANs) have demonstrated unprecedented success in various image generation tasks. The encouraging results, however, come at the price of a cumbersome training process, during which the generator and discriminator are alternately updated in two stages. In this paper, we investigate a general training scheme that enables training GANs efficiently in only one stage. Based on the adversarial losses of the generator and discriminator, we categorize GANs into two classes, Symmetric GANs and Asymmetric GANs, and introduce a novel gradient decomposition method to unify the two, allowing us to train both classes in one stage and hence alleviate the training effort. We also computationally analyze the efficiency of the proposed method, and empirically demonstrate that, the proposed method yields a solid: 1.5x acceleration across various datasets and network architectures. Furthermore, we show that the proposed method is readily applicable to other adversarial-training scenarios, such as data-free knowledge distillation. The code is available at https://github.com/zju-vipa/OSGAN. | https://openaccess.thecvf.com/content/CVPR2021/papers/Shen_Training_Generative_Adversarial_Networks_in_One_Stage_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.00430 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Shen_Training_Generative_Adversarial_Networks_in_One_Stage_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Shen_Training_Generative_Adversarial_Networks_in_One_Stage_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Shen_Training_Generative_Adversarial_CVPR_2021_supplemental.pdf | null |
Learning To Aggregate and Personalize 3D Face From In-the-Wild Photo Collection | Zhenyu Zhang, Yanhao Ge, Renwang Chen, Ying Tai, Yan Yan, Jian Yang, Chengjie Wang, Jilin Li, Feiyue Huang | Non-prior face modeling aims to reconstruct 3D face only from images without shape assumptions. While plausible facial details are predicted, the models tend to over-depend on local color appearance and suffer from ambiguous noise. To address such problem, this paper presents a novel Learning to Aggregate and Personalize (LAP) framework for unsupervised robust 3D face modeling. Instead of using controlled environment, the proposed method implicitly disentangles ID-consistent and scene-specific face from unconstrained photo set. Specifically, to learn ID-consistent face, LAP adaptively aggregates intrinsic face factors of an identity based on a novel curriculum learning approach with relaxed consistency loss. To adapt the face for a personalized scene, we propose a novel attribute-refining network to modify ID-consistent face with target attribute and details. Based on the proposed method, we make unsupervised 3D face modeling benefit from meaningful image facial structure and possibly higher resolutions. Extensive experiments on benchmarks show LAP recovers superior or competitive face shape and texture, compared with state-of-the-art (SOTA) methods with or without prior and supervision. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_Learning_To_Aggregate_and_Personalize_3D_Face_From_In-the-Wild_Photo_CVPR_2021_paper.pdf | http://arxiv.org/abs/2106.07852 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Learning_To_Aggregate_and_Personalize_3D_Face_From_In-the-Wild_Photo_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Learning_To_Aggregate_and_Personalize_3D_Face_From_In-the-Wild_Photo_CVPR_2021_paper.html | CVPR 2021 | null | null |
Leveraging Line-Point Consistence To Preserve Structures for Wide Parallax Image Stitching | Qi Jia, ZhengJun Li, Xin Fan, Haotian Zhao, Shiyu Teng, Xinchen Ye, Longin Jan Latecki | Generating high-quality stitched images with natural structures is a challenging task in computer vision. In this paper, we succeed in preserving both local and global geometric structures for wide parallax images, while reducing artifacts and distortions. A projective invariant, Characteristic Number, is used to match co-planar local sub-regions for input images. The homography between these well-matched sub-regions produces consistent line and point pairs, suppressing artifacts in overlapping areas. We explore and introduce global collinear structures into an objective function to specify and balance the desired characters for image warping, which can preserve both local and global structures while alleviating distortions. We also develop comprehensive measures for stitching quality to quantify the collinearity of points and the discrepancy of matched line pairs by considering the sensitivity to linear structures for human vision. Extensive experiments demonstrate the superior performance of the proposed method over the state-of-the-art by presenting sharp textures and preserving prominent natural structures in stitched images. Especially, our method not only exhibits lower errors but also the least divergence across all test images. Code is available at https://github.com/dut-media-lab/Image-Stitching | https://openaccess.thecvf.com/content/CVPR2021/papers/Jia_Leveraging_Line-Point_Consistence_To_Preserve_Structures_for_Wide_Parallax_Image_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Jia_Leveraging_Line-Point_Consistence_To_Preserve_Structures_for_Wide_Parallax_Image_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Jia_Leveraging_Line-Point_Consistence_To_Preserve_Structures_for_Wide_Parallax_Image_CVPR_2021_paper.html | CVPR 2021 | null | null |
3DIoUMatch: Leveraging IoU Prediction for Semi-Supervised 3D Object Detection | He Wang, Yezhen Cong, Or Litany, Yue Gao, Leonidas J. Guibas | 3D object detection is an important yet demanding task that heavily relies on difficult to obtain 3D annotations. To reduce the required amount of supervision, we propose 3DIoUMatch, a novel semi-supervised method for 3D object detection applicable to both indoor and outdoor scenes. We leverage a teacher-student mutual learning framework to propagate information from the labeled to the unlabeled train set in the form of pseudo-labels. However, due to the high task complexity, we observe that the pseudo-labels suffer from significant noise and are thus not directly usable. To that end, we introduce a confidence-based filtering mechanism, inspired by FixMatch. We set confidence thresholds based upon the predicted objectness and class probability to filter low-quality pseudo-labels. While effective, we observe that these two measures do not sufficiently capture localization quality. We therefore propose to use the estimated 3D IoU as a localization metric and set category-aware self-adjusted thresholds to filter poorly localized proposals. We adopt VoteNet as our backbone detector on indoor datasets while we use PV-RCNN on the autonomous driving dataset, KITTI. Our method consistently improves state-of-the-art methods on both ScanNet and SUN-RGBD benchmarks by significant margins under all label ratios (including fully labeled setting). For example, when training using only 10% labeled data on ScanNet, 3DIoUMatch achieves 7.7 absolute improvement on [email protected] and 8.5 absolute improvement on [email protected] upon the prior art. On KITTI, we are the first to demonstrate semi-supervised 3D object detection and our method surpasses a fully supervised baseline from 1.8% to 7.6% under different label ratio and categories. | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_3DIoUMatch_Leveraging_IoU_Prediction_for_Semi-Supervised_3D_Object_Detection_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.04355 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_3DIoUMatch_Leveraging_IoU_Prediction_for_Semi-Supervised_3D_Object_Detection_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_3DIoUMatch_Leveraging_IoU_Prediction_for_Semi-Supervised_3D_Object_Detection_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wang_3DIoUMatch_Leveraging_IoU_CVPR_2021_supplemental.pdf | null |
Self-Supervised Video GANs: Learning for Appearance Consistency and Motion Coherency | Sangeek Hyun, Jihwan Kim, Jae-Pil Heo | A video can be represented by the composition of appearance and motion. Appearance (or content) expresses the information invariant throughout time, and motion describes the time-variant movement. Here, we propose self-supervised approaches for video Generative Adversarial Networks (GANs) to achieve the appearance consistency and motion coherency in videos. Specifically, the dual discriminators for image and video individually learn to solve their own pretext tasks; appearance contrastive learning and temporal structure puzzle. The proposed tasks enable the discriminators to learn representations of appearance and temporal context, and force the generator to synthesize videos with consistent appearance and natural flow of motions. Extensive experiments in facial expression and human action public benchmarks show that our method outperforms the state-of-the-art video GANs. Moreover, consistent improvements regardless of the architecture of video GANs confirm that our framework is generic. | https://openaccess.thecvf.com/content/CVPR2021/papers/Hyun_Self-Supervised_Video_GANs_Learning_for_Appearance_Consistency_and_Motion_Coherency_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Hyun_Self-Supervised_Video_GANs_Learning_for_Appearance_Consistency_and_Motion_Coherency_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Hyun_Self-Supervised_Video_GANs_Learning_for_Appearance_Consistency_and_Motion_Coherency_CVPR_2021_paper.html | CVPR 2021 | null | null |
Neural Lumigraph Rendering | Petr Kellnhofer, Lars C. Jebe, Andrew Jones, Ryan Spicer, Kari Pulli, Gordon Wetzstein | Novel view synthesis is a challenging and ill-posed inverse rendering problem. Neural rendering techniques have recently achieved photorealistic image quality for this task. State-of-the-art (SOTA) neural volume rendering approaches, however, are slow to train and require minutes of inference (i.e., rendering) time for high image resolutions. We adopt high-capacity neural scene representations with periodic activations for jointly optimizing an implicit surface and a radiance field of a scene supervised exclusively with posed 2D images. Our neural rendering pipeline accelerates SOTA neural volume rendering by about two orders of magnitude and our implicit surface representation is unique in allowing us to export a mesh with view-dependent texture information. Thus, like other implicit surface representations, ours is compatible with traditional graphics pipelines, enabling real-time rendering rates, while achieving unprecedented image quality compared to other surface methods. We assess the quality of our approach using existing datasets as well as high-quality 3D face data captured with a custom multi-camera rig. | https://openaccess.thecvf.com/content/CVPR2021/papers/Kellnhofer_Neural_Lumigraph_Rendering_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.11571 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Kellnhofer_Neural_Lumigraph_Rendering_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Kellnhofer_Neural_Lumigraph_Rendering_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Kellnhofer_Neural_Lumigraph_Rendering_CVPR_2021_supplemental.pdf | null |
Robust Multimodal Vehicle Detection in Foggy Weather Using Complementary Lidar and Radar Signals | Kun Qian, Shilin Zhu, Xinyu Zhang, Li Erran Li | Vehicle detection with visual sensors like lidar and camera is one of the critical functions enabling autonomous driving. While they generate fine-grained point clouds or high-resolution images with rich information in good weather conditions, they fail in adverse weather (e.g., fog) where opaque particles distort lights and significantly reduce visibility. Thus, existing methods relying on lidar or camera experience significant performance degradation in rare but critical adverse weather conditions. To remedy this, we resort to exploiting complementary radar, which is less impacted by adverse weather and becomes prevalent on vehicles. In this paper, we present Multimodal Vehicle Detection Network (MVDNet), a two-stage deep fusion detector, which first generates proposals from two sensors and then fuses region-wise features between multimodal sensor streams to improve final detection results. To evaluate MVDNet, we create a procedurally generated training dataset based on the collected raw lidar and radar signals from the open-source Oxford Radar Robotcar. We show that the proposed MVDNet surpasses other state-of-the-art methods, notably in terms of Average Precision (AP), especially in adverse weather conditions. The code and data are available at https://github.com/qiank10/MVDNet. | https://openaccess.thecvf.com/content/CVPR2021/papers/Qian_Robust_Multimodal_Vehicle_Detection_in_Foggy_Weather_Using_Complementary_Lidar_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Qian_Robust_Multimodal_Vehicle_Detection_in_Foggy_Weather_Using_Complementary_Lidar_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Qian_Robust_Multimodal_Vehicle_Detection_in_Foggy_Weather_Using_Complementary_Lidar_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Qian_Robust_Multimodal_Vehicle_CVPR_2021_supplemental.pdf | null |
Stochastic Whitening Batch Normalization | Shengdong Zhang, Ehsan Nezhadarya, Homa Fashandi, Jiayi Liu, Darin Graham, Mohak Shah | Batch Normalization (BN) is a popular technique for training Deep Neural Networks (DNNs). BN uses scaling and shifting to normalize activations of mini-batches to accelerate convergence and improve generalization. The recently proposed Iterative Normalization (IterNorm) method improves these properties by whitening the activations iteratively using Newton's method. However, since Newton's method initializes the whitening matrix independently at each training step, no information is shared between consecutive steps. In this work, instead of exact computation of whitening matrix at each time step, we estimate it gradually during training in an online fashion, using our proposed Stochastic Whitening Batch Normalization (SWBN) algorithm. We show that while SWBN improves the convergence rate and generalization of DNNs, its computational overhead is less than that of IterNorm. Due to the high efficiency of the proposed method, it can be easily employed in most DNN architectures with a large number of layers. We provide comprehensive experiments and comparisons between BN, IterNorm, and SWBN layers to demonstrate the effectiveness of the proposed technique in conventional (many-shot) image classification and few-shot classification tasks. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_Stochastic_Whitening_Batch_Normalization_CVPR_2021_paper.pdf | http://arxiv.org/abs/2106.04413 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Stochastic_Whitening_Batch_Normalization_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Stochastic_Whitening_Batch_Normalization_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhang_Stochastic_Whitening_Batch_CVPR_2021_supplemental.pdf | null |
Self-Guided and Cross-Guided Learning for Few-Shot Segmentation | Bingfeng Zhang, Jimin Xiao, Terry Qin | Few-shot segmentation has been attracting a lot of attention due to its effectiveness to segment unseen object classes with a few annotated samples. Most existing approaches use masked Global Average Pooling (GAP) to encode an annotated support image to a feature vector to facilitate query image segmentation. However, this pipeline unavoidably loses some discriminative information due to the average operation. In this paper, we propose a simple but effective self-guided learning approach, where the lost critical information is mined. Specifically, through making an initial prediction for the annotated support image, the covered and uncovered foreground regions are encoded to the primary and auxiliary support vectors using masked GAP, respectively. By aggregating both the primary and auxiliary support vectors, better segmentation performance is obtained on query images. Enlightened by our self-guided module for 1-shot segmentation, we propose a cross-guided module for multiple shot segmentation, where the final mask is fused using predictions from multiple annotated samples with high-quality support vectors contributing more and vice versa. This module improves the final prediction in the inference stage without re-training. Extensive experiments show that our approach achieves new state-of-the-art performances on both PASCAL-5i and COCO-20i datasets. Source code will be released once the paper is accepted. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_Self-Guided_and_Cross-Guided_Learning_for_Few-Shot_Segmentation_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.16129 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Self-Guided_and_Cross-Guided_Learning_for_Few-Shot_Segmentation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Self-Guided_and_Cross-Guided_Learning_for_Few-Shot_Segmentation_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhang_Self-Guided_and_Cross-Guided_CVPR_2021_supplemental.pdf | null |
M3P: Learning Universal Representations via Multitask Multilingual Multimodal Pre-Training | Minheng Ni, Haoyang Huang, Lin Su, Edward Cui, Taroon Bharti, Lijuan Wang, Dongdong Zhang, Nan Duan | We present M3P, a Multitask Multilingual Multimodal Pre-trained model that combines multilingual pre-training and multimodal pre-training into a unified framework via multitask pre-training. Our goal is to learn universal representations that can map objects occurred in different modalities or texts expressed in different languages into a common semantic space. In addition, to explicitly encourage fine-grained alignment between images and non-English languages, we also propose Multimodal Code-switched Training (MCT) to combine monolingual pre-training and multimodal pre-training via a code-switch strategy. Experiments are performed on the multilingual image retrieval task across two benchmark datasets, including MSCOCO and Multi30K. M3P can achieve comparable results for English and new state-of-the-art results for non-English languages. | https://openaccess.thecvf.com/content/CVPR2021/papers/Ni_M3P_Learning_Universal_Representations_via_Multitask_Multilingual_Multimodal_Pre-Training_CVPR_2021_paper.pdf | http://arxiv.org/abs/2006.02635 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Ni_M3P_Learning_Universal_Representations_via_Multitask_Multilingual_Multimodal_Pre-Training_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Ni_M3P_Learning_Universal_Representations_via_Multitask_Multilingual_Multimodal_Pre-Training_CVPR_2021_paper.html | CVPR 2021 | null | null |
Hyperdimensional Computing as a Framework for Systematic Aggregation of Image Descriptors | Peer Neubert, Stefan Schubert | Image and video descriptors are an omnipresent tool in computer vision and its application fields like mobile robotics. Many hand-crafted and in particular learned image descriptors are numerical vectors with a potentially (very) large number of dimensions. Practical considerations like memory consumption or time for comparisons call for the creation of compact representations. In this paper, we use hyperdimensional computing (HDC) as an approach to systematically combine information from a set of vectors in a single vector of the same dimensionality. HDC is a known technique to perform symbolic processing with distributed representations in numerical vectors with thousands of dimensions. We present a HDC implementation that is suitable for processing the output of existing and future (deep learning based) image descriptors. We discuss how this can be used as a framework to process descriptors together with additional knowledge by simple and fast vector operations. A concrete outcome is a novel HDC-based approach to aggregate a set of local image descriptors together with their image positions in a single holistic descriptor. The comparison to available holistic descriptors and aggregation methods on a series of standard mobile robotics place recognition experiments shows a 20% improvement in average performance and >2x better worst-case performance compared to runner-up. | https://openaccess.thecvf.com/content/CVPR2021/papers/Neubert_Hyperdimensional_Computing_as_a_Framework_for_Systematic_Aggregation_of_Image_CVPR_2021_paper.pdf | http://arxiv.org/abs/2101.07720 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Neubert_Hyperdimensional_Computing_as_a_Framework_for_Systematic_Aggregation_of_Image_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Neubert_Hyperdimensional_Computing_as_a_Framework_for_Systematic_Aggregation_of_Image_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Neubert_Hyperdimensional_Computing_as_CVPR_2021_supplemental.pdf | null |
Layerwise Optimization by Gradient Decomposition for Continual Learning | Shixiang Tang, Dapeng Chen, Jinguo Zhu, Shijie Yu, Wanli Ouyang | Deep neural networks achieve state-of-the-art and sometimes super-human performance across a variety of domains. However, when learning tasks sequentially, the networks easily forget the knowledge of previous tasks, known as "catastrophic forgetting". To achieve the consistencies between the old tasks and the new task, one effective solution is to modify the gradient for update. Previous methods enforce independent gradient constraints for different tasks, while we consider these gradients contain complex information, and propose to leverage inter-task information by gradient decomposition. In particular, the gradient of an old task is decomposed into a part shared by all old tasks and a part specific to that task. The gradient for update should be close to the gradient of the new task, consistent with the gradients shared by all old tasks, and orthogonal to the space spanned by the gradients specific to the old tasks. In this way, our approach will encourage common knowledge consolidation but will not impair the task-specific knowledge. Furthermore, the optimization is performed for the gradients of each layer separately rather than the concatenation of all gradients as in previous works. This effectively avoids the influence of the magnitude variation of the gradients in different layers. Extensive experiments validate the effectiveness of both gradient-decomposed optimization and layer-wise updates. Our proposed method achieves state-of-the-art results on various benchmarks of continual learning. | https://openaccess.thecvf.com/content/CVPR2021/papers/Tang_Layerwise_Optimization_by_Gradient_Decomposition_for_Continual_Learning_CVPR_2021_paper.pdf | http://arxiv.org/abs/2105.07561 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Tang_Layerwise_Optimization_by_Gradient_Decomposition_for_Continual_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Tang_Layerwise_Optimization_by_Gradient_Decomposition_for_Continual_Learning_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Tang_Layerwise_Optimization_by_CVPR_2021_supplemental.pdf | null |
Boosting Monocular Depth Estimation Models to High-Resolution via Content-Adaptive Multi-Resolution Merging | S. Mahdi H. Miangoleh, Sebastian Dille, Long Mai, Sylvain Paris, Yagiz Aksoy | Neural networks have shown great abilities in estimating depth from a single image. However, the inferred depth maps are well below one-megapixel resolution and often lack fine-grained details, which limits their practicality. Our method builds on our analysis on how the input resolution and the scene structure affects depth estimation performance. We demonstrate that there is a trade-off between a consistent scene structure and the high-frequency details, and merge low- and high-resolution estimations to take advantage of this duality using a simple depth merging network. We present a double estimation method that improves the whole-image depth estimation and a patch selection method that adds local details to the final result. We demonstrate that by merging estimations at different resolutions with changing context, we can generate multi-megapixel depth maps with a high level of detail using a pre-trained model. | https://openaccess.thecvf.com/content/CVPR2021/papers/Miangoleh_Boosting_Monocular_Depth_Estimation_Models_to_High-Resolution_via_Content-Adaptive_Multi-Resolution_CVPR_2021_paper.pdf | http://arxiv.org/abs/2105.14021 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Miangoleh_Boosting_Monocular_Depth_Estimation_Models_to_High-Resolution_via_Content-Adaptive_Multi-Resolution_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Miangoleh_Boosting_Monocular_Depth_Estimation_Models_to_High-Resolution_via_Content-Adaptive_Multi-Resolution_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Miangoleh_Boosting_Monocular_Depth_CVPR_2021_supplemental.zip | null |
Blind Deblurring for Saturated Images | Liang Chen, Jiawei Zhang, Songnan Lin, Faming Fang, Jimmy S. Ren | Blind deblurring has received considerable attention in recent years. However, state-of-the-art methods often fail to process saturated blurry images. The main reason is that saturated pixels are not conforming to the commonly used linear blur model. Pioneer arts suggest excluding saturated pixels during the deblurring process, which sacrifices the informative edges from saturated regions and results in insufficient information for kernel estimation when large saturated regions exist. To address this problem, we introduce a new blur model to fit both saturated and unsaturated pixels, and all informative pixels can be considered during deblurring process. Based on our model, we develop an effective maximum a posterior (MAP)-based optimization framework. Quantitative and qualitative evaluations on benchmark datasets and challenging real-world examples show that the proposed method performs favorably against existing methods. | https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_Blind_Deblurring_for_Saturated_Images_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Blind_Deblurring_for_Saturated_Images_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Blind_Deblurring_for_Saturated_Images_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Chen_Blind_Deblurring_for_CVPR_2021_supplemental.pdf | null |
Turning Frequency to Resolution: Video Super-Resolution via Event Cameras | Yongcheng Jing, Yiding Yang, Xinchao Wang, Mingli Song, Dacheng Tao | State-of-the-art video super-resolution (VSR) methods focus on exploiting inter- and intra-frame correlations to estimate high-resolution (HR) video frames from low-resolution (LR) ones. In this paper, we study VSR from an exotic perspective, by explicitly looking into the role of temporal frequency of video frames. Through experiments, we observe that a higher frequency, and hence a smaller pixel displacement between consecutive frames, tends to deliver favorable super-resolved results. This discovery motivates us to introduce Event Cameras, a novel sensing device that responds instantly to pixel intensity changes and produces up to millions of asynchronous events per second, to facilitate VSR. To this end, we propose an Event-based VSR framework (E-VSR), of which the key component is an asynchronous interpolation (EAI) module that reconstructs a high-frequency (HF) video stream with uniform and tiny pixel displacements between neighboring frames from an event stream. The derived HF video stream is then encoded into a VSR module to recover the desired HR videos. Furthermore, an LR bi-directional interpolation loss and an HR self-supervision loss are also introduced to respectively regulate the EAI and VSR modules. Experiments on both real-world and synthetic datasets demonstrate that the proposed approach yields results superior to the state of the art. | https://openaccess.thecvf.com/content/CVPR2021/papers/Jing_Turning_Frequency_to_Resolution_Video_Super-Resolution_via_Event_Cameras_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Jing_Turning_Frequency_to_Resolution_Video_Super-Resolution_via_Event_Cameras_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Jing_Turning_Frequency_to_Resolution_Video_Super-Resolution_via_Event_Cameras_CVPR_2021_paper.html | CVPR 2021 | null | null |
Time Adaptive Recurrent Neural Network | Anil Kag, Venkatesh Saligrama | We propose a learning method that, dynamically modifies the time-constants of the continuous-time counterpart of a vanilla RNN. The time-constants are modified based on the current observation and hidden state. Our proposal overcomes the issues of RNN trainability, by mitigating exploding and vanishing gradient phenomena based on placing novel constraints on the parameter space, and by suppressing noise in inputs based on pondering over informative inputs to strengthen their contribution in the hidden state. As a result, our method is computationally efficient overcoming overheads of many existing methods that also attempt to improve RNN training. Our RNNs, despite being simpler and having light memory footprint, shows competitive performance against standard LSTMs and baseline RNN models on many benchmark datasets including those that require long-term memory. | https://openaccess.thecvf.com/content/CVPR2021/papers/Kag_Time_Adaptive_Recurrent_Neural_Network_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Kag_Time_Adaptive_Recurrent_Neural_Network_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Kag_Time_Adaptive_Recurrent_Neural_Network_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Kag_Time_Adaptive_Recurrent_CVPR_2021_supplemental.pdf | null |
DeFMO: Deblurring and Shape Recovery of Fast Moving Objects | Denys Rozumnyi, Martin R. Oswald, Vittorio Ferrari, Jiri Matas, Marc Pollefeys | Objects moving at high speed appear significantly blurred when captured with cameras. The blurry appearance is especially ambiguous when the object has complex shape or texture. In such cases, classical methods, or even humans, are unable to recover the object's appearance and motion. We propose a method that, given a single image with its estimated background, outputs the object's appearance and position in a series of sub-frames as if captured by a high-speed camera (i.e. temporal super-resolution). The proposed generative model embeds an image of the blurred object into a latent space representation, disentangles the background, and renders the sharp appearance. Inspired by the image formation model, we design novel self-supervised loss function terms that boost performance and show good generalization capabilities. The proposed DeFMO method is trained on a complex synthetic dataset, yet it performs well on real-world data from several datasets. DeFMO outperforms the state of the art and generates high-quality temporal super-resolution frames. | https://openaccess.thecvf.com/content/CVPR2021/papers/Rozumnyi_DeFMO_Deblurring_and_Shape_Recovery_of_Fast_Moving_Objects_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.00595 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Rozumnyi_DeFMO_Deblurring_and_Shape_Recovery_of_Fast_Moving_Objects_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Rozumnyi_DeFMO_Deblurring_and_Shape_Recovery_of_Fast_Moving_Objects_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Rozumnyi_DeFMO_Deblurring_and_CVPR_2021_supplemental.zip | null |
PISE: Person Image Synthesis and Editing With Decoupled GAN | Jinsong Zhang, Kun Li, Yu-Kun Lai, Jingyu Yang | Person image synthesis, e.g., pose transfer, is a challenging problem due to large variation and occlusion. Existing methods have difficulties predicting reasonable invisible regions and fail to decouple the shape and style of clothing, which limits their applications on person image editing. In this paper, we propose PISE, a novel two-stage generative model for person image synthesis and editing, which can generate realistic person images with desired poses, textures, and semantic layouts. To better predict the invisible region, we first synthesize a human parsing map aligned with the target pose to represent the shape of clothing by a parsing generator, and then generate the final image by an image generator. To decouple the shape and style of clothing, we propose joint global and local per-region encoding and normalization to predict the reasonable style of clothing for invisible regions. We also propose spatial-aware normalization to retain the spatial context relationship in the source image. The results of qualitative and quantitative experiments demonstrate the superiority of our model. Besides, the results of texture transfer and parsing editing show that our model can be applied to person image editing. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_PISE_Person_Image_Synthesis_and_Editing_With_Decoupled_GAN_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.04023 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_PISE_Person_Image_Synthesis_and_Editing_With_Decoupled_GAN_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_PISE_Person_Image_Synthesis_and_Editing_With_Decoupled_GAN_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhang_PISE_Person_Image_CVPR_2021_supplemental.pdf | null |
4D Hyperspectral Photoacoustic Data Restoration With Reliability Analysis | Weihang Liao, Art Subpa-asa, Yinqiang Zheng, Imari Sato | Hyperspectral photoacoustic (HSPA) spectroscopy is an emerging bi-modal imaging technology that is able to show the wavelength-dependent absorption distribution of the interior of a 3D volume. However, HSPA devices have to scan an object exhaustively in the spatial and spectral domains; and the acquired data tend to suffer from complex noise. This time-consuming scanning process and noise severely affects the usability of HSPA. It is therefore critical to examine the feasibility of 4D HSPA data restoration from an incomplete and noisy observation. In this work, we present a data reliability analysis for the depth and spectral domain. On the basis of this analysis, we explore the inherent data correlations and develop a restoration algorithm to recover 4D HSPA cubes. Experiments on real data verify that the proposed method achieves satisfactory restoration results. | https://openaccess.thecvf.com/content/CVPR2021/papers/Liao_4D_Hyperspectral_Photoacoustic_Data_Restoration_With_Reliability_Analysis_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Liao_4D_Hyperspectral_Photoacoustic_Data_Restoration_With_Reliability_Analysis_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Liao_4D_Hyperspectral_Photoacoustic_Data_Restoration_With_Reliability_Analysis_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Liao_4D_Hyperspectral_Photoacoustic_CVPR_2021_supplemental.pdf | null |
OBoW: Online Bag-of-Visual-Words Generation for Self-Supervised Learning | Spyros Gidaris, Andrei Bursuc, Gilles Puy, Nikos Komodakis, Matthieu Cord, Patrick Perez | Learning image representations without human supervision is an important and active research field. Several recent approaches have successfully leveraged the idea of making such a representation invariant under different types of perturbations, especially via contrastive-based instance discrimination training. Although effective visual representations should indeed exhibit such invariances, there are other important characteristics, such as encoding contextual reasoning skills, for which alternative reconstruction-based approaches might be better suited. With this in mind, we propose a teacher-student scheme to learn representations by training a convolutional net to reconstruct a bag-of-visual-words (BoW) representation of an image, given as input a perturbed version of that same image. Our strategy performs an online training of both the teacher network (whose role is to generate the BoW targets) and the student network (whose role is to learn representations), along with an online update of the visual-words vocabulary (used for the BoW targets). This idea effectively enables fully online BoW-guided unsupervised learning. Extensive experiments demonstrate the interest of our BoW-based strategy, which surpasses previous state-of-the-art methods (including contrastive-based ones) in several applications. For instance, in downstream tasks such Pascal object detection, Pascal classification and Places205 classification, our method improves over all prior unsupervised approaches, thus establishing new state-of-the-art results that are also significantly better even than those of supervised pre-training. We provide the implementation code at https://github.com/valeoai/obow. | https://openaccess.thecvf.com/content/CVPR2021/papers/Gidaris_OBoW_Online_Bag-of-Visual-Words_Generation_for_Self-Supervised_Learning_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Gidaris_OBoW_Online_Bag-of-Visual-Words_Generation_for_Self-Supervised_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Gidaris_OBoW_Online_Bag-of-Visual-Words_Generation_for_Self-Supervised_Learning_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Gidaris_OBoW_Online_Bag-of-Visual-Words_CVPR_2021_supplemental.pdf | null |
Learning-Based Image Registration With Meta-Regularization | Ebrahim Al Safadi, Xubo Song | We introduce a meta-regularization framework for learning-based image registration. Current learning-based image registration methods use high-resolution architectures such as U-Nets to produce spatial transformations, and impose simple and explicit regularization on the output of the network to ensure that the estimated displacements are smooth. While this approach works well on small deformations, it has been known to struggle when the deformations are large. Our method uses a more advanced form of meta-regularization to increase the generalization ability of learned registration models. We motivate our approach based on Reproducing Kernel Hilbert Space (RKHS) theory, and approximate that framework via a meta-regularization convolutional layer with radially symmetric, positive semi-definite filters that inherent its regularization properties. We then provide a method to learn such regularization filters while also learning to register. Our experiments on synthetic and real datasets as well as ablation analysis show that our method can improve anatomical correspondence compared to competing methods, and reduce the percentage of folding and tear in the large deformation setting, reflecting better regularization and model generalization. | https://openaccess.thecvf.com/content/CVPR2021/papers/Safadi_Learning-Based_Image_Registration_With_Meta-Regularization_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Safadi_Learning-Based_Image_Registration_With_Meta-Regularization_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Safadi_Learning-Based_Image_Registration_With_Meta-Regularization_CVPR_2021_paper.html | CVPR 2021 | null | null |
A Hyperbolic-to-Hyperbolic Graph Convolutional Network | Jindou Dai, Yuwei Wu, Zhi Gao, Yunde Jia | Hyperbolic graph convolutional networks (GCNs) demonstrate powerful representation ability to model graphs with hierarchical structure. Existing hyperbolic GCNs resort to tangent spaces to realize graph convolution on hyperbolic manifolds, which is inferior because tangent space is only a local approximation of a manifold. In this paper, we propose a hyperbolic-to-hyperbolic graph convolutional network (H2H-GCN) that directly works on hyperbolic manifolds. Specifically, we developed a manifold-preserving graph convolution that consists of a hyperbolic feature transformation and a hyperbolic neighborhood aggregation. The hyperbolic feature transformation works as linear transformation on hyperbolic manifolds. It ensures the transformed node representations still lie on the hyperbolic manifold by imposing the orthogonal constraint on the transformation sub-matrix. The hyperbolic neighborhood aggregation updates each node representation via the Einstein midpoint. The H2H-GCN avoids the distortion caused by tangent space approximations and keeps the global hyperbolic structure. Extensive experiments show that the H2H-GCN achieves substantial improvements on the link prediction, node classification, and graph classification tasks. | https://openaccess.thecvf.com/content/CVPR2021/papers/Dai_A_Hyperbolic-to-Hyperbolic_Graph_Convolutional_Network_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.06942 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Dai_A_Hyperbolic-to-Hyperbolic_Graph_Convolutional_Network_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Dai_A_Hyperbolic-to-Hyperbolic_Graph_Convolutional_Network_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Dai_A_Hyperbolic-to-Hyperbolic_Graph_CVPR_2021_supplemental.pdf | null |
Deep Homography for Efficient Stereo Image Compression | Xin Deng, Wenzhe Yang, Ren Yang, Mai Xu, Enpeng Liu, Qianhan Feng, Radu Timofte | In this paper, we propose HESIC, an end-to-end trainable deep network for stereo image compression (SIC). To fully explore the mutual information across two stereo images, we use a deep regression model to estimate the homography matrix, i.e., H matrix. Then, the left image is spatially transformed by the H matrix, and only the residual information between the left and right images is encoded to save bit-rates. A two-branch auto-encoder architecture is adopted in HESIC, corresponding to the left and right images, respectively. For entropy coding, we propose two conditional stereo entropy models, i.e., Gaussian mixture model (GMM) based and context based entropy models, to fully explore the correlation between the two images to reduce the coding bit-rates. In decoding, a cross quality enhancement module is proposed to enhance the image quality based on inverse H matrix. Experimental results show that our HESIC outperforms state-of-the-art SIC methods on InStereo2K and KITTI datasets both quantitatively and qualitatively. | https://openaccess.thecvf.com/content/CVPR2021/papers/Deng_Deep_Homography_for_Efficient_Stereo_Image_Compression_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Deng_Deep_Homography_for_Efficient_Stereo_Image_Compression_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Deng_Deep_Homography_for_Efficient_Stereo_Image_Compression_CVPR_2021_paper.html | CVPR 2021 | null | null |
Point2Skeleton: Learning Skeletal Representations from Point Clouds | Cheng Lin, Changjian Li, Yuan Liu, Nenglun Chen, Yi-King Choi, Wenping Wang | We introduce Point2Skeleton, an unsupervised method to learn skeletal representations from point clouds. Existing skeletonization methods are limited to tubular shapes and the stringent requirement of watertight input, while our method aims to produce more generalized skeletal representations for complex structures and handle point clouds. Our key idea is to use the insights of the medial axis transform (MAT) to capture the intrinsic geometric and topological natures of the original input points. We first predict a set of skeletal points by learning a geometric transformation, and then analyze the connectivity of the skeletal points to form skeletal mesh structures. Extensive evaluations and comparisons show our method has superior performance and robustness. The learned skeletal representation will benefit several unsupervised tasks for point clouds, such as surface reconstruction and segmentation. | https://openaccess.thecvf.com/content/CVPR2021/papers/Lin_Point2Skeleton_Learning_Skeletal_Representations_from_Point_Clouds_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.00230 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Lin_Point2Skeleton_Learning_Skeletal_Representations_from_Point_Clouds_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Lin_Point2Skeleton_Learning_Skeletal_Representations_from_Point_Clouds_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Lin_Point2Skeleton_Learning_Skeletal_CVPR_2021_supplemental.pdf | null |
Neighborhood Contrastive Learning for Novel Class Discovery | Zhun Zhong, Enrico Fini, Subhankar Roy, Zhiming Luo, Elisa Ricci, Nicu Sebe | In this paper, we address Novel Class Discovery (NCD), the task of unveiling new classes in a set of unlabeled samples given a labeled dataset with known classes. We exploit the peculiarities of NCD to build a new framework, named Neighborhood Contrastive Learning (NCL), to learn discriminative representations that are important to clustering performance. Our contribution is twofold. First, we find that a feature extractor trained on the labeled set generates representations in which a generic query sample and its neighbors are likely to share the same class. We exploit this observation to retrieve and aggregate pseudo positive pairs with contrastive learning, thus encouraging the model to learn more discriminative representations. Second, we notice that most of the instances are easily discriminated by the network, contributing less to the contrastive loss. To overcome this issue, we propose to generate hard negatives by mixing labeled and unlabeled samples in the feature space. We experimentally demonstrate that these two ingredients significantly contribute to clustering performance and lead our model to outperform state of the art by a large margin (e.g., clustering accuracy +13% on CIFAR-100 and +8% on ImageNet). | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhong_Neighborhood_Contrastive_Learning_for_Novel_Class_Discovery_CVPR_2021_paper.pdf | http://arxiv.org/abs/2106.10731 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhong_Neighborhood_Contrastive_Learning_for_Novel_Class_Discovery_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhong_Neighborhood_Contrastive_Learning_for_Novel_Class_Discovery_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhong_Neighborhood_Contrastive_Learning_CVPR_2021_supplemental.pdf | null |
SimPoE: Simulated Character Control for 3D Human Pose Estimation | Ye Yuan, Shih-En Wei, Tomas Simon, Kris Kitani, Jason Saragih | Accurate estimation of 3D human motion from monocular video requires modeling both kinematics (body motion without physical forces) and dynamics (motion with physical forces). To demonstrate this, we present SimPoE, a Simulation-based approach for 3D human Pose Estimation, which integrates image-based kinematic inference and physics-based dynamics modeling. SimPoE learns a policy that takes as input the current-frame pose estimate and the next image frame to control a physically-simulated character to output the next-frame pose estimate. The policy contains a learnable kinematic pose refinement unit that uses 2D keypoints to iteratively refine its kinematic pose estimate of the next frame. Based on this refined kinematic pose, the policy learns to compute dynamics-based control (e.g., joint torques) of the character to advance the current-frame pose estimate to the pose estimate of the next frame. This design couples the kinematic pose refinement unit with the dynamics-based control generation unit, which are learned jointly with reinforcement learning to achieve accurate and physically-plausible pose estimation. Furthermore, we propose a meta-control mechanism that dynamically adjusts the character's dynamics parameters based on the character state to attain more accurate pose estimates. Experiments on large-scale motion datasets demonstrate that our approach establishes the new state of the art in pose accuracy while ensuring physical plausibility. | https://openaccess.thecvf.com/content/CVPR2021/papers/Yuan_SimPoE_Simulated_Character_Control_for_3D_Human_Pose_Estimation_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.00683 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Yuan_SimPoE_Simulated_Character_Control_for_3D_Human_Pose_Estimation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Yuan_SimPoE_Simulated_Character_Control_for_3D_Human_Pose_Estimation_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yuan_SimPoE_Simulated_Character_CVPR_2021_supplemental.zip | null |
Neural Camera Simulators | Hao Ouyang, Zifan Shi, Chenyang Lei, Ka Lung Law, Qifeng Chen | We present a controllable camera simulator based on deep neural networks to synthesize raw image data under different camera settings, including exposure time, ISO, and aperture. The proposed simulator includes an exposure module that utilizes the principle of modern lens designs for correcting the luminance level. It also contains a noise module using the noise level function and an aperture module with adaptive attention to simulate the side effects on noise and defocus blur. To facilitate the learning of a simulator model, we collect a dataset of the 10,000 raw images of 450 scenes with different exposure settings. Quantitative experiments and qualitative comparisons show that our approach outperforms relevant baselines in raw data synthesize on multiple cameras. Furthermore, the camera simulator enables various applications, including large-aperture enhancement, HDR, auto exposure, and data augmentation for training local feature detectors. Our work represents the first attempt to simulate a camera sensor's behavior leveraging both the advantage of traditional raw sensor features and the power of data-driven deep learning. | https://openaccess.thecvf.com/content/CVPR2021/papers/Ouyang_Neural_Camera_Simulators_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.05237 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Ouyang_Neural_Camera_Simulators_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Ouyang_Neural_Camera_Simulators_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Ouyang_Neural_Camera_Simulators_CVPR_2021_supplemental.pdf | null |
Neighborhood Normalization for Robust Geometric Feature Learning | Xingtong Liu, Benjamin D. Killeen, Ayushi Sinha, Masaru Ishii, Gregory D. Hager, Russell H. Taylor, Mathias Unberath | Extracting geometric features from 3D models is a common first step in applications such as 3D registration, tracking, and scene flow estimation. Many hand-crafted and learning-based methods aim to produce consistent and distinguishable geometric features for 3D models with partial overlap. These methods work well in cases where the point density and scale of the overlapping 3D objects are similar, but struggle in applications where 3D data are obtained independently with unknown global scale and scene overlap. Unfortunately, instances of this resolution mismatch are common in practice, e.g., when aligning data from multiple sensors. In this work, we introduce a new normalization technique, Batch-Neighborhood Normalization, aiming to improve robustness to mean-std variation of local feature distributions that presumably can happen in samples with varying point density. We empirically demonstrate that the presented normalization method's performance compares favorably to comparison methods in indoor and outdoor environments, and on a clinical dataset, on common point registration benchmarks in both standard and, particularly, resolution-mismatch settings. The source code and clinical dataset are available at https://github.com/lppllppl920/NeighborhoodNormalization-Pytorch. | https://openaccess.thecvf.com/content/CVPR2021/papers/Liu_Neighborhood_Normalization_for_Robust_Geometric_Feature_Learning_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_Neighborhood_Normalization_for_Robust_Geometric_Feature_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_Neighborhood_Normalization_for_Robust_Geometric_Feature_Learning_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Liu_Neighborhood_Normalization_for_CVPR_2021_supplemental.pdf | null |
Video Rescaling Networks With Joint Optimization Strategies for Downscaling and Upscaling | Yan-Cheng Huang, Yi-Hsin Chen, Cheng-You Lu, Hui-Po Wang, Wen-Hsiao Peng, Ching-Chun Huang | This paper addresses the video rescaling task, which arises from the needs of adapting the video spatial resolution to suit individual viewing devices. We aim to jointly optimize video downscaling and upscaling as a combined task. Most recent studies focus on image-based solutions, which do not consider temporal information. We present two joint optimization approaches based on invertible neural networks with coupling layers. Our Long Short-Term Memory Video Rescaling Network (LSTM-VRN) leverages temporal information in the low-resolution video to form an explicit prediction of the missing high-frequency information for upscaling. Our Multi-input Multi-output Video Rescaling Network (MIMO-VRN) proposes a new strategy for downscaling and upscaling a group of video frames simultaneously. Not only do they outperform the image-based invertible model in terms of quantitative and qualitative results, but also show much improved upscaling quality than the video rescaling methods without joint optimization. To our best knowledge, this work is the first attempt at the joint optimization of video downscaling and upscaling. | https://openaccess.thecvf.com/content/CVPR2021/papers/Huang_Video_Rescaling_Networks_With_Joint_Optimization_Strategies_for_Downscaling_and_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.14858 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Huang_Video_Rescaling_Networks_With_Joint_Optimization_Strategies_for_Downscaling_and_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Huang_Video_Rescaling_Networks_With_Joint_Optimization_Strategies_for_Downscaling_and_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Huang_Video_Rescaling_Networks_CVPR_2021_supplemental.pdf | null |
TPCN: Temporal Point Cloud Networks for Motion Forecasting | Maosheng Ye, Tongyi Cao, Qifeng Chen | We propose the Temporal Point Cloud Networks (TPCN), a novel and flexible framework with joint spatial and temporal learning for trajectory prediction. Unlike existing approaches that rasterize agents and map information as 2D images or operate in a graph representation, our approach extends ideas from point cloud learning with dynamic temporal learning to capture both spatial and temporal information by splitting trajectory prediction into both spatial and temporal dimensions. In the spatial dimension, agents can be viewed as an unordered point set, and thus it is straightforward to apply point cloud learning techniques to model agents' locations. While the spatial dimension does not take kinematic and motion information into account, we further propose dynamic temporal learning to model agents' motion over time. Experiments on the Argoverse motion forecasting benchmark show that our approach achieves state-of-the-art results. | https://openaccess.thecvf.com/content/CVPR2021/papers/Ye_TPCN_Temporal_Point_Cloud_Networks_for_Motion_Forecasting_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.03067 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Ye_TPCN_Temporal_Point_Cloud_Networks_for_Motion_Forecasting_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Ye_TPCN_Temporal_Point_Cloud_Networks_for_Motion_Forecasting_CVPR_2021_paper.html | CVPR 2021 | null | null |
TSGCNet: Discriminative Geometric Feature Learning With Two-Stream Graph Convolutional Network for 3D Dental Model Segmentation | Lingming Zhang, Yue Zhao, Deyu Meng, Zhiming Cui, Chenqiang Gao, Xinbo Gao, Chunfeng Lian, Dinggang Shen | The ability to segment teeth precisely from digitized 3D dental models is an essential task in computer-aided orthodontic surgical planning. To date, deep learning based methods have been popularly used to handle this task. State-of-the-art methods directly concatenate the raw attributes of 3D inputs, namely coordinates and normal vectors of mesh cells, to train a single-stream network for fully-automated tooth segmentation. This, however, has the drawback of ignoring the different geometric meanings provided by those raw attributes. This issue might possibly confuse the network in learning discriminative geometric features and result in many isolated false predictions on the dental model. Against this issue, we propose a two-stream graph convolutional network (TSGCNet) to learn multi-view geometric information from different geometric attributes. Our TSGCNet adopts two graph-learning streams, designed in an input-aware fashion, to extract more discriminative high-level geometric representations from coordinates and normal vectors, respectively. These feature representations learned from the designed two different streams are further fused to integrate the multi-view complementary information for the cell-wise dense prediction task. We evaluate our proposed TSGCNet on a real-patient dataset of dental models acquired by 3D intraoral scanners, and experimental results demonstrate that our method significantly outperforms state-of-the-art methods for 3D shape segmentation. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_TSGCNet_Discriminative_Geometric_Feature_Learning_With_Two-Stream_Graph_Convolutional_Network_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_TSGCNet_Discriminative_Geometric_Feature_Learning_With_Two-Stream_Graph_Convolutional_Network_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_TSGCNet_Discriminative_Geometric_Feature_Learning_With_Two-Stream_Graph_Convolutional_Network_CVPR_2021_paper.html | CVPR 2021 | null | null |
Meta Batch-Instance Normalization for Generalizable Person Re-Identification | Seokeon Choi, Taekyung Kim, Minki Jeong, Hyoungseob Park, Changick Kim | Although supervised person re-identification (Re-ID) methods have shown impressive performance, they suffer from a poor generalization capability on unseen domains. Therefore, generalizable Re-ID has recently attracted growing attention. Many existing methods have employed an instance normalization technique to reduce style variations, but the loss of discriminative information could not be avoided. In this paper, we propose a novel generalizable Re-ID framework, named Meta Batch-Instance Normalization (MetaBIN). Our main idea is to generalize normalization layers by simulating unsuccessful generalization scenarios beforehand in the meta-learning pipeline. To this end, we combine learnable batch-instance normalization layers with meta-learning and investigate the challenging cases caused by both batch and instance normalization layers. Moreover, we diversify the virtual simulations via our meta-train loss accompanied by a cyclic inner-updating manner to boost generalization capability. After all, the MetaBIN framework prevents our model from overfitting to the given source styles and improves the generalization capability to unseen domains without additional data augmentation or complicated network design. Extensive experimental results show that our model outperforms the state-of-the-art methods on the large-scale domain generalization Re-ID benchmark and the cross-domain Re-ID problem. The source code is available at: https://github.com/bismex/MetaBIN. | https://openaccess.thecvf.com/content/CVPR2021/papers/Choi_Meta_Batch-Instance_Normalization_for_Generalizable_Person_Re-Identification_CVPR_2021_paper.pdf | http://arxiv.org/abs/2011.14670 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Choi_Meta_Batch-Instance_Normalization_for_Generalizable_Person_Re-Identification_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Choi_Meta_Batch-Instance_Normalization_for_Generalizable_Person_Re-Identification_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Choi_Meta_Batch-Instance_Normalization_CVPR_2021_supplemental.pdf | null |
Dictionary-Guided Scene Text Recognition | Nguyen Nguyen, Thu Nguyen, Vinh Tran, Minh-Triet Tran, Thanh Duc Ngo, Thien Huu Nguyen, Minh Hoai | Language prior plays an important role in the way humans perceive and recognize text in the wild. In this work, we present an approach to train and use scene text recognition models by exploiting multiple clues from a language reference. Current scene text recognition methods have used lexicons to improve recognition performance, but their naive approach of simply casting the output into a dictionary word based purely on the edit distance has many limitations. We introduce here a novel approach to incorporate a dictionary in both the training and inference stage of a scene text recognition system. We use the dictionary to generate a list of possible outcomes and find the one that is most compatible with the visual appearance of the text. The proposed method leads to a robust scene text recognition model, which is better at handling ambiguous cases encountered in the wild, and improves the overall performance of a state-of-the-art scene text spotting framework. Our work suggests that incorporating language prior is a potential approach to advance scene text detection and recognition methods. Besides, we contribute a challenging scene text dataset for Vietnamese, where some characters are equivocal in the visual form due to accent symbols. This dataset will serve as a challenging benchmark for measuring the applicability and robustness of scene text detection and recognition algorithms. | https://openaccess.thecvf.com/content/CVPR2021/papers/Nguyen_Dictionary-Guided_Scene_Text_Recognition_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Nguyen_Dictionary-Guided_Scene_Text_Recognition_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Nguyen_Dictionary-Guided_Scene_Text_Recognition_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Nguyen_Dictionary-Guided_Scene_Text_CVPR_2021_supplemental.pdf | null |
Glance and Gaze: Inferring Action-Aware Points for One-Stage Human-Object Interaction Detection | Xubin Zhong, Xian Qu, Changxing Ding, Dacheng Tao | Modern human-object interaction (HOI) detection approaches can be divided into one-stage methods and two-stage ones. One-stage models are more efficient due to their straightforward architectures, but the two-stage models are still advantageous in accuracy. Existing one-stage models usually begin by detecting predefined interaction areas or points, and then attend to these areas only for interaction prediction; therefore, they lack reasoning steps that dynamically search for discriminative cues. In this paper, we propose a novel one-stage method, namely Glance and Gaze Network (GGNet), which adaptively models a set of action-aware points (ActPoints) via glance and gaze steps. The glance step quickly determines whether each pixel in the feature maps is an interaction point. The gaze step leverages feature maps produced by the glance step to adaptively infer ActPoints around each pixel in a progressive manner. Features of the refined ActPoints are aggregated for interaction prediction. Moreover, we design an action-aware approach that effectively matches each detected interaction with its associated human-object pair, along with a novel hard negative attentive loss to improve the optimization of GGNet. All the above operations are conducted simultaneously and efficiently for all pixels in the feature maps. Finally, GGNet outperforms state-of-the-art methods by significant margins on both V-COCO and HICO-DET benchmarks. Code of GGNet is available at https://github.com/SherlockHolmes221/GGNet. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhong_Glance_and_Gaze_Inferring_Action-Aware_Points_for_One-Stage_Human-Object_Interaction_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.05269 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhong_Glance_and_Gaze_Inferring_Action-Aware_Points_for_One-Stage_Human-Object_Interaction_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhong_Glance_and_Gaze_Inferring_Action-Aware_Points_for_One-Stage_Human-Object_Interaction_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhong_Glance_and_Gaze_CVPR_2021_supplemental.pdf | null |
Activate or Not: Learning Customized Activation | Ningning Ma, Xiangyu Zhang, Ming Liu, Jian Sun | We present a simple, effective, and general activation function we term ACON which learns to activate the neurons or not. Interestingly, we find Swish, the recent popular NAS-searched activation, can be interpreted as a smooth approximation to ReLU. Intuitively, in the same way, we approximate the more general Maxout family to our novel ACON family, which remarkably improves the performance and makes Swish a special case of ACON. Next, we present meta-ACON, which explicitly learns to optimize the parameter switching between non-linear (activate) and linear (inactivate) and provides a new design space. By simply changing the activation function, we show its effectiveness on both small models and highly optimized large models (e.g. it improves the ImageNet top-1 accuracy rate by 6.7% and 1.8% on MobileNet-0.25 and ResNet-152, respectively). Moreover, our novel ACON can be naturally transferred to object detection and semantic segmentation, showing that ACON is an effective alternative in a variety of tasks. Code is available at https://github.com/nmaac/acon. | https://openaccess.thecvf.com/content/CVPR2021/papers/Ma_Activate_or_Not_Learning_Customized_Activation_CVPR_2021_paper.pdf | http://arxiv.org/abs/2009.04759 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Ma_Activate_or_Not_Learning_Customized_Activation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Ma_Activate_or_Not_Learning_Customized_Activation_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Ma_Activate_or_Not_CVPR_2021_supplemental.pdf | null |
Wide-Baseline Relative Camera Pose Estimation With Directional Learning | Kefan Chen, Noah Snavely, Ameesh Makadia | Modern deep learning techniques that regress the relative camera pose between two images have difficulty dealing with challenging scenarios, such as large camera motions resulting in occlusions and significant changes in perspective that leave little overlap between images. These models continue to struggle even with the benefit of large supervised training datasets. To address the limitations of these models, we take inspiration from techniques that show regressing keypoint locations in 2D and 3D can be improved by estimating a discrete distribution over keypoint locations. Analogously, in this paper we explore improving camera pose regression by instead predicting a discrete distribution over camera poses. To realize this idea, we introduce DirectionNet, which estimates discrete distributions over the 5D relative pose space using a novel parameterization to make the estimation problem tractable. Specifically, DirectionNet factorizes relative camera pose, specified by a 3D rotation and a translation direction, into a set of 3D direction vectors. Since 3D directions can be identified with points on the sphere, DirectionNet estimates discrete distributions on the sphere as its output. We evaluate our model on challenging synthetic and real pose estimation datasets constructed from Matterport3D and InteriorNet. Promising results show a near 50% reduction in error over direct regression methods. | https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_Wide-Baseline_Relative_Camera_Pose_Estimation_With_Directional_Learning_CVPR_2021_paper.pdf | http://arxiv.org/abs/2106.03336 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Wide-Baseline_Relative_Camera_Pose_Estimation_With_Directional_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Wide-Baseline_Relative_Camera_Pose_Estimation_With_Directional_Learning_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Chen_Wide-Baseline_Relative_Camera_CVPR_2021_supplemental.pdf | null |
Improving Unsupervised Image Clustering With Robust Learning | Sungwon Park, Sungwon Han, Sundong Kim, Danu Kim, Sungkyu Park, Seunghoon Hong, Meeyoung Cha | Unsupervised image clustering methods often introduce alternative objectives to indirectly train the model and are subject to faulty predictions and overconfident results. To overcome these challenges, the current research proposes an innovative model RUC that is inspired by robust learning. RUC's novelty is at utilizing pseudo-labels of existing image clustering models as a noisy dataset that may include misclassified samples. Its retraining process can revise misaligned knowledge and alleviate the overconfidence problem in predictions. The model's flexible structure makes it possible to be used as an add-on module to other clustering methods and helps them achieve better performance on multiple datasets. Extensive experiments show that the proposed model can adjust the model confidence with better calibration and gain additional robustness against adversarial noise. | https://openaccess.thecvf.com/content/CVPR2021/papers/Park_Improving_Unsupervised_Image_Clustering_With_Robust_Learning_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.11150 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Park_Improving_Unsupervised_Image_Clustering_With_Robust_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Park_Improving_Unsupervised_Image_Clustering_With_Robust_Learning_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Park_Improving_Unsupervised_Image_CVPR_2021_supplemental.pdf | null |
Neural Surface Maps | Luca Morreale, Noam Aigerman, Vladimir G. Kim, Niloy J. Mitra | Maps are arguably one of the most fundamental concepts used to define and operate on manifold surfaces in differentiable geometry. Accordingly, in geometry processing, maps are ubiquitous and are used in many core applications, such as paramterization, shape analysis, remeshing, and deformation. Unfortunately, most computational representations of surface maps do not lend themselves to manipulation and optimization, usually entailing hard, discrete problems. While algorithms exist to solve these problems, they are problem-specific, and a general framework for surface maps is still in need. In this paper, we advocate to consider neural networks as encoding surface maps. Since neural networks can be composed on one another and are differentiable, we show it is easy to use them to define surfaces via atlases, compose them for surface-to-surface mappings, and optimize differentiable objectives relating to them, such as any notion of distortion, in a trivial manner. In our experiments, we represent surfaces by generating a neural map that approximates a UV parameterization of a 3D model. Then, we compose this map with other neural maps which we optimize with respect to distortion measures. We show that our formulation enables trivial optimization of rather elusive mapping tasks, such as maps between a collection of surfaces. | https://openaccess.thecvf.com/content/CVPR2021/papers/Morreale_Neural_Surface_Maps_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.16942 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Morreale_Neural_Surface_Maps_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Morreale_Neural_Surface_Maps_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Morreale_Neural_Surface_Maps_CVPR_2021_supplemental.pdf | null |
Enhance Curvature Information by Structured Stochastic Quasi-Newton Methods | Minghan Yang, Dong Xu, Hongyu Chen, Zaiwen Wen, Mengyun Chen | In this paper, we consider stochastic second-order methods for minimizing a finite summation of nonconvex functions. One important key is to find an ingenious but cheap scheme to incorporate local curvature information. Since the true Hessian matrix is often a combination of a cheap part and an expensive part, we propose a structured stochastic quasi-Newton method by using partial Hessian information as much as possible. By further exploiting either the low-rank structure or the Kronecker-product properties of the quasi-Newton approximations, the computation of the quasi-Newton direction is affordable. Global convergence to stationary point and local superlinear convergence rate are established under some mild assumptions. Numerical results on logistic regression, deep autoencoder networks and deep convolutional neural networks show that our proposed method is quite competitive to the state-of-the-art methods. | https://openaccess.thecvf.com/content/CVPR2021/papers/Yang_Enhance_Curvature_Information_by_Structured_Stochastic_Quasi-Newton_Methods_CVPR_2021_paper.pdf | http://arxiv.org/abs/2006.09606 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Yang_Enhance_Curvature_Information_by_Structured_Stochastic_Quasi-Newton_Methods_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Yang_Enhance_Curvature_Information_by_Structured_Stochastic_Quasi-Newton_Methods_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yang_Enhance_Curvature_Information_CVPR_2021_supplemental.pdf | null |
Variational Relational Point Completion Network | Liang Pan, Xinyi Chen, Zhongang Cai, Junzhe Zhang, Haiyu Zhao, Shuai Yi, Ziwei Liu | Real-scanned point clouds are often incomplete due to viewpoint, occlusion, and noise. Existing point cloud completion methods tend to generate global shape skeletons and hence lack fine local details. Furthermore, they mostly learn a deterministic partial-to-complete mapping, but overlook structural relations in man-made objects. To tackle these challenges, this paper proposes a variational framework, Variational Relational point Completion network (VRCNet) with two appealing properties: 1) Probabilistic Modeling. In particular, we propose a dual-path architecture to enable principled probabilistic modeling across partial and complete clouds. One path consumes complete point clouds for reconstruction by learning a point VAE. The other path generates complete shapes for partial point clouds, whose embedded distribution is guided by distribution obtained from the reconstruction path during training. 2) Relational Enhancement. Specifically, we carefully design point self-attention kernel and point selective kernel module to exploit relational point features, which refines local shape details conditioned on the coarse completion. In addition, we contribute a multi-view partial point cloud dataset (MVP dataset) containing over 100,000 high-quality scans, which renders partial 3D shapes from 26 uniformly distributed camera poses for each 3D CAD model. Extensive experiments demonstrate that VRCNet outperforms state-of-the-art methods on all standard point cloud completion benchmarks. Notably, VRCNet shows great generalizability and robustness on real-world point cloud scans. | https://openaccess.thecvf.com/content/CVPR2021/papers/Pan_Variational_Relational_Point_Completion_Network_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.10154 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Pan_Variational_Relational_Point_Completion_Network_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Pan_Variational_Relational_Point_Completion_Network_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Pan_Variational_Relational_Point_CVPR_2021_supplemental.pdf | null |
StruMonoNet: Structure-Aware Monocular 3D Prediction | Zhenpei Yang, Li Erran Li, Qixing Huang | Monocular 3D prediction is one of the fundamental problems in 3D vision. Recent deep learning-based approaches have brought us exciting progress on this problem. However, existing approaches have predominantly focused on end-to-end depth and normal predictions, which do not fully utilize the underlying 3D environment's geometric structures. This paper introduces StruMonoNet, which detects and enforces a planar structure to enhance pixel-wise predictions. StruMonoNet innovates in leveraging a hybrid representation that combines visual feature and a surfel representation for plane prediction. This formulation allows us to combine the power of visual feature learning and the flexibility of geometric representations in incorporating geometric relations. As a result, StruMonoNet can detect relations between planes such as adjacent planes, perpendicular planes, and parallel planes, all of which are beneficial for dense 3D prediction. Experimental results show that StruMonoNet considerably outperforms state-of-the-art approaches on NYUv2 and ScanNet. | https://openaccess.thecvf.com/content/CVPR2021/papers/Yang_StruMonoNet_Structure-Aware_Monocular_3D_Prediction_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Yang_StruMonoNet_Structure-Aware_Monocular_3D_Prediction_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Yang_StruMonoNet_Structure-Aware_Monocular_3D_Prediction_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yang_StruMonoNet_Structure-Aware_Monocular_CVPR_2021_supplemental.pdf | null |
Learning To Relate Depth and Semantics for Unsupervised Domain Adaptation | Suman Saha, Anton Obukhov, Danda Pani Paudel, Menelaos Kanakis, Yuhua Chen, Stamatios Georgoulis, Luc Van Gool | We present an approach for encoding visual task relationships to improve model performance in an Unsupervised Domain Adaptation (UDA) setting. Semantic segmentation and monocular depth estimation are shown to be complementary tasks; in a multi-task learning setting, a proper encoding of their relationships can further improve performance on both tasks. Motivated by this observation, we propose a novel Cross-Task Relation Layer (CTRL), which encodes task dependencies between the semantic and depth predictions. To capture the cross-task relationships, we propose a neural network architecture that contains task-specific and cross-task refinement heads. Furthermore, we propose an Iterative Self-Learning (ISL) training scheme, which exploits semantic pseudo-labels to provide extra supervision on the target domain. We experimentally observe improvements in both tasks' performance because the complementary information present in these tasks is better captured. Specifically, we show that: (1) our approach improves performance on all tasks when they are complementary and mutually dependent; (2) the CTRL helps to improve both semantic segmentation and depth estimation tasks performance in the challenging UDA setting; (3) the proposed ISL training scheme further improves the semantic segmentation performance. The implementation is available at https://github.com/susaha/ctrl-uda. | https://openaccess.thecvf.com/content/CVPR2021/papers/Saha_Learning_To_Relate_Depth_and_Semantics_for_Unsupervised_Domain_Adaptation_CVPR_2021_paper.pdf | http://arxiv.org/abs/2105.07830 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Saha_Learning_To_Relate_Depth_and_Semantics_for_Unsupervised_Domain_Adaptation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Saha_Learning_To_Relate_Depth_and_Semantics_for_Unsupervised_Domain_Adaptation_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Saha_Learning_To_Relate_CVPR_2021_supplemental.pdf | null |
Training Networks in Null Space of Feature Covariance for Continual Learning | Shipeng Wang, Xiaorong Li, Jian Sun, Zongben Xu | In the setting of continual learning, a network is trained on a sequence of tasks, and suffers from catastrophic forgetting. To balance plasticity and stability of network in continual learning, in this paper, we propose a novel network training algorithm called Adam-NSCL, which sequentially optimizes network parameters in the null space of previous tasks. We first propose two mathematical conditions respectively for achieving network stability and plasticity in continual learning. Based on them, the network training for sequential tasks can be simply achieved by projecting the candidate parameter update into the approximate null space of all previous tasks in the network training process, where the candidate parameter update can be generated by Adam. The approximate null space can be derived by applying singular value decomposition to the uncentered covariance matrix of all input features of previous tasks for each linear layer. For efficiency, the uncentered covariance matrix can be incrementally computed after learning each task. We also empirically verify the rationality of the approximate null space at each linear layer. We apply our approach to training networks for continual learning on benchmark datasets of CIFAR-100 and TinyImageNet, and the results suggest that the proposed approach outperforms or matches the state-ot-the-art continual learning approaches. | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_Training_Networks_in_Null_Space_of_Feature_Covariance_for_Continual_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.07113 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Training_Networks_in_Null_Space_of_Feature_Covariance_for_Continual_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Training_Networks_in_Null_Space_of_Feature_Covariance_for_Continual_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wang_Training_Networks_in_CVPR_2021_supplemental.pdf | null |
PiCIE: Unsupervised Semantic Segmentation Using Invariance and Equivariance in Clustering | Jang Hyun Cho, Utkarsh Mall, Kavita Bala, Bharath Hariharan | We present a new framework for semantic segmentation without annotations via clustering. Off-the-shelf clustering methods are limited to curated, single-label, and object-centric images yet real-world data are dominantly uncurated, multi-label, and scene-centric. We extend clustering from images to pixels and assign separate cluster membership to different instances within each image. However, solely relying on pixel-wise feature similarity fails to learn high-level semantic concepts and overfits to low-level visual cues. We propose a method to incorporate geometric consistency as an inductive bias to learn invariance and equivariance for photometric and geometric variations. With our novel learning objective, our framework can learn high-level semantic concepts. Our method, PiCIE (Pixel-level feature Clustering using Invariance and Equivariance), is the first method capable of segmenting both things and stuff categories without any hyperparameter tuning or task-specific pre-processing. Our method largely outperforms existing baselines on COCO and Cityscapes with +17.5 Acc. and +4.5 mIoU. We show that PiCIE gives a better initialization for standard supervised training. The code is available at https:// github.com/janghyuncho/PiCIE. | https://openaccess.thecvf.com/content/CVPR2021/papers/Cho_PiCIE_Unsupervised_Semantic_Segmentation_Using_Invariance_and_Equivariance_in_Clustering_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.17070 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Cho_PiCIE_Unsupervised_Semantic_Segmentation_Using_Invariance_and_Equivariance_in_Clustering_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Cho_PiCIE_Unsupervised_Semantic_Segmentation_Using_Invariance_and_Equivariance_in_Clustering_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Cho_PiCIE_Unsupervised_Semantic_CVPR_2021_supplemental.pdf | null |
DyCo3D: Robust Instance Segmentation of 3D Point Clouds Through Dynamic Convolution | Tong He, Chunhua Shen, Anton van den Hengel | Previous top-performing approaches for point cloud instance segmentation involve a bottom-up strategy, which often includes inefficient operations or complex pipelines, such as grouping over-segmented components, introducing additional steps for refining, or designing complicated loss functions. The inevitable variation in the instance scales can lead bottom-up methods to become particularly sensitive to hyper-parameter values. To this end, we propose instead a dynamic, proposal-free, data-driven approach that generates the appropriate convolution kernels to apply in response to the nature of the instances. To make the kernels discriminative, we explore a large context by gathering homogeneous points that share identical semantic categories and have close votes for the geometric centroids. Instances are then decoded by several simple convolutional layers. Due to the limited receptive field introduced by the sparse convolution, a small light-weight transformer is also devised to capture the long-range dependencies and high-level interactions among point samples. The proposed method achieves promising results on both ScanetNetV2 and S3DIS, and this performance is robust to the particular hyper-parameter values chosen. It also improves inference speed by more than 25% over the current state-of-the-art. Code is available at: https://git.io/DyCo3D | https://openaccess.thecvf.com/content/CVPR2021/papers/He_DyCo3D_Robust_Instance_Segmentation_of_3D_Point_Clouds_Through_Dynamic_CVPR_2021_paper.pdf | http://arxiv.org/abs/2011.13328 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/He_DyCo3D_Robust_Instance_Segmentation_of_3D_Point_Clouds_Through_Dynamic_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/He_DyCo3D_Robust_Instance_Segmentation_of_3D_Point_Clouds_Through_Dynamic_CVPR_2021_paper.html | CVPR 2021 | null | null |
SSLayout360: Semi-Supervised Indoor Layout Estimation From 360deg Panorama | Phi Vu Tran | Recent years have seen flourishing research on both semi-supervised learning and 3D room layout reconstruction. In this work, we explore the intersection of these two fields to advance the research objective of enabling more accurate 3D indoor scene modeling with less labeled data. We propose the first approach to learn representations of room corners and boundaries by using a combination of labeled and unlabeled data for improved layout estimation in a 360-degree panoramic scene. Through extensive comparative experiments, we demonstrate that our approach can advance layout estimation of complex indoor scenes using as few as 20 labeled examples. When coupled with a layout predictor pre-trained on synthetic data, our semi-supervised method matches the fully supervised counterpart using only 12% of the labels. Our work takes an important first step towards robust semi-supervised layout estimation that can enable many applications in 3D perception with limited labeled data. | https://openaccess.thecvf.com/content/CVPR2021/papers/Tran_SSLayout360_Semi-Supervised_Indoor_Layout_Estimation_From_360deg_Panorama_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Tran_SSLayout360_Semi-Supervised_Indoor_Layout_Estimation_From_360deg_Panorama_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Tran_SSLayout360_Semi-Supervised_Indoor_Layout_Estimation_From_360deg_Panorama_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Tran_SSLayout360_Semi-Supervised_Indoor_CVPR_2021_supplemental.pdf | null |
SLADE: A Self-Training Framework for Distance Metric Learning | Jiali Duan, Yen-Liang Lin, Son Tran, Larry S. Davis, C.-C. Jay Kuo | Most existing distance metric learning approaches use fully labeled data to learn the sample similarities in an embedding space. We present a self-training framework, SLADE, to improve retrieval performance by leveraging additional unlabeled data. We first train a teacher model on the labeled data and use it to generate pseudo labels for the unlabeled data. We then train a student model on both labels and pseudo labels to generate final feature embeddings. We use self-supervised representation learning to initialize the teacher model. To better deal with noisy pseudo labels generated by the teacher network, we design a new feature basis learning component for the student network, which learns basis functions of feature representations for unlabeled data. The learned basis vectors better measure the pairwise similarity and are used to select high-confident samples for training the student network. We evaluate our method on standard retrieval benchmarks: CUB-200, Cars-196 and In-shop. Experimental results demonstrate that with additional unlabeled data, our approach significantly improves the performance over the state-of-the-art methods. | https://openaccess.thecvf.com/content/CVPR2021/papers/Duan_SLADE_A_Self-Training_Framework_for_Distance_Metric_Learning_CVPR_2021_paper.pdf | http://arxiv.org/abs/2011.10269 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Duan_SLADE_A_Self-Training_Framework_for_Distance_Metric_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Duan_SLADE_A_Self-Training_Framework_for_Distance_Metric_Learning_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Duan_SLADE_A_Self-Training_CVPR_2021_supplemental.pdf | null |
NormalFusion: Real-Time Acquisition of Surface Normals for High-Resolution RGB-D Scanning | Hyunho Ha, Joo Ho Lee, Andreas Meuleman, Min H. Kim | Multiview shape-from-shading (SfS) has achieved high-detail geometry, but its computation is expensive for solving a multiview registration and an ill-posed inverse rendering problem. Therefore, it has been mainly used for offline methods. Volumetric fusion enables real-time scanning using a conventional RGB-D camera, but its geometry resolution has been limited by the grid resolution of the volumetric distance field and depth registration errors. In this paper, we propose a real-time scanning method that can acquire high-detail geometry by bridging volumetric fusion and multiview SfS in two steps. First, we propose the first real-time acquisition of photometric normals stored in texture space to achieve high-detail geometry. We also introduce geometry-aware texture mapping, which progressively refines geometric registration between the texture space and the volumetric distance field by means of normal texture, achieving real-time multiview SfS. We demonstrate our scanning of high-detail geometry using an RGB-D camera at 20 fps. Results verify that the geometry quality of our method is strongly competitive with that of offline multi-view SfS methods. | https://openaccess.thecvf.com/content/CVPR2021/papers/Ha_NormalFusion_Real-Time_Acquisition_of_Surface_Normals_for_High-Resolution_RGB-D_Scanning_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Ha_NormalFusion_Real-Time_Acquisition_of_Surface_Normals_for_High-Resolution_RGB-D_Scanning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Ha_NormalFusion_Real-Time_Acquisition_of_Surface_Normals_for_High-Resolution_RGB-D_Scanning_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Ha_NormalFusion_Real-Time_Acquisition_CVPR_2021_supplemental.zip | null |
SE-SSD: Self-Ensembling Single-Stage Object Detector From Point Cloud | Wu Zheng, Weiliang Tang, Li Jiang, Chi-Wing Fu | We present Self-Ensembling Single-Stage object Detector (SE-SSD) for accurate and efficient 3D object detection in outdoor point clouds. Our key focus is on exploiting both soft and hard targets with our formulated constraints to jointly optimize the model, without introducing extra computation in the inference. Specifically, SE-SSD contains a pair of teacher and student SSDs, in which we design an effective IoU-based matching strategy to filter soft targets from the teacher and formulate a consistency loss to align student predictions with them. Also, to maximize the distilled knowledge for ensembling the teacher, we design a new augmentation scheme to produce shape-aware augmented samples to train the student, aiming to encourage it to infer complete object shapes. Lastly, to better exploit hard targets, we design an ODIoU loss to supervise the student with constraints on the predicted box centers and orientations. Our SE-SSD attains top performance compared with all prior published works. Also, it attains top precisions for car detection in the KITTI benchmark (ranked 1st and 2nd on the BEV and 3D leaderboards, respectively) with an ultra-high inference speed. The code is available at https://github.com/Vegeta2020/SE-SSD. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zheng_SE-SSD_Self-Ensembling_Single-Stage_Object_Detector_From_Point_Cloud_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zheng_SE-SSD_Self-Ensembling_Single-Stage_Object_Detector_From_Point_Cloud_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zheng_SE-SSD_Self-Ensembling_Single-Stage_Object_Detector_From_Point_Cloud_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zheng_SE-SSD_Self-Ensembling_Single-Stage_CVPR_2021_supplemental.pdf | null |
Where and What? Examining Interpretable Disentangled Representations | Xinqi Zhu, Chang Xu, Dacheng Tao | Capturing interpretable variations has long been one of the goals in disentanglement learning. However, unlike the independence assumption, interpretability has rarely been exploited to encourage disentanglement in the unsupervised setting. In this paper, we examine the interpretability of disentangled representations by investigating two questions: where to be interpreted and what to be interpreted? A latent code is easily to be interpreted if it would consistently impact a certain subarea of the resulting generated image. We thus propose to learn a spatial mask to localize the effect of each individual latent dimension. On the other hand, interpretability usually comes from latent dimensions that capture simple and basic variations in data. We thus impose a perturbation on a certain dimension of the latent code, and expect to identify the perturbation along this dimension from the generated images so that the encoding of simple variations can be enforced. Additionally, we develop an unsupervised model selection method, which accumulates perceptual distance scores along axes in the latent space. On various datasets, our models can learn high-quality disentangled representations without supervision, showing the proposed modeling of interpretability is an effective proxy for achieving unsupervised disentanglement. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhu_Where_and_What_Examining_Interpretable_Disentangled_Representations_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.05622 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhu_Where_and_What_Examining_Interpretable_Disentangled_Representations_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhu_Where_and_What_Examining_Interpretable_Disentangled_Representations_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhu_Where_and_What_CVPR_2021_supplemental.pdf | null |
Physically-Aware Generative Network for 3D Shape Modeling | Mariem Mezghanni, Malika Boulkenafed, Andre Lieutier, Maks Ovsjanikov | Shapes are often designed to satisfy structural properties and serve a particular functionality in the physical world. Unfortunately, most existing generative models focus primarily on the geometric or visual plausibility, ignoring the physical or structural constraints. To remedy this, we present a novel method aimed to endow deep generative models with physical reasoning. In particular, we introduce a loss and a learning framework that promote two key characteristics of the generated shapes: their connectivity and physical stability. The former ensures that each generated shape consists of a single connected component, while the latter promotes the stability of that shape when subjected to gravity. Our proposed physical losses are fully differentiable and we demonstrate their use in end-to-end learning. Crucially we demonstrate that such physical objectives can be achieved without sacrificing the expressive power of the model and variability of the generated results. We demonstrate through extensive comparisons with the state-of-the-art deep generative models, the utility and efficiency of our proposed approach, while avoiding the potentially costly differentiable physical simulation at training time. | https://openaccess.thecvf.com/content/CVPR2021/papers/Mezghanni_Physically-Aware_Generative_Network_for_3D_Shape_Modeling_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Mezghanni_Physically-Aware_Generative_Network_for_3D_Shape_Modeling_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Mezghanni_Physically-Aware_Generative_Network_for_3D_Shape_Modeling_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Mezghanni_Physically-Aware_Generative_Network_CVPR_2021_supplemental.pdf | null |
Bilinear Parameterization for Non-Separable Singular Value Penalties | Marcus Valtonen Ornhag, Jose Pedro Iglesias, Carl Olsson | Low rank inducing penalties have been proven to successfully uncover fundamental structures considered in computer vision and machine learning; however, such methods generally lead to non-convex optimization problems. Since the resulting objective is non-convex one often resorts to using standard splitting schemes such as Alternating Direction Methods of Multipliers (ADMM), or other subgradient methods, which exhibit slow convergence in the neighbourhood of a local minimum. We propose a method using second order methods, in particular the variable Projection method (VarPro), by replacing the non-convex penalties with a surrogate capable of converting the original objectives to differentiable equivalents. In this way we benefit from faster convergence. The bilinear framework is compatible with a large family of regularizers, and we demonstrate the benefits of our approach on real datasets for rigid and non-rigid structure from motion. The qualitative difference in reconstructions show that many popular non-convex objectives enjoy an advantage in transitioning to the proposed framework. | https://openaccess.thecvf.com/content/CVPR2021/papers/Ornhag_Bilinear_Parameterization_for_Non-Separable_Singular_Value_Penalties_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Ornhag_Bilinear_Parameterization_for_Non-Separable_Singular_Value_Penalties_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Ornhag_Bilinear_Parameterization_for_Non-Separable_Singular_Value_Penalties_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Ornhag_Bilinear_Parameterization_for_CVPR_2021_supplemental.pdf | null |
Objectron: A Large Scale Dataset of Object-Centric Videos in the Wild With Pose Annotations | Adel Ahmadyan, Liangkai Zhang, Artsiom Ablavatski, Jianing Wei, Matthias Grundmann | 3D object detection has recently become popular due to many applications in robotics, augmented reality, autonomy, and image retrieval. We introduce the Objectron dataset to advance the state of the art in 3D object detection and foster new research and applications, such as 3D object tracking, view synthesis, and improved 3D shape representation. The dataset contains object-centric short videos with pose annotations for nine categories and includes 4 million annotated images in 14,819 annotated videos. We also propose a new evaluation metric, 3D Intersection over Union, for 3D object detection. We demonstrate the usefulness of our dataset in 3D object detection and novel view synthesis tasks by providing baseline models trained on this dataset. Our dataset and evaluation source code are available online at Github.com/google-research-datasets/Objectron. | https://openaccess.thecvf.com/content/CVPR2021/papers/Ahmadyan_Objectron_A_Large_Scale_Dataset_of_Object-Centric_Videos_in_the_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.09988 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Ahmadyan_Objectron_A_Large_Scale_Dataset_of_Object-Centric_Videos_in_the_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Ahmadyan_Objectron_A_Large_Scale_Dataset_of_Object-Centric_Videos_in_the_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Ahmadyan_Objectron_A_Large_CVPR_2021_supplemental.pdf | null |
Intra-Inter Camera Similarity for Unsupervised Person Re-Identification | Shiyu Xuan, Shiliang Zhang | Most of unsupervised person Re-Identification (Re-ID) works produce pseudo-labels by measuring the feature similarity without considering the distribution discrepancy among cameras, leading to degraded accuracy in label computation across cameras. This paper targets to address this challenge by studying a novel intra-inter camera similarity for pseudo-label generation. We decompose the sample similarity computation into two stage, i.e., the intra-camera and inter-camera computations, respectively. The intra-camera computation directly leverages the CNN features for similarity computation within each camera. Pseudo-labels generated on different cameras train the re-id model in a multi-branch network. The second stage considers the classification scores of each sample on different cameras as a new feature vector. This new feature effectively alleviates the distribution discrepancy among cameras and generates more reliable pseudo-labels. We hence train our re-id model in two stages with intra-camera and inter-camera pseudo-labels, respectively. This simple intra-inter camera similarity produces surprisingly good performance on multiple datasets, e.g., achieves rank-1 accuracy of 89.5% on the Market1501 dataset, outperforming the recent unsupervised works by 9+%, and is comparable with the latest transfer learning works that leverage extra annotations. | https://openaccess.thecvf.com/content/CVPR2021/papers/Xuan_Intra-Inter_Camera_Similarity_for_Unsupervised_Person_Re-Identification_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.11658 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Xuan_Intra-Inter_Camera_Similarity_for_Unsupervised_Person_Re-Identification_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Xuan_Intra-Inter_Camera_Similarity_for_Unsupervised_Person_Re-Identification_CVPR_2021_paper.html | CVPR 2021 | null | null |
Efficient Feature Transformations for Discriminative and Generative Continual Learning | Vinay Kumar Verma, Kevin J Liang, Nikhil Mehta, Piyush Rai, Lawrence Carin | As neural networks are increasingly being applied to real-world applications, mechanisms to address distributional shift and sequential task learning without forgetting are critical. Methods incorporating network expansion have shown promise by naturally adding model capacity for learning new tasks while simultaneously avoiding catastrophic forgetting. However, the growth in the number of additional parameters of many of these types of methods can be computationally expensive at larger scales, at times prohibitively so. Instead, we propose a simple task-specific feature map transformation strategy for continual learning, which we call Efficient Feature Transformations (EFTs). These EFTs provide powerful flexibility for learning new tasks, achieved with minimal parameters added to the base architecture. We further propose a feature distance maximization strategy, which significantly improves task prediction in class incremental settings, without needing expensive generative models. We demonstrate the efficacy and efficiency of our method with an extensive set of experiments in discriminative (CIFAR-100 and ImageNet-1K) and generative (LSUN, CUB-200, Cats) sequences of tasks. Even with low single-digit parameter growth rates, EFTs can outperform many other continual learning methods in a wide range of settings. | https://openaccess.thecvf.com/content/CVPR2021/papers/Verma_Efficient_Feature_Transformations_for_Discriminative_and_Generative_Continual_Learning_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.13558 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Verma_Efficient_Feature_Transformations_for_Discriminative_and_Generative_Continual_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Verma_Efficient_Feature_Transformations_for_Discriminative_and_Generative_Continual_Learning_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Verma_Efficient_Feature_Transformations_CVPR_2021_supplemental.zip | null |
Learning a Self-Expressive Network for Subspace Clustering | Shangzhi Zhang, Chong You, Rene Vidal, Chun-Guang Li | State-of-the-art subspace clustering methods are based on the self-expressive model, which represents each data point as a linear combination of other data points. However, such methods are designed for a finite sample dataset and lack the ability to generalize to out-of-sample data. Moreover, since the number of self-expressive coefficients grows quadratically with the number of data points, their ability to handle large-scale datasets is often limited. In this paper, we propose a novel framework for subspace clustering, termed Self-Expressive Network (SENet), which employs a properly designed neural network to learn a self-expressive representation of the data. We show that our SENet can not only learn the self-expressive coefficients with desired properties on the training data, but also handle out-of-sample data. Besides, we show that SENet can also be leveraged to perform subspace clustering on large-scale datasets. Extensive experiments conducted on synthetic data and real world benchmark data validate the effectiveness of the proposed method. In particular, SENet yields highly competitive performance on MNIST, Fashion MNIST and Extended MNIST and state-of-the-art performance on CIFAR-10. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_Learning_a_Self-Expressive_Network_for_Subspace_Clustering_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Learning_a_Self-Expressive_Network_for_Subspace_Clustering_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Learning_a_Self-Expressive_Network_for_Subspace_Clustering_CVPR_2021_paper.html | CVPR 2021 | null | null |
A Large-Scale Study on Unsupervised Spatiotemporal Representation Learning | Christoph Feichtenhofer, Haoqi Fan, Bo Xiong, Ross Girshick, Kaiming He | We present a large-scale study on unsupervised spatiotemporal representation learning from videos. With a unified perspective on four recent image-based frameworks, we study a simple objective that can easily generalize all these methods to space-time. Our objective encourages temporally-persistent features in the same video, and in spite of its simplicity, it works surprisingly well across: (i) different unsupervised frameworks, (ii) pre-training datasets, (iii) downstream datasets, and (iv) backbone architectures. We draw a series of intriguing observations from this study, e.g., we discover that encouraging long-spanned persistency can be effective even if the timespan is 60 seconds. In addition to state-of-the-art results in multiple benchmarks, we report a few promising cases in which unsupervised pre-training can outperform its supervised counterpart. Code will be made available at https://github.com/facebookresearch/SlowFast. | https://openaccess.thecvf.com/content/CVPR2021/papers/Feichtenhofer_A_Large-Scale_Study_on_Unsupervised_Spatiotemporal_Representation_Learning_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.14558 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Feichtenhofer_A_Large-Scale_Study_on_Unsupervised_Spatiotemporal_Representation_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Feichtenhofer_A_Large-Scale_Study_on_Unsupervised_Spatiotemporal_Representation_Learning_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Feichtenhofer_A_Large-Scale_Study_CVPR_2021_supplemental.pdf | null |
Asymmetric Metric Learning for Knowledge Transfer | Mateusz Budnik, Yannis Avrithis | Knowledge transfer from large teacher models to smaller student models has recently been studied for metric learning, focusing on fine-grained classification. In this work, focusing on instance-level image retrieval, we study an asymmetric testing task, where the database is represented by the teacher and queries by the student. Inspired by this task, we introduce asymmetric metric learning, a novel paradigm of using asymmetric representations at training. This acts as a simple combination of knowledge transfer with the original metric learning task. We systematically evaluate different teacher and student models, metric learning and knowledge transfer loss functions on the new asymmetric testing as well as the standard symmetric testing task, where database and queries are represented by the same model. We find that plain regression is surprisingly effective compared to more complex knowledge transfer mechanisms, working best in asymmetric testing. Interestingly, our asymmetric metric learning approach works best in symmetric testing, allowing the student to even outperform the teacher. Our implementation is publicly available, including trained student models for all loss functions and all pairs of teacher/student models. This can serve as a benchmark for future research. | https://openaccess.thecvf.com/content/CVPR2021/papers/Budnik_Asymmetric_Metric_Learning_for_Knowledge_Transfer_CVPR_2021_paper.pdf | http://arxiv.org/abs/2006.16331 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Budnik_Asymmetric_Metric_Learning_for_Knowledge_Transfer_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Budnik_Asymmetric_Metric_Learning_for_Knowledge_Transfer_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Budnik_Asymmetric_Metric_Learning_CVPR_2021_supplemental.pdf | null |
Frequency-Aware Discriminative Feature Learning Supervised by Single-Center Loss for Face Forgery Detection | Jiaming Li, Hongtao Xie, Jiahong Li, Zhongyuan Wang, Yongdong Zhang | Face forgery detection is raising ever-increasing interest in computer vision since facial manipulation technologies cause serious worries. Though recent works have reached sound achievements, there are still unignorable problems: a) learned features supervised by softmax loss are separable but not discriminative enough, since softmax loss does not explicitly encourage intra-class compactness and interclass separability; and b) fixed filter banks and hand-crafted features are insufficient to capture forgery patterns of frequency from diverse inputs. To compensate for such limitations, a novel frequency-aware discriminative feature learning framework is proposed in this paper. Specifically, we design a novel single-center loss (SCL) that only compresses intra-class variations of natural faces while boosting interclass differences in the embedding space. In such a case, the network can learn more discriminative features with less optimization difficulty. Besides, an adaptive frequency feature generation module is developed to mine frequency clues in a completely data-driven fashion. With the above two modules, the whole framework can learn more discriminative features in an end-to-end manner. Extensive experiments demonstrate the effectiveness and superiority of our framework on three versions of the FF++ dataset. | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_Frequency-Aware_Discriminative_Feature_Learning_Supervised_by_Single-Center_Loss_for_Face_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.09096 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Frequency-Aware_Discriminative_Feature_Learning_Supervised_by_Single-Center_Loss_for_Face_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Frequency-Aware_Discriminative_Feature_Learning_Supervised_by_Single-Center_Loss_for_Face_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Li_Frequency-Aware_Discriminative_Feature_CVPR_2021_supplemental.pdf | null |
3DCaricShop: A Dataset and a Baseline Method for Single-View 3D Caricature Face Reconstruction | Yuda Qiu, Xiaojie Xu, Lingteng Qiu, Yan Pan, Yushuang Wu, Weikai Chen, Xiaoguang Han | Caricature is an artistic representation that deliberately exaggerates the distinctive features of a human face to convey humor or sarcasm. However, reconstructing a 3D caricature from a 2D caricature image remains a challenging task, mostly due to the lack of data. We propose to fill this gap by introducing 3DCaricShop, the first large-scale 3D caricature dataset that contains 2000 high-quality diversified 3D caricatures manually crafted by professional artists. 3DCaricShop also provides rich annotations including a paired 2D caricature image, camera parameters, and 3D facial landmarks. To demonstrate the advantage of 3DCaricShop, we present a novel baseline approach for single-view 3D caricature reconstruction. To ensure a faithful reconstruction with plausible face deformations, we propose to connect the good ends of the detail-rich implicit functions and the parametric mesh representations. In particular, we first register a template mesh to the output of the implicit generator and iteratively project the registration result onto a pre-trained PCA space to resolve artifacts and self-intersections. To deal with the large deformation during non-rigid registration, we propose a novel view-collaborative graph convolution network (VC-GCN) to extract key points from the implicit mesh for accurate alignment. Our method is able to generate high-fidelity 3D caricature in a pre-defined mesh topology that is animation-ready. Extensive experiments have been conducted on 3DCaricShop to verify the significance of the database and the effectiveness of the proposed method. We will release 3DCaricShop upon publication. | https://openaccess.thecvf.com/content/CVPR2021/papers/Qiu_3DCaricShop_A_Dataset_and_a_Baseline_Method_for_Single-View_3D_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.08204 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Qiu_3DCaricShop_A_Dataset_and_a_Baseline_Method_for_Single-View_3D_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Qiu_3DCaricShop_A_Dataset_and_a_Baseline_Method_for_Single-View_3D_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Qiu_3DCaricShop_A_Dataset_CVPR_2021_supplemental.pdf | null |
OCONet: Image Extrapolation by Object Completion | Richard Strong Bowen, Huiwen Chang, Charles Herrmann, Piotr Teterwak, Ce Liu, Ramin Zabih | Image extrapolation extends an input image beyond the originally-captured field of view. Existing methods struggle to extrapolate images with salient objects in the foreground or are limited to very specific objects such as humans, but tend to work well on indoor/outdoor scenes. We introduce OCONet (Object COmpletion Networks) to extrapolate foreground objects, with an object completion network conditioned on its class. OCONet uses an encoder-decoder architecture trained with adversarial loss to predict the object's texture as well as its extent, represented as a predicted signed-distance field. An independent step extends the background, and the object is composited on top using the predicted mask. Both qualitative and quantitative results show that we improve on state-of-the-art image extrapolation results for challenging examples. | https://openaccess.thecvf.com/content/CVPR2021/papers/Bowen_OCONet_Image_Extrapolation_by_Object_Completion_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Bowen_OCONet_Image_Extrapolation_by_Object_Completion_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Bowen_OCONet_Image_Extrapolation_by_Object_Completion_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Bowen_OCONet_Image_Extrapolation_CVPR_2021_supplemental.pdf | null |
VisualVoice: Audio-Visual Speech Separation With Cross-Modal Consistency | Ruohan Gao, Kristen Grauman | We introduce a new approach for audio-visual speech separation. Given a video, the goal is to extract the speech associated with a face in spite of simultaneous background sounds and/or other human speakers. Whereas existing methods focus on learning the alignment between the speaker's lip movements and the sounds they generate, we propose to leverage the speaker's face appearance as an additional prior to isolate the corresponding vocal qualities they are likely to produce. Our approach jointly learns audio-visual speech separation and cross-modal speaker embeddings from unlabeled video. It yields state-of-the-art results on five benchmark datasets for audio-visual speech separation and enhancement, and generalizes well to challenging real-world videos of diverse scenarios. Our video results and code: http://vision.cs.utexas.edu/projects/VisualVoice/. | https://openaccess.thecvf.com/content/CVPR2021/papers/Gao_VisualVoice_Audio-Visual_Speech_Separation_With_Cross-Modal_Consistency_CVPR_2021_paper.pdf | http://arxiv.org/abs/2101.03149 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Gao_VisualVoice_Audio-Visual_Speech_Separation_With_Cross-Modal_Consistency_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Gao_VisualVoice_Audio-Visual_Speech_Separation_With_Cross-Modal_Consistency_CVPR_2021_paper.html | CVPR 2021 | null | null |
Fair Attribute Classification Through Latent Space De-Biasing | Vikram V. Ramaswamy, Sunnie S. Y. Kim, Olga Russakovsky | Fairness in visual recognition is becoming a prominent and critical topic of discussion as recognition systems are deployed at scale in the real world. Models trained from data in which target labels are correlated with protected attributes (e.g., gender, race) are known to learn and exploit those correlations. In this work, we introduce a method for training accurate target classifiers while mitigating biases that stem from these correlations. We use GANs to generate realistic-looking images, and perturb these images in the underlying latent space to generate training data that is balanced for each protected attribute. We augment the original dataset with this generated data, and empirically demonstrate that target classifiers trained on the augmented dataset exhibit a number of both quantitative and qualitative benefits. We conduct a thorough evaluation across multiple target labels and protected attributes in the CelebA dataset, and provide an in-depth analysis and comparison to existing literature in the space. Code can be found at https://github.com/princetonvisualai/gan-debiasing. | https://openaccess.thecvf.com/content/CVPR2021/papers/Ramaswamy_Fair_Attribute_Classification_Through_Latent_Space_De-Biasing_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.01469 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Ramaswamy_Fair_Attribute_Classification_Through_Latent_Space_De-Biasing_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Ramaswamy_Fair_Attribute_Classification_Through_Latent_Space_De-Biasing_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Ramaswamy_Fair_Attribute_Classification_CVPR_2021_supplemental.pdf | null |
Correlated Input-Dependent Label Noise in Large-Scale Image Classification | Mark Collier, Basil Mustafa, Efi Kokiopoulou, Rodolphe Jenatton, Jesse Berent | Large scale image classification datasets often contain noisy labels. We take a principled probabilistic approach to modelling input-dependent, also known as heteroscedastic, label noise in these datasets. We place a multivariate Normal distributed latent variable on the final hidden layer of a neural network classifier. The covariance matrix of this latent variable, models the aleatoric uncertainty due to label noise. We demonstrate that the learned covariance structure captures known sources of label noise between semantically similar and co-occurring classes. Compared to standard neural network training and other baselines, we show significantly improved accuracy on Imagenet ILSVRC 2012 79.3% (+ 2.6%), Imagenet-21k 47.0% (+ 1.1%) and JFT 64.7% (+ 1.6%). We set a new state-of-the-art result on WebVision 1.0 with 76.6% top-1 accuracy. These datasets range from over 1M to over 300M training examples and from 1k classes to more than 21k classes. Our method is simple to use, and we provide an implementation that is a drop-in replacement for the final fully-connected layer in a deep classifier. | https://openaccess.thecvf.com/content/CVPR2021/papers/Collier_Correlated_Input-Dependent_Label_Noise_in_Large-Scale_Image_Classification_CVPR_2021_paper.pdf | http://arxiv.org/abs/2105.10305 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Collier_Correlated_Input-Dependent_Label_Noise_in_Large-Scale_Image_Classification_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Collier_Correlated_Input-Dependent_Label_Noise_in_Large-Scale_Image_Classification_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Collier_Correlated_Input-Dependent_Label_CVPR_2021_supplemental.zip | null |
Delving Into Localization Errors for Monocular 3D Object Detection | Xinzhu Ma, Yinmin Zhang, Dan Xu, Dongzhan Zhou, Shuai Yi, Haojie Li, Wanli Ouyang | Estimating 3D bounding boxes from monocular images is an essential component in autonomous driving, while accurate 3D object detection from this kind of data is very challenging. In this work, by intensive diagnosis experiments, we quantify the impact introduced by each sub-task and found the `localization error' is the vital factor in restricting monocular 3D detection. Besides, we also investigate the underlying reasons behind localization errors, analyze the issues they might bring, and propose three strategies. First, we revisit the misalignment between the center of the 2D bounding box and the projected center of the 3D object, which is a vital factor leading to low localization accuracy. Second, we observe that accurately localizing distant objects with existing technologies is almost impossible, while those samples will mislead the learned network. To this end, we propose to remove such samples from the training set for improving the overall performance of the detector. Lastly, we also propose a novel 3D IoU oriented loss for the size estimation of the object, which is not affected by `localization error'. We conduct extensive experiments on the KITTI dataset, where the proposed method achieves real-time detection and outperforms previous methods by a large margin. The code will be made available at: https://github.com/xinzhuma/monodle. | https://openaccess.thecvf.com/content/CVPR2021/papers/Ma_Delving_Into_Localization_Errors_for_Monocular_3D_Object_Detection_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.16237 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Ma_Delving_Into_Localization_Errors_for_Monocular_3D_Object_Detection_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Ma_Delving_Into_Localization_Errors_for_Monocular_3D_Object_Detection_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Ma_Delving_Into_Localization_CVPR_2021_supplemental.pdf | null |
Nearest Neighbor Matching for Deep Clustering | Zhiyuan Dang, Cheng Deng, Xu Yang, Kun Wei, Heng Huang | Deep clustering gradually becomes an important branch in unsupervised learning methods. However, current approaches hardly take into consideration the semantic sample relationships that existed in both local and global features. In addition, since the deep features are updated on-the-fly, relying on these sample relationships may construct more semantically confident sample pairs, leading to inferior performance. To tackle this issue, we propose a method called Nearest Neighbor Matching (NNM) to match samples with their nearest neighbors from both local (batch) and global (overall) levels. Specifically, for the local level, we match the nearest neighbors based on batch embedded features, as for the global one, we match neighbors from overall embedded features. To keep the clustering assignment consistent in both neighbors and classes, we frame consistent loss and class contrastive loss for both local and global levels. Experimental results on three benchmark datasets demonstrate the superiority of our new model against state-of-the-art methods. Particularly on the STL-10 dataset, our method can achieve supervised performance. As for the CIFAR-100 dataset, our NNM leads 3.7% against the latest comparison method. Our code will be available at https://github.com/ZhiyuanDang/NNM. | https://openaccess.thecvf.com/content/CVPR2021/papers/Dang_Nearest_Neighbor_Matching_for_Deep_Clustering_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Dang_Nearest_Neighbor_Matching_for_Deep_Clustering_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Dang_Nearest_Neighbor_Matching_for_Deep_Clustering_CVPR_2021_paper.html | CVPR 2021 | null | null |
MOOD: Multi-Level Out-of-Distribution Detection | Ziqian Lin, Sreya Dutta Roy, Yixuan Li | Out-of-distribution (OOD) detection is essential to prevent anomalous inputs from causing a model to fail during deployment. While improved OOD detection methods have emerged, they often rely on the final layer outputs and require a full feedforward pass for any given input. In this paper, we propose a novel framework, multi-level out-of-distribution detection MOOD, which exploits intermediate classifier outputs for dynamic and efficient OOD inference. We explore and establish a direct relationship between the OOD data complexity and optimal exit level, and show that easy OOD examples can be effectively detected early without propagating to deeper layers. At each exit, the OOD examples can be distinguished through our proposed adjusted energy score, which is both empirically and theoretically suitable for networks with multiple classifiers. We extensively evaluate MOOD across 10 OOD datasets spanning a wide range of complexities. Experiments demonstrate that MOOD achieves up to 71.05% computational reduction in inference, while maintaining competitive OOD detection performance. | https://openaccess.thecvf.com/content/CVPR2021/papers/Lin_MOOD_Multi-Level_Out-of-Distribution_Detection_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.14726 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Lin_MOOD_Multi-Level_Out-of-Distribution_Detection_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Lin_MOOD_Multi-Level_Out-of-Distribution_Detection_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Lin_MOOD_Multi-Level_Out-of-Distribution_CVPR_2021_supplemental.pdf | null |
Equalization Loss v2: A New Gradient Balance Approach for Long-Tailed Object Detection | Jingru Tan, Xin Lu, Gang Zhang, Changqing Yin, Quanquan Li | Recently proposed decoupled training methods emerge as a dominant paradigm for long-tailed object detection. But they require an extra fine-tuning stage, and the disjointed optimization of representation and classifier might lead to suboptimal results. However, end-to-end training methods, like equalization loss (EQL), still perform worse than decoupled training methods. In this paper, we reveal the main issue in long-tailed object detection is the imbalanced gradients between positives and negatives, and find that EQL does not solve it well. To address the problem of imbalanced gradients, we introduce a new version of equalization loss, called equalization loss v2 (EQL v2), a novel gradient guided reweighing mechanism that re-balances the training process for each category independently and equally. Extensive experiments are performed on the challenging LVIS benchmark. EQL v2 outperforms origin EQL by about 4 points overall AP with 14 - 18 points improvements on the rare categories. More importantly, it also surpasses decoupled training methods. Without further tuning for the Open Images dataset, EQL v2 improves EQL by 7.3 points AP, showing strong generalization ability. Codes have been released at https://github.com/tztztztztz/eqlv2 | https://openaccess.thecvf.com/content/CVPR2021/papers/Tan_Equalization_Loss_v2_A_New_Gradient_Balance_Approach_for_Long-Tailed_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.08548 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Tan_Equalization_Loss_v2_A_New_Gradient_Balance_Approach_for_Long-Tailed_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Tan_Equalization_Loss_v2_A_New_Gradient_Balance_Approach_for_Long-Tailed_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Tan_Equalization_Loss_v2_CVPR_2021_supplemental.pdf | null |
Dynamic Metric Learning: Towards a Scalable Metric Space To Accommodate Multiple Semantic Scales | Yifan Sun, Yuke Zhu, Yuhan Zhang, Pengkun Zheng, Xi Qiu, Chi Zhang, Yichen Wei | This paper introduces a new fundamental characteristics, i.e., the dynamic range, from real-world metric tools to deep visual recognition. In metrology, the dynamic range is a basic quality of a metric tool, indicating its flexibility to accommodate various scales. Larger dynamic range offers higher flexibility. We argue that such flexibility is also important for deep metric learning, because different visual concepts indeed correspond to different semantic scales. Introducing the dynamic range to deep metric learning, we get a novel computer vision task, i.e., the Dynamic Metric Learning. Dynamic Metric Learning aims to learn a scalable metric space to accommodate visual concepts across multiple semantic scales. Based on three different types of images, i.e., vehicle, animal and online products, we construct three datasets for Dynamic Metric Learning. We benchmark these datasets with popular deep metric learning methods and find Dynamic Metric Learning to be very challenging. The major difficulty lies in a conflict between different scales: the discriminative ability under a small scale usually compromises the discriminative ability under a large one, and vice versa. As a minor contribution, we propose Cross-Scale Learning (CSL) to alleviate such conflict. We show that CSL consistently improves the baseline on all the three datasets. | https://openaccess.thecvf.com/content/CVPR2021/papers/Sun_Dynamic_Metric_Learning_Towards_a_Scalable_Metric_Space_To_Accommodate_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.11781 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Sun_Dynamic_Metric_Learning_Towards_a_Scalable_Metric_Space_To_Accommodate_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Sun_Dynamic_Metric_Learning_Towards_a_Scalable_Metric_Space_To_Accommodate_CVPR_2021_paper.html | CVPR 2021 | null | null |
Primitive Representation Learning for Scene Text Recognition | Ruijie Yan, Liangrui Peng, Shanyu Xiao, Gang Yao | Scene text recognition is a challenging task due to diverse variations of text instances in natural scene images. Conventional methods based on CNN-RNN-CTC or encoder-decoder with attention mechanism may not fully investigate stable and efficient feature representations for multi-oriented scene texts. In this paper, we propose a primitive representation learning method that aims to exploit intrinsic representations of scene text images. We model elements in feature maps as the nodes of an undirected graph. A pooling aggregator and a weighted aggregator are proposed to learn primitive representations, which are transformed into high-level visual text representations by graph convolutional networks. A Primitive REpresentation learning Network (PREN) is constructed to use the visual text representations for parallel decoding. Furthermore, by integrating visual text representations into an encoder-decoder model with the 2D attention mechanism, we propose a framework called PREN2D to alleviate the misalignment problem in attention-based methods. Experimental results on both English and Chinese scene text recognition tasks demonstrate that PREN keeps a balance between accuracy and efficiency, while PREN2D achieves state-of-the-art performance. | https://openaccess.thecvf.com/content/CVPR2021/papers/Yan_Primitive_Representation_Learning_for_Scene_Text_Recognition_CVPR_2021_paper.pdf | http://arxiv.org/abs/2105.04286 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Yan_Primitive_Representation_Learning_for_Scene_Text_Recognition_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Yan_Primitive_Representation_Learning_for_Scene_Text_Recognition_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yan_Primitive_Representation_Learning_CVPR_2021_supplemental.pdf | null |
RPSRNet: End-to-End Trainable Rigid Point Set Registration Network Using Barnes-Hut 2D-Tree Representation | Sk Aziz Ali, Kerem Kahraman, Gerd Reis, Didier Stricker | We propose RPSRNet - a novel end-to-end trainable deep neural network for rigid point set registration. For this task, we use a novel 2^D-tree representation for the input point sets and a hierarchical deep feature embedding in the neural network. An iterative transformation refinement module in our network boosts the feature matching accuracy in the intermediate stages. We achieve an inference speed of 12-15ms to register a pair of input point clouds as large as 250K. Extensive evaluation on (i) KITTI LiDAR odometry and (ii) ModelNet-40 datasets shows that our method outperforms prior state-of-the-art methods -- e.g., on the KITTI data set, DCP-v2 by1.3 and 1.5 times, and PointNetLK by 1.8 and 1.9 times better rotational and translational accuracy respectively. Evaluation on ModelNet40 shows that RPSRNet is more robust than other benchmark methods when the samples contain a significant amount of noise and other disturbances. RPSRNet accurately registers point clouds with non-uniform sampling densities, e.g., LiDAR data, which cannot be processed by many existing deep-learning-based registration methods. | https://openaccess.thecvf.com/content/CVPR2021/papers/Ali_RPSRNet_End-to-End_Trainable_Rigid_Point_Set_Registration_Network_Using_Barnes-Hut_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Ali_RPSRNet_End-to-End_Trainable_Rigid_Point_Set_Registration_Network_Using_Barnes-Hut_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Ali_RPSRNet_End-to-End_Trainable_Rigid_Point_Set_Registration_Network_Using_Barnes-Hut_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Ali_RPSRNet_End-to-End_Trainable_CVPR_2021_supplemental.pdf | null |
On the Difficulty of Membership Inference Attacks | Shahbaz Rezaei, Xin Liu | Recent studies propose membership inference (MI) attacks on deep models, where the goal is to infer if a sample has been used in the training process. Despite their apparent success, these studies only report accuracy, precision, and recall of the positive class (member class). Hence, the performance of these attacks have not been clearly reported on negative class (non-member class). In this paper, we show that the way the MI attack performance has been reported is often misleading because they suffer from high false positive rate or false alarm rate (FAR) that has not been reported. FAR shows how often the attack model mislabel non-training samples (non-member) as training (member) ones. The high FAR makes MI attacks fundamentally impractical, which is particularly more significant for tasks such as membership inference where the majority of samples in reality belong to the negative (non-training) class. Moreover, we show that the current MI attack models can only identify the membership of misclassified samples with mediocre accuracy at best, which only constitute a very small portion of training samples. We analyze several new features that have not been comprehensively explored for membership inference before, including distance to the decision boundary and gradient norms, and conclude that deep models' responses are mostly similar among train and non-train samples. We conduct several experiments on image classification tasks, including MNIST, CIFAR-10, CIFAR-100, and ImageNet, using various model architecture, including LeNet, AlexNet, ResNet, etc. We show that the current state-of-the-art MI attacks cannot achieve high accuracy and low FAR at the same time, even when the attacker is given several advantages. The source code is available at https://github.com/shrezaei/MI-Attack. | https://openaccess.thecvf.com/content/CVPR2021/papers/Rezaei_On_the_Difficulty_of_Membership_Inference_Attacks_CVPR_2021_paper.pdf | http://arxiv.org/abs/2005.13702 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Rezaei_On_the_Difficulty_of_Membership_Inference_Attacks_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Rezaei_On_the_Difficulty_of_Membership_Inference_Attacks_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Rezaei_On_the_Difficulty_CVPR_2021_supplemental.pdf | null |
Neural Geometric Level of Detail: Real-Time Rendering With Implicit 3D Shapes | Towaki Takikawa, Joey Litalien, Kangxue Yin, Karsten Kreis, Charles Loop, Derek Nowrouzezahrai, Alec Jacobson, Morgan McGuire, Sanja Fidler | Neural signed distance functions (SDFs) are emerging as an effective representation for 3D shapes. State-of-the-art methods typically encode the SDF with a large, fixed-size neural network to approximate complex shapes with implicit surfaces. Rendering with these large networks is, however, computationally expensive since it requires many forward passes through the network for every pixel, making these representations impractical for real-time graphics. We introduce an efficient neural representation that, for the first time, enables real-time rendering of high-fidelity neural SDFs, while achieving state-of-the-art geometry reconstruction quality. We represent implicit surfaces using an octree-based feature volume which adaptively fits shapes with multiple discrete levels of detail (LODs), and enables continuous LOD with SDF interpolation. We further develop an efficient algorithm to directly render our novelneural SDF representation in real-time by querying only the necessary LODswith sparse octree traversal. We show that our representation is 2-3 orders of magnitude more efficient in terms of rendering speed compared to previous works. Furthermore, it produces state-of-the-art reconstruction quality for complex shapes under both 3D geometric and 2D image-space metrics. | https://openaccess.thecvf.com/content/CVPR2021/papers/Takikawa_Neural_Geometric_Level_of_Detail_Real-Time_Rendering_With_Implicit_3D_CVPR_2021_paper.pdf | http://arxiv.org/abs/2101.10994 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Takikawa_Neural_Geometric_Level_of_Detail_Real-Time_Rendering_With_Implicit_3D_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Takikawa_Neural_Geometric_Level_of_Detail_Real-Time_Rendering_With_Implicit_3D_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Takikawa_Neural_Geometric_Level_CVPR_2021_supplemental.pdf | null |
Pareidolia Face Reenactment | Linsen Song, Wayne Wu, Chaoyou Fu, Chen Qian, Chen Change Loy, Ran He | We present a new application direction named Pareidolia Face Reenactment, which is defined as animating a static illusory face to move in tandem with a human face in the video. For the large differences between pareidolia face reenactment and traditional human face reenactment, two main challenges are introduced, i.e., shape variance and texture variance. In this work, we propose a novel Parametric Unsupervised Reenactment Algorithm to tackle these two challenges. Specifically, we propose to decompose the reenactment into three catenate processes: shape modeling, motion transfer and texture synthesis. With the decomposition, we introduce three crucial components, i.e., Parametric Shape Modeling, Expansionary Motion Transfer and Unsupervised Texture Synthesizer, to overcome the problems brought by the remarkably variances on pareidolia faces. Extensive experiments show the superior performance of our method both qualitatively and quantitatively. Code, model and data are available on our project page. | https://openaccess.thecvf.com/content/CVPR2021/papers/Song_Pareidolia_Face_Reenactment_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.03061 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Song_Pareidolia_Face_Reenactment_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Song_Pareidolia_Face_Reenactment_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Song_Pareidolia_Face_Reenactment_CVPR_2021_supplemental.zip | null |
ProSelfLC: Progressive Self Label Correction for Training Robust Deep Neural Networks | Xinshao Wang, Yang Hua, Elyor Kodirov, David A. Clifton, Neil M. Robertson | To train robust deep neural networks (DNNs), we systematically study several target modification approaches, which include output regularisation, self and non-self label correction (LC). Two key issues are discovered: (1) Self LC is the most appealing as it exploits its own knowledge and requires no extra models. However, how to automatically decide the trust degree of a learner as training goes is not well answered in the literature? (2) Some methods penalise while the others reward low-entropy predictions, prompting us to ask which one is better? To resolve the first issue, taking two well-accepted propositions-deep neural networks learn meaningful patterns before fitting noise (Arpit et al., 2017) and minimum entropy regularisation principle (Grandvalet & Bengio, 2006)-we propose a novel end-to-end method named ProSelfLC, which is designed according to learning time and entropy. Specifically, given a data point, we progressively increase trust in its predicted label distribution versus its annotated one if a model has been trained for enough time and the prediction is of low entropy (high confidence). For the second issue, according to ProSelfLC, we empirically prove that it is better to redefine a meaningful low-entropy status and optimise the learner toward it. This serves as a defence of entropy minimisation. We demonstrate the effectiveness of ProSelfLC through extensive experiments in both clean and noisy settings. The source code is available at https://github.com/XinshaoAmosWang/ProSelfLC-CVPR2021. | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_ProSelfLC_Progressive_Self_Label_Correction_for_Training_Robust_Deep_Neural_CVPR_2021_paper.pdf | http://arxiv.org/abs/2005.03788 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_ProSelfLC_Progressive_Self_Label_Correction_for_Training_Robust_Deep_Neural_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_ProSelfLC_Progressive_Self_Label_Correction_for_Training_Robust_Deep_Neural_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wang_ProSelfLC_Progressive_Self_CVPR_2021_supplemental.pdf | null |
Learning To Segment Rigid Motions From Two Frames | Gengshan Yang, Deva Ramanan | Appearance-based detectors achieve remarkable performance on common scenes, benefiting from high-capacity models and massive annotated data, but tend to fail for scenarios that lack training data. Geometric motion segmentation algorithms, however, generalize to novel scenes, but have yet to achieve comparable performance to appearance-based ones, due to noisy motion estimations and degenerate motion configurations. To combine the best of both worlds, we propose a modular network, whose architecture is motivated by a geometric analysis of what independent object motions can be recovered from an ego-motion field. It takes two consecutive frames as input and predicts segmentation masks for the background and multiple rigidly moving objects, which are then parameterized by 3D rigid transformations. Our method achieves state-of-the-art performance for rigid motion segmentation on KITTI and Sintel. The inferred rigid motions lead to a significant improvement for depth and scene flow estimation. | https://openaccess.thecvf.com/content/CVPR2021/papers/Yang_Learning_To_Segment_Rigid_Motions_From_Two_Frames_CVPR_2021_paper.pdf | http://arxiv.org/abs/2101.03694 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Yang_Learning_To_Segment_Rigid_Motions_From_Two_Frames_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Yang_Learning_To_Segment_Rigid_Motions_From_Two_Frames_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yang_Learning_To_Segment_CVPR_2021_supplemental.pdf | null |
Joint Deep Model-Based MR Image and Coil Sensitivity Reconstruction Network (Joint-ICNet) for Fast MRI | Yohan Jun, Hyungseob Shin, Taejoon Eo, Dosik Hwang | Magnetic resonance imaging (MRI) can provide diagnostic information with high-resolution and high-contrast images. However, MRI requires a relatively long scan time compared to other medical imaging techniques, where long scan time might occur patient's discomfort and limit the increase in resolution of magnetic resonance (MR) image. In this study, we propose a Joint Deep Model-based MR Image and Coil Sensitivity Reconstruction Network, called Joint-ICNet, which jointly reconstructs an MR image and coil sensitivity maps from undersampled multi-coil k-space data using deep learning networks combined with MR physical models. Joint-ICNet has two main blocks, where one is an MR image reconstruction block that reconstructs an MR image from undersampled multi-coil k-space data and the other is a coil sensitivity maps reconstruction block that estimates coil sensitivity maps from undersampled multi-coil k-space data. The desired MR image and coil sensitivity maps can be obtained by sequentially estimating them with two blocks based on the unrolled network architecture. To demonstrate the performance of Joint-ICNet, we performed experiments with a fastMRI brain dataset for two reduction factors (R = 4 and 8). With qualitative and quantitative results, we demonstrate that our proposed Joint-ICNet outperforms conventional parallel imaging and deep-learning-based methods in reconstructing MR images from undersampled multi-coil k-space data. | https://openaccess.thecvf.com/content/CVPR2021/papers/Jun_Joint_Deep_Model-Based_MR_Image_and_Coil_Sensitivity_Reconstruction_Network_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Jun_Joint_Deep_Model-Based_MR_Image_and_Coil_Sensitivity_Reconstruction_Network_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Jun_Joint_Deep_Model-Based_MR_Image_and_Coil_Sensitivity_Reconstruction_Network_CVPR_2021_paper.html | CVPR 2021 | null | null |
On Feature Normalization and Data Augmentation | Boyi Li, Felix Wu, Ser-Nam Lim, Serge Belongie, Kilian Q. Weinberger | The moments (a.k.a., mean and standard deviation) of latent features are often removed as noise when training image recognition models, to increase stability and reduce training time. However, in the field of image generation, the moments play a much more central role. Studies have shown that the moments extracted from instance normalization and positional normalization can roughly capture style and shape information of an image. Instead of being discarded, these moments are instrumental to the generation process. In this paper we propose Moment Exchange, an implicit data augmentation method that encourages the model to utilize the moment information also for recognition models. Specifically, we replace the moments of the learned features of one training image by those of another, and also interpolate the target labels---forcing the model to extract training signal from the moments in addition to the normalized features. As our approach is fast, operates entirely in feature space, and mixes different signals than prior methods, one can effectively combine it with existing augmentation approaches. We demonstrate its efficacy across several recognition benchmark data sets where it improves the generalization capability of highly competitive baseline networks with remarkable consistency. | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_On_Feature_Normalization_and_Data_Augmentation_CVPR_2021_paper.pdf | http://arxiv.org/abs/2002.11102 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Li_On_Feature_Normalization_and_Data_Augmentation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Li_On_Feature_Normalization_and_Data_Augmentation_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Li_On_Feature_Normalization_CVPR_2021_supplemental.pdf | null |
SelfDoc: Self-Supervised Document Representation Learning | Peizhao Li, Jiuxiang Gu, Jason Kuen, Vlad I. Morariu, Handong Zhao, Rajiv Jain, Varun Manjunatha, Hongfu Liu | We propose SelfDoc, a task-agnostic pre-training framework for document image understanding. Because documents are multimodal and are intended for sequential reading, our framework exploits the positional, textual, and visual information of every semantically meaningful component in a document, and it models the contextualization between each block of content. Unlike existing document pre-training models, our model is coarse-grained instead of treating individual words as input, therefore avoiding an overly fine-grained with excessive contextualization. Beyond that, we introduce cross-modal learning in the model pre-training phase to fully leverage multimodal information from unlabeled documents. For downstream usage, we propose a novel modality-adaptive attention mechanism for multimodal feature fusion by adaptively emphasizing language and vision signals. Our framework benefits from self-supervised pre-training on documents without requiring annotations by a feature masking training strategy. It achieves superior performance on multiple downstream tasks with significantly fewer document images used in the pre-training stage compared to previous works. | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_SelfDoc_Self-Supervised_Document_Representation_Learning_CVPR_2021_paper.pdf | http://arxiv.org/abs/2106.03331 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Li_SelfDoc_Self-Supervised_Document_Representation_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Li_SelfDoc_Self-Supervised_Document_Representation_Learning_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Li_SelfDoc_Self-Supervised_Document_CVPR_2021_supplemental.pdf | null |
Towards Rolling Shutter Correction and Deblurring in Dynamic Scenes | Zhihang Zhong, Yinqiang Zheng, Imari Sato | Joint rolling shutter correction and deblurring (RSCD) techniques are critical for the prevalent CMOS cameras. However, current approaches are still based on conventional energy optimization and are developed for static scenes. To enable learning-based approaches to address real-world RSCD problem, we contribute the first dataset, BS-RSCD, which includes both ego-motion and object-motion in dynamic scenes. Real distorted and blurry videos with corresponding ground truth are recorded simultaneously via a beam-splitter-based acquisition system. Since direct application of existing individual rolling shutter correction (RSC) or global shutter deblurring (GSD) methods on RSCD leads to undesirable results due to inherent flaws in the network architecture, we further present the first learning-based model (JCD) for RSCD. The key idea is that we adopt bi-directional warping streams for displacement compensation, while also preserving the non-warped deblurring stream for details restoration. The experimental results demonstrate that JCD achieves state-of-the-art performance on the realistic RSCD dataset (BS-RSCD) and the synthetic RSC dataset (Fastec-RS). The dataset and code are available at https://github.com/zzh-tech/RSCD. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhong_Towards_Rolling_Shutter_Correction_and_Deblurring_in_Dynamic_Scenes_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.01601 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhong_Towards_Rolling_Shutter_Correction_and_Deblurring_in_Dynamic_Scenes_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhong_Towards_Rolling_Shutter_Correction_and_Deblurring_in_Dynamic_Scenes_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhong_Towards_Rolling_Shutter_CVPR_2021_supplemental.zip | null |
VSPW: A Large-scale Dataset for Video Scene Parsing in the Wild | Jiaxu Miao, Yunchao Wei, Yu Wu, Chen Liang, Guangrui Li, Yi Yang | In this paper, we present a new dataset with the target of advancing the scene parsing task from images to videos. Our dataset aims to perform Video Scene Parsing in the Wild (VSPW), which covers a wide range of real-world scenarios and categories. To be specific, our VSPW is featured from the following aspects: 1) Well-trimmed long-temporal clips. Each video contains a complete shot, lasting around 5 seconds on average. 2) Dense annotation. The pixel-level annotations are provided at a high frame rate of 15 f/s. 3) High resolution. Over 96% of the captured videos are with high spatial resolutions from 720P to 4K. We totally annotate 3,337 videos, including 239,934 frames from 124 categories. To the best of our knowledge, our VSPW is the first attempt to tackle the challenging video scene parsing task in the wild by considering diverse scenarios. Based on VSPW, we design a generic Temporal Context Blending (TCB) network, which can effectively harness long-range contextual information from the past frames to help segment the current one. Extensive experiments show that our TCB network improves both the segmentation performance and temporal stability comparing with image-/video-based state-of-the-art methods. We hope that the scale, diversity, long-temporal, and high frame rate of our VSPW can significantly advance the research of video scene parsing and beyond. | https://openaccess.thecvf.com/content/CVPR2021/papers/Miao_VSPW_A_Large-scale_Dataset_for_Video_Scene_Parsing_in_the_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Miao_VSPW_A_Large-scale_Dataset_for_Video_Scene_Parsing_in_the_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Miao_VSPW_A_Large-scale_Dataset_for_Video_Scene_Parsing_in_the_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Miao_VSPW_A_Large-scale_CVPR_2021_supplemental.pdf | null |
Multi-Label Learning From Single Positive Labels | Elijah Cole, Oisin Mac Aodha, Titouan Lorieul, Pietro Perona, Dan Morris, Nebojsa Jojic | Predicting all applicable labels for a given image is known as multi-label classification. Compared to the standard multi-class case (where each image has only one label), it is considerably more challenging to annotate training data for multi-label classification. When the number of potential labels is large, human annotators find it difficult to mention all applicable labels for each training image. Furthermore, in some settings detection is intrinsically difficult e.g. finding small object instances in high resolution images. As a result, multi-label training data is often plagued by false negatives. We consider the hardest version of this problem, where annotators provide only one relevant label for each image. As a result, training sets will have only one positive label per image and no confirmed negatives. We explore this special case of learning from missing labels across four different multi-label image classification datasets for both linear classifiers and end-to-end fine-tuned deep networks. We extend existing multi-label losses to this setting and propose novel variants that constrain the number of expected positive labels during training. Surprisingly, we show that in some cases it is possible to approach the performance of fully labeled classifiers despite training with significantly fewer confirmed labels. | https://openaccess.thecvf.com/content/CVPR2021/papers/Cole_Multi-Label_Learning_From_Single_Positive_Labels_CVPR_2021_paper.pdf | http://arxiv.org/abs/2106.09708 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Cole_Multi-Label_Learning_From_Single_Positive_Labels_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Cole_Multi-Label_Learning_From_Single_Positive_Labels_CVPR_2021_paper.html | CVPR 2021 | null | null |
Towards Part-Based Understanding of RGB-D Scans | Alexey Bokhovkin, Vladislav Ishimtsev, Emil Bogomolov, Denis Zorin, Alexey Artemov, Evgeny Burnaev, Angela Dai | Recent advances in 3D semantic scene understanding have shown impressive progress in 3D instance segmentation, enabling object-level reasoning about 3D scenes; however, a finer-grained understanding is required to enable interactions with objects and their functional understanding. Thus, we propose the task of part-based scene understanding of real-world 3D environments: from an RGB-D scan of a scene, we detect objects, and for each object predict its decomposition into geometric part masks, which composed together form the complete geometry of the observed object. We leverage an intermediary part graph representation to enable robust completion as well as building of part priors, which we use to construct the final part mask predictions. Our experiments demonstrate that guiding part understanding through part graph to part prior-based predictions significantly outperforms alternative approaches to the task of part-based instance completion. | https://openaccess.thecvf.com/content/CVPR2021/papers/Bokhovkin_Towards_Part-Based_Understanding_of_RGB-D_Scans_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Bokhovkin_Towards_Part-Based_Understanding_of_RGB-D_Scans_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Bokhovkin_Towards_Part-Based_Understanding_of_RGB-D_Scans_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Bokhovkin_Towards_Part-Based_Understanding_CVPR_2021_supplemental.pdf | null |
Learning Semantic-Aware Dynamics for Video Prediction | Xinzhu Bei, Yanchao Yang, Stefano Soatto | We propose an architecture and training scheme to predict video frames by explicitly modeling dis-occlusions and capturing the evolution of semantically consistent regions in the video. The scene layout (semantic map) and motion (optical flow) are decomposed into layers, which are predicted and fused with their context to generate future layouts and motions. The appearance of the scene is warped from past frames using the predicted motion in co-visible regions; dis-occluded regions are synthesized with content-aware inpainting utilizing the predicted scene layout. The result is a predictive model that explicitly represents objects and learns their class-specific motion, which we evaluate on video prediction benchmarks. | https://openaccess.thecvf.com/content/CVPR2021/papers/Bei_Learning_Semantic-Aware_Dynamics_for_Video_Prediction_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.09762 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Bei_Learning_Semantic-Aware_Dynamics_for_Video_Prediction_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Bei_Learning_Semantic-Aware_Dynamics_for_Video_Prediction_CVPR_2021_paper.html | CVPR 2021 | null | null |
Subsets and Splits