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 |
|---|---|---|---|---|---|---|---|---|---|---|
House-GAN++: Generative Adversarial Layout Refinement Network towards Intelligent Computational Agent for Professional Architects | Nelson Nauata, Sepidehsadat Hosseini, Kai-Hung Chang, Hang Chu, Chin-Yi Cheng, Yasutaka Furukawa | This paper proposes a generative adversarial layout refinement network for automated floorplan generation. Our architecture is an integration of a graph-constrained relational GAN and a conditional GAN, where a previously generated layout becomes the next input constraint, enabling iterative refinement. A surprising discovery of our research is that a simple non-iterative training process, dubbed component-wise GT-conditioning, is effective in learning such a generator. The iterative generator further allows us to improve a metric of choice via meta-optimization techniques by controlling when to pass which input constraints during iterative refinement. Our qualitative and quantitative evaluation based on the three standard metrics demonstrate that the proposed system makes significant improvements over the current state-of-the-art, even competitive against the ground-truth floorplans, designed by professional architects. Code, model, and data are available at https://ennauata.github.io/houseganpp/page.html. | https://openaccess.thecvf.com/content/CVPR2021/papers/Nauata_House-GAN_Generative_Adversarial_Layout_Refinement_Network_towards_Intelligent_Computational_Agent_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Nauata_House-GAN_Generative_Adversarial_Layout_Refinement_Network_towards_Intelligent_Computational_Agent_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Nauata_House-GAN_Generative_Adversarial_Layout_Refinement_Network_towards_Intelligent_Computational_Agent_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Nauata_House-GAN_Generative_Adversarial_CVPR_2021_supplemental.pdf | null |
HDR Environment Map Estimation for Real-Time Augmented Reality | Gowri Somanath, Daniel Kurz | We present a method to estimate an HDR environment map from a narrow field-of-view LDR camera image in real-time. This enables perceptually appealing reflections and shading on virtual objects of any material finish, from mirror to diffuse, rendered into a real environment using augmented reality. Our method is based on our efficient convolutional neural network, EnvMapNet, trained end-to-end with two novel losses, ProjectionLoss for the generated image, and ClusterLoss for adversarial training. Through qualitative and quantitative comparison to state-of-the-art methods, we demonstrate that our algorithm reduces the directional error of estimated light sources by more than 50%, and achieves 3.7 times lower Frechet Inception Distance (FID). We further showcase a mobile application that is able to run our neural network model in under 9ms on an iPhone XS, and render in real-time, visually coherent virtual objects in previously unseen real-world environments. | https://openaccess.thecvf.com/content/CVPR2021/papers/Somanath_HDR_Environment_Map_Estimation_for_Real-Time_Augmented_Reality_CVPR_2021_paper.pdf | http://arxiv.org/abs/2011.10687 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Somanath_HDR_Environment_Map_Estimation_for_Real-Time_Augmented_Reality_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Somanath_HDR_Environment_Map_Estimation_for_Real-Time_Augmented_Reality_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Somanath_HDR_Environment_Map_CVPR_2021_supplemental.pdf | null |
OTA: Optimal Transport Assignment for Object Detection | Zheng Ge, Songtao Liu, Zeming Li, Osamu Yoshie, Jian Sun | Recent advances in label assignment in object detection mainly seek to independently define positive/negative training samples for each ground-truth (gt) object. In this paper, we innovatively revisit the label assignment from a global perspective and propose to formulate the assigning procedure as an Optimal Transport (OT) problem -- a well-studied topic in Optimization Theory. Concretely, we define the unit transportation cost between each demander (anchor) and supplier (gt) pair as the weighted summation of their classification and regression losses. After formulation, finding the best assignment solution is converted to solve the optimal transport plan at minimal transportation costs, which can be solved via Sinkhorn-Knopp Iteration. On COCO, a single FCOS-ResNet-50 detector equipped with Optimal Transport Assignment (OTA) can reach 40.7% mAP under 1x scheduler, outperforming all other existing assigning methods. Extensive experiments conducted on COCO and CrowdHuman further validate the effectiveness of our proposed OTA, especially its superiority in crowd scenarios. The code is available at https://github.com/Megvii-BaseDetection/OTA. | https://openaccess.thecvf.com/content/CVPR2021/papers/Ge_OTA_Optimal_Transport_Assignment_for_Object_Detection_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.14259 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Ge_OTA_Optimal_Transport_Assignment_for_Object_Detection_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Ge_OTA_Optimal_Transport_Assignment_for_Object_Detection_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Ge_OTA_Optimal_Transport_CVPR_2021_supplemental.pdf | null |
Progressive Semantic Segmentation | Chuong Huynh, Anh Tuan Tran, Khoa Luu, Minh Hoai | The objective of this work is to segment high-resolution images without overloading GPU memory usage or losing the fine details in the output segmentation map. The memory constraint means that we must either downsample the big image or divide the image into local patches for separate processing. However, the former approach would lose the fine details, while the latter can be ambiguous due to the lack of a global picture. In this work, we present MagNet, a multi-scale framework that resolves local ambiguity by looking at the image at multiple magnification levels. MagNet has multiple processing stages, where each stage corresponds to a magnification level, and the output of one stage is fed into the next stage for coarse-to-fine information propagation. Each stage analyzes the image at a higher resolution than the previous stage, recovering the previously lost details due to the lossy downsampling step, and the segmentation output is progressively refined through the processing stages. Experiments on three high-resolution datasets of urban views, aerial scenes, and medical images shows that MagNet consistently outperforms the state-of-the-art methods by a significant margin. | https://openaccess.thecvf.com/content/CVPR2021/papers/Huynh_Progressive_Semantic_Segmentation_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.03778 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Huynh_Progressive_Semantic_Segmentation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Huynh_Progressive_Semantic_Segmentation_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Huynh_Progressive_Semantic_Segmentation_CVPR_2021_supplemental.pdf | null |
BasicVSR: The Search for Essential Components in Video Super-Resolution and Beyond | Kelvin C.K. Chan, Xintao Wang, Ke Yu, Chao Dong, Chen Change Loy | Video super-resolution (VSR) approaches tend to have more components than the image counterparts as they need to exploit the additional temporal dimension. Complex designs are not uncommon. In this study, we wish to untangle the knots and reconsider some most essential components for VSR guided by four basic functionalities, i.e., Propagation, Alignment, Aggregation, and Upsampling. By reusing some existing components added with minimal redesigns, we show a succinct pipeline, BasicVSR, that achieves appealing improvements in terms of speed and restoration quality in comparison to many state-of-the-art algorithms. We conduct systematic analysis to explain how such gain can be obtained and discuss the pitfalls. We further show the extensibility of BasicVSR by presenting an information-refill mechanism and a coupled propagation scheme to facilitate information aggregation. The BasicVSR and its extension, IconVSR, can serve as strong baselines for future VSR approaches. | https://openaccess.thecvf.com/content/CVPR2021/papers/Chan_BasicVSR_The_Search_for_Essential_Components_in_Video_Super-Resolution_and_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.02181 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Chan_BasicVSR_The_Search_for_Essential_Components_in_Video_Super-Resolution_and_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Chan_BasicVSR_The_Search_for_Essential_Components_in_Video_Super-Resolution_and_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Chan_BasicVSR_The_Search_CVPR_2021_supplemental.pdf | null |
Efficient Multi-Stage Video Denoising With Recurrent Spatio-Temporal Fusion | Matteo Maggioni, Yibin Huang, Cheng Li, Shuai Xiao, Zhongqian Fu, Fenglong Song | In recent years, denoising methods based on deep learning have achieved unparalleled performance at the cost of large computational complexity. In this work, we propose an Efficient Multi-stage Video Denoising algorithm, called EMVD, to drastically reduce the complexity while maintaining or even improving the performance. First, a fusion stage reduces the noise through a recursive combination of all past frames in the video. Then, a denoising stage removes the noise in the fused frame. Finally, a refinement stage restores the missing high frequency in the denoised frame. All stages operate on a transform-domain representation obtained by learnable and invertible linear operators which simultaneously increase accuracy and decrease complexity of the model. A single loss on the final output is sufficient for successful convergence, hence making EMVD easy to train. Experiments on real raw data demonstrate that EMVD outperforms the state of the art when complexity is constrained, and even remains competitive against methods whose complexities are several orders of magnitude higher. Further, the low complexity and memory requirements of EMVD enable real-time video denoising on commercial SoC in mobile devices. | https://openaccess.thecvf.com/content/CVPR2021/papers/Maggioni_Efficient_Multi-Stage_Video_Denoising_With_Recurrent_Spatio-Temporal_Fusion_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.05407 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Maggioni_Efficient_Multi-Stage_Video_Denoising_With_Recurrent_Spatio-Temporal_Fusion_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Maggioni_Efficient_Multi-Stage_Video_Denoising_With_Recurrent_Spatio-Temporal_Fusion_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Maggioni_Efficient_Multi-Stage_Video_CVPR_2021_supplemental.pdf | null |
Self-Supervised Simultaneous Multi-Step Prediction of Road Dynamics and Cost Map | Elmira Amirloo, Mohsen Rohani, Ershad Banijamali, Jun Luo, Pascal Poupart | In this paper we propose a system consisting of a modular network and a trajectory planner. The network simultaneously predicts Occupancy Grid Maps (OGMs) and estimates space-time cost maps (CMs) corresponding to the areas around the vehicle. The trajectory planner computes the cost of a set of predefined trajectories and chooses the one with the lowest cost. Training this network is done in a self-supervised manner which desirably do not require any labeled data. The proposed training objective takes into account the accuracy of OGM predictions as well as contextual information and human driver behavior. Training these modules end-to-end makes each module aware of the errors caused by the other components of the system. We show that our proposed method can lead to the selection of low cost trajectories with a low collision rate and road violation in fairly long planning horizons. | https://openaccess.thecvf.com/content/CVPR2021/papers/Amirloo_Self-Supervised_Simultaneous_Multi-Step_Prediction_of_Road_Dynamics_and_Cost_Map_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.01039 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Amirloo_Self-Supervised_Simultaneous_Multi-Step_Prediction_of_Road_Dynamics_and_Cost_Map_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Amirloo_Self-Supervised_Simultaneous_Multi-Step_Prediction_of_Road_Dynamics_and_Cost_Map_CVPR_2021_paper.html | CVPR 2021 | null | null |
Probabilistic Tracklet Scoring and Inpainting for Multiple Object Tracking | Fatemeh Saleh, Sadegh Aliakbarian, Hamid Rezatofighi, Mathieu Salzmann, Stephen Gould | Despite the recent advances in multiple object tracking (MOT), achieved by joint detection and tracking, dealing with long occlusions remains a challenge. This is due to the fact that such techniques tend to ignore the long-term motion information. In this paper, we introduce a probabilistic autoregressive motion model to score tracklet proposals by directly measuring their likelihood. This is achieved by training our model to learn the underlying distribution of natural tracklets. As such, our model allows us not only to assign new detections to existing tracklets, but also to inpaint a tracklet when an object has been lost for a long time, e.g., due to occlusion, by sampling tracklets so as to fill the gap caused by misdetections. Our experiments demonstrate the superiority of our approach at tracking objects in challenging sequences; it outperforms the state of the art in most standard MOT metrics on multiple MOT benchmark datasets, including MOT16, MOT17, and MOT20. | https://openaccess.thecvf.com/content/CVPR2021/papers/Saleh_Probabilistic_Tracklet_Scoring_and_Inpainting_for_Multiple_Object_Tracking_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.02337 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Saleh_Probabilistic_Tracklet_Scoring_and_Inpainting_for_Multiple_Object_Tracking_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Saleh_Probabilistic_Tracklet_Scoring_and_Inpainting_for_Multiple_Object_Tracking_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Saleh_Probabilistic_Tracklet_Scoring_CVPR_2021_supplemental.pdf | null |
Stay Positive: Non-Negative Image Synthesis for Augmented Reality | Katie Luo, Guandao Yang, Wenqi Xian, Harald Haraldsson, Bharath Hariharan, Serge Belongie | In applications such as optical see-through and projector augmented reality, producing images amounts to solving non-negative image generation, where one can only add light to an existing image. Most image generation methods, however, are ill-suited to this problem setting, as they make the assumption that one can assign arbitrary color to each pixel. In fact, naive application of existing methods fails even in simple domains such as MNIST digits, since one cannot create darker pixels by adding light. We know, however, that the human visual system can be fooled by optical illusions involving certain spatial configurations of brightness and contrast. Our key insight is that one can leverage this behavior to produce high quality images with negligible artifacts. For example, we can create the illusion of darker patches by brightening surrounding pixels. We propose a novel optimization procedure to produce images that satisfy both semantic and non-negativity constraints. Our approach can incorporate existing state-of-the-art methods, and exhibits strong performance in a variety of tasks including image-to-image translation and style transfer. | https://openaccess.thecvf.com/content/CVPR2021/papers/Luo_Stay_Positive_Non-Negative_Image_Synthesis_for_Augmented_Reality_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Luo_Stay_Positive_Non-Negative_Image_Synthesis_for_Augmented_Reality_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Luo_Stay_Positive_Non-Negative_Image_Synthesis_for_Augmented_Reality_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Luo_Stay_Positive_Non-Negative_CVPR_2021_supplemental.zip | null |
3D-to-2D Distillation for Indoor Scene Parsing | Zhengzhe Liu, Xiaojuan Qi, Chi-Wing Fu | Indoor scene semantic parsing from RGB images is very challenging due to occlusions, object distortion, and viewpoint variations. Going beyond prior works that leverage geometry information, typically paired depth maps, we present a new approach, a 3D-to-2D distillation framework, that enables us to leverage 3D features extracted from large-scale 3D data repository (e.g., ScanNet-v2) to enhance 2D features extracted from RGB images. Our work has three novel contributions. First, we distill 3D knowledge from a pretrained 3D network to supervise a 2D network to learn simulated 3D features from 2D features during the training, so the 2D network can infer without requiring 3D data. Second, we design a two-stage dimension normalization scheme to calibrate the 2D and 3D features for better integration. Third, we design a semantic-aware adversarial training model to extend our framework for training with unpaired 3D data. Extensive experiments on various datasets, ScanNet-V2, S3DIS, and NYU-v2, demonstrate the superiority of our approach. Also, experimental results show that our 3D-to-2D distillation improves the model generalization. | https://openaccess.thecvf.com/content/CVPR2021/papers/Liu_3D-to-2D_Distillation_for_Indoor_Scene_Parsing_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.02243 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_3D-to-2D_Distillation_for_Indoor_Scene_Parsing_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_3D-to-2D_Distillation_for_Indoor_Scene_Parsing_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Liu_3D-to-2D_Distillation_for_CVPR_2021_supplemental.pdf | null |
Learning the Best Pooling Strategy for Visual Semantic Embedding | Jiacheng Chen, Hexiang Hu, Hao Wu, Yuning Jiang, Changhu Wang | Visual Semantic Embedding (VSE) is a dominant approach for vision-language retrieval, which aims at learning a deep embedding space such that visual data are embedded close to their semantic text labels or descriptions. Recent VSE models use complex methods to better contextualize and aggregate multi-modal features into holistic embeddings. However, we discover that surprisingly simple (but carefully selected) global pooling functions (e.g., max pooling) outperform those complex models, across different feature extractors. Despite its simplicity and effectiveness, seeking the best pooling function for different data modality and feature extractor is costly and tedious, especially when the size of features varies (e.g., text, video). Therefore, we propose a Generalized Pooling Operator (GPO), which learns to automatically adapt itself to the best pooling strategy for different features, requiring no manual tuning while staying effective and efficient. We extend the VSE model using this proposed GPO and denote it as VSE. Without bells and whistles, VSE outperforms previous VSE methods significantly on image-text retrieval benchmarks across popular feature extractors. With a simple adaptation, variants of VSE further demonstrate its strength by achieving the new state of the art on two video-text retrieval datasets. Comprehensive experiments and visualizations confirm that GPO always discovers the best pooling strategy and can be a plug-and-play feature aggregation module for standard VSE models. | https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_Learning_the_Best_Pooling_Strategy_for_Visual_Semantic_Embedding_CVPR_2021_paper.pdf | http://arxiv.org/abs/2011.04305 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Learning_the_Best_Pooling_Strategy_for_Visual_Semantic_Embedding_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Learning_the_Best_Pooling_Strategy_for_Visual_Semantic_Embedding_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Chen_Learning_the_Best_CVPR_2021_supplemental.pdf | null |
GLAVNet: Global-Local Audio-Visual Cues for Fine-Grained Material Recognition | Fengmin Shi, Jie Guo, Haonan Zhang, Shan Yang, Xiying Wang, Yanwen Guo | In this paper, we aim to recognize materials with combined use of auditory and visual perception. To this end, we construct a new dataset named GLAudio that consists of both the geometry of the object being struck and the sound captured from either modal sound synthesis (for virtual objects) or real measurements (for real objects). Besides global geometries, our dataset also takes local geometries around different hitpoints into consideration. This local information is less explored in existing datasets. We demonstrate that local geometry has a greater impact on the sound than the global geometry and offers more cues in material recognition. To extract features from different modalities and perform proper fusion, we propose a new deep neural network GLAVNet that comprises multiple branches and a well-designed fusion module. Once trained on GLAudio, our GLAVNet provides state-of-the-art performance on material identification and supports fine-grained material categorization. | https://openaccess.thecvf.com/content/CVPR2021/papers/Shi_GLAVNet_Global-Local_Audio-Visual_Cues_for_Fine-Grained_Material_Recognition_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Shi_GLAVNet_Global-Local_Audio-Visual_Cues_for_Fine-Grained_Material_Recognition_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Shi_GLAVNet_Global-Local_Audio-Visual_Cues_for_Fine-Grained_Material_Recognition_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Shi_GLAVNet_Global-Local_Audio-Visual_CVPR_2021_supplemental.pdf | null |
Refining Pseudo Labels With Clustering Consensus Over Generations for Unsupervised Object Re-Identification | Xiao Zhang, Yixiao Ge, Yu Qiao, Hongsheng Li | Unsupervised object re-identification targets at learning discriminative representations for object retrieval without any annotations. Clustering-based methods conduct training with the generated pseudo labels and currently dominate this research direction. However, they still suffer from the issue of pseudo label noise. To tackle the challenge, we propose to properly estimate pseudo label similarities between consecutive training generations with clustering consensus and refine pseudo labels with temporally propagated and ensembled pseudo labels. To the best of our knowledge, this is the first attempt to leverage the spirit of temporal ensembling to improve classification with dynamically changing classes over generations. The proposed pseudo label refinery strategy is simple yet effective and can be seamlessly integrated into existing clustering-based unsupervised re-identification methods. With our proposed approach, state-of-the-art method can be further boosted with up to 8.8% mAP improvements on the challenging MSMT17 dataset. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_Refining_Pseudo_Labels_With_Clustering_Consensus_Over_Generations_for_Unsupervised_CVPR_2021_paper.pdf | http://arxiv.org/abs/2106.06133 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Refining_Pseudo_Labels_With_Clustering_Consensus_Over_Generations_for_Unsupervised_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Refining_Pseudo_Labels_With_Clustering_Consensus_Over_Generations_for_Unsupervised_CVPR_2021_paper.html | CVPR 2021 | null | null |
Regularizing Generative Adversarial Networks Under Limited Data | Hung-Yu Tseng, Lu Jiang, Ce Liu, Ming-Hsuan Yang, Weilong Yang | Recent years have witnessed the rapid progress of generative adversarial networks (GANs). However, the success of the GAN models hinges on a large amount of training data. This work proposes a regularization approach for training robust GAN models on limited data. We theoretically show a connection between the regularized loss and an f-divergence called LeCam-divergence, which we find is more robust under limited training data. Extensive experiments on several benchmark datasets demonstrate that the proposed regularization scheme 1) improves the generalization performance and stabilizes the learning dynamics of GAN models under limited training data, and 2) complements the recent data augmentation methods. These properties facilitate training GAN models to achieve state-of-the-art performance when only limited training data of the ImageNet benchmark is available. The source code is available at https://github.com/google/lecam-gan. | https://openaccess.thecvf.com/content/CVPR2021/papers/Tseng_Regularizing_Generative_Adversarial_Networks_Under_Limited_Data_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.03310 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Tseng_Regularizing_Generative_Adversarial_Networks_Under_Limited_Data_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Tseng_Regularizing_Generative_Adversarial_Networks_Under_Limited_Data_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Tseng_Regularizing_Generative_Adversarial_CVPR_2021_supplemental.pdf | null |
Skeleton Merger: An Unsupervised Aligned Keypoint Detector | Ruoxi Shi, Zhengrong Xue, Yang You, Cewu Lu | Detecting aligned 3D keypoints is essential under many scenarios such as object tracking, shape retrieval and robotics. However, it is generally hard to prepare a high-quality dataset for all types of objects due to the ambiguity of keypoint itself. Meanwhile, current unsupervised detectors are unable to generate aligned keypoints with good coverage. In this paper, we propose an unsupervised aligned keypoint detector, Skeleton Merger, which utilizes skeletons to reconstruct objects. It is based on an Autoencoder architecture. The encoder proposes keypoints and predicts activation strengths of edges between keypoints. The decoder performs uniform sampling on the skeleton and refines it into small point clouds with pointwise offsets. Then the activation strengths are applied and the sub-clouds are merged. Composite Chamfer Distance (CCD) is proposed as a distance between the input point cloud and the reconstruction composed of sub-clouds masked by activation strengths. We demonstrate that Skeleton Merger is capable of detecting semantically-rich salient keypoints with good alignment, and shows comparable performance to supervised methods on the KeypointNet dataset. It is also shown that the detector is robust to noise and subsampling. Our code is available at https://github.com/eliphatfs/SkeletonMerger. | https://openaccess.thecvf.com/content/CVPR2021/papers/Shi_Skeleton_Merger_An_Unsupervised_Aligned_Keypoint_Detector_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.10814 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Shi_Skeleton_Merger_An_Unsupervised_Aligned_Keypoint_Detector_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Shi_Skeleton_Merger_An_Unsupervised_Aligned_Keypoint_Detector_CVPR_2021_paper.html | CVPR 2021 | null | null |
Regularizing Neural Networks via Adversarial Model Perturbation | Yaowei Zheng, Richong Zhang, Yongyi Mao | Effective regularization techniques are highly desired in deep learning for alleviating overfitting and improving generalization. This work proposes a new regularization scheme, based on the understanding that the flat local minima of the empirical risk cause the model to generalize better. This scheme is referred to as adversarial model perturbation (AMP), where instead of directly minimizing the empirical risk, an alternative "AMP loss" is minimized via SGD. Specifically, the AMP loss is obtained from the empirical risk by applying the "worst" norm-bounded perturbation on each point in the parameter space. Comparing with most existing regularization schemes, AMP has strong theoretical justifications, in that minimizing the AMP loss can be shown theoretically to favour flat local minima of the empirical risk. Extensive experiments on various modern deep architectures establish AMP as a new state of the art among regularization schemes. Our code is available at https://github.com/hiyouga/AMP-Regularizer. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zheng_Regularizing_Neural_Networks_via_Adversarial_Model_Perturbation_CVPR_2021_paper.pdf | http://arxiv.org/abs/2010.04925 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zheng_Regularizing_Neural_Networks_via_Adversarial_Model_Perturbation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zheng_Regularizing_Neural_Networks_via_Adversarial_Model_Perturbation_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zheng_Regularizing_Neural_Networks_CVPR_2021_supplemental.pdf | null |
Learning by Aligning Videos in Time | Sanjay Haresh, Sateesh Kumar, Huseyin Coskun, Shahram N. Syed, Andrey Konin, Zeeshan Zia, Quoc-Huy Tran | We present a self-supervised approach for learning video representations using temporal video alignment as a pretext task, while exploiting both frame-level and video-level information. We leverage a novel combination of temporal alignment loss and temporal regularization terms, which can be used as supervision signals for training an encoder network. Specifically, the temporal alignment loss (i.e., Soft-DTW) aims for the minimum cost for temporally aligning videos in the embedding space. However, optimizing solely for this term leads to trivial solutions, particularly, one where all frames get mapped to a small cluster in the embedding space. To overcome this problem, we propose a temporal regularization term (i.e., Contrastive-IDM) which encourages different frames to be mapped to different points in the embedding space. Extensive evaluations on various tasks, including action phase classification, action phase progression, and fine-grained frame retrieval, on three datasets, namely Pouring, Penn Action, and IKEA ASM, show superior performance of our approach over state-of-the-art methods for self-supervised representation learning from videos. In addition, our method provides significant performance gain where labeled data is lacking. | https://openaccess.thecvf.com/content/CVPR2021/papers/Haresh_Learning_by_Aligning_Videos_in_Time_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.17260 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Haresh_Learning_by_Aligning_Videos_in_Time_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Haresh_Learning_by_Aligning_Videos_in_Time_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Haresh_Learning_by_Aligning_CVPR_2021_supplemental.pdf | null |
Contrastive Neural Architecture Search With Neural Architecture Comparators | Yaofo Chen, Yong Guo, Qi Chen, Minli Li, Wei Zeng, Yaowei Wang, Mingkui Tan | One of the key steps in Neural Architecture Search (NAS) is to estimate the performance of candidate architectures. Existing methods either directly use the validation performance or learn a predictor to estimate the performance. However, these methods can be either computationally expensive or very inaccurate, which may severely affect the search efficiency and performance. Moreover, as it is very difficult to annotate architectures with accurate performance on specific tasks, learning a promising performance predictor is often non-trivial due to the lack of labeled data. In this paper, we argue that it may not be necessary to estimate the absolute performance for NAS. On the contrary, we may need only to understand whether an architecture is better than a baseline one. However, how to exploit this comparison information as the reward and how to well use the limited labeled data remains two great challenges. In this paper, we propose a novel Contrastive Neural Architecture Search (CTNAS) method which performs architecture search by taking the comparison results between architectures as the reward. Specifically, we design and learn a Neural Architecture Comparator (NAC) to compute the probability of candidate architectures being better than a baseline one. Moreover, we present a baseline updating scheme to improve the baseline iteratively in a curriculum learning manner. More critically, we theoretically show that learning NAC is equivalent to optimizing the ranking over architectures. Extensive experiments in three search spaces demonstrate the superiority of our CTNAS over existing methods. | https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_Contrastive_Neural_Architecture_Search_With_Neural_Architecture_Comparators_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.05471 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Contrastive_Neural_Architecture_Search_With_Neural_Architecture_Comparators_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Contrastive_Neural_Architecture_Search_With_Neural_Architecture_Comparators_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Chen_Contrastive_Neural_Architecture_CVPR_2021_supplemental.pdf | null |
Implicit Feature Alignment: Learn To Convert Text Recognizer to Text Spotter | Tianwei Wang, Yuanzhi Zhu, Lianwen Jin, Dezhi Peng, Zhe Li, Mengchao He, Yongpan Wang, Canjie Luo | Text recognition is a popular research subject with many associated challenges. Despite the considerable progress made in recent years, the text recognition task itself is still constrained to solve the problem of reading cropped line text images and serves as a subtask of optical character recognition (OCR) systems. As a result, the final text recognition result is limited by the performance of the text detector. In this paper, we propose a simple, elegant and effective paradigm called Implicit Feature Alignment (IFA), which can be easily integrated into current text recognizers, resulting in a novel inference mechanism called IFA-inference. This enables an ordinary text recognizer to process multi-line text such that text detection can be completely freed. Specifically, we integrate IFA into the two most prevailing text recognition streams (attention-based and CTC-based) and propose attention-guided dense prediction (ADP) and Extended CTC (ExCTC). Furthermore, the Wasserstein-based Hollow Aggregation Cross-Entropy (WH-ACE) is proposed to suppress negative predictions to assist in training ADP and ExCTC. We experimentally demonstrate that IFA achieves state-of-the-art performance on end-to-end document recognition tasks while maintaining the fastest speed, and ADP and ExCTC complement each other on the perspective of different application scenarios. Code will be available at https://github.com/Wang-Tianwei/Implicit-feature-alignment. | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_Implicit_Feature_Alignment_Learn_To_Convert_Text_Recognizer_to_Text_CVPR_2021_paper.pdf | http://arxiv.org/abs/2106.05920 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Implicit_Feature_Alignment_Learn_To_Convert_Text_Recognizer_to_Text_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Implicit_Feature_Alignment_Learn_To_Convert_Text_Recognizer_to_Text_CVPR_2021_paper.html | CVPR 2021 | null | null |
Populating 3D Scenes by Learning Human-Scene Interaction | Mohamed Hassan, Partha Ghosh, Joachim Tesch, Dimitrios Tzionas, Michael J. Black | Humans live within a 3D space and constantly interact with it to perform tasks. Such interactions involve physical contact between surfaces that is semantically meaningful. Our goal is to learn how humans interact with scenes and leverage this to enable virtual characters to do the same. To that end, we introduce a novel Human-Scene Interaction (HSI) model that encodes proximal relationships, called POSA for "Pose with prOximitieS and contActs". The representation of interaction is body-centric, which enables it to generalize to new scenes. Specifically, POSA augments the SMPL-X parametric human body model such that, for every mesh vertex, it encodes (a) the contact probability with the scene surface and (b) the corresponding semantic scene label. We learn POSA with a VAE conditioned on the SMPL-X vertices, and train on the PROX dataset, which contains SMPL-X meshes of people interacting with 3D scenes, and the corresponding scene semantics from the PROX-E dataset. We demonstrate the value of POSA with two applications. First, we automatically place 3D scans of people in scenes. We use a SMPL-X model fit to the scan as a proxy and then find its most likely placement in 3D. POSA provides an effective representation to search for "affordances" in the scene that match the likely contact relationships for that pose. We perform a perceptual study that shows significant improvement over the state of the art on this task. Second, we show that POSA's learned representation of body-scene interaction supports monocular human pose estimation that is consistent with a 3D scene, improving on the state of the art. Our model and code are available for research purposes at https://posa.is.tue.mpg.de. | https://openaccess.thecvf.com/content/CVPR2021/papers/Hassan_Populating_3D_Scenes_by_Learning_Human-Scene_Interaction_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.11581 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Hassan_Populating_3D_Scenes_by_Learning_Human-Scene_Interaction_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Hassan_Populating_3D_Scenes_by_Learning_Human-Scene_Interaction_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Hassan_Populating_3D_Scenes_CVPR_2021_supplemental.pdf | null |
Variational Pedestrian Detection | Yuang Zhang, Huanyu He, Jianguo Li, Yuxi Li, John See, Weiyao Lin | Pedestrian detection in a crowd is a challenging task due to a high number of mutually-occluding human instances, which brings ambiguity and optimization difficulties to the current IoU-based ground truth assignment procedure in classical object detection methods. In this paper, we develop a unique perspective of pedestrian detection as a variational inference problem. We formulate a novel and efficient algorithm for pedestrian detection by modeling the dense proposals as a latent variable while proposing a customized Auto-Encoding Variational Bayes (AEVB) algorithm. Through the optimization of our proposed algorithm, a classical detector can be fashioned into a variational pedestrian detector. Experiments conducted on CrowdHuman and CityPersons datasets show that the proposed algorithm serves as an efficient solution to handle the dense pedestrian detection problem for the case of single-stage detectors. Our method can also be flexibly applied to two-stage detectors, achieving notable performance enhancement. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_Variational_Pedestrian_Detection_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.12389 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Variational_Pedestrian_Detection_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Variational_Pedestrian_Detection_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhang_Variational_Pedestrian_Detection_CVPR_2021_supplemental.pdf | null |
SIPSA-Net: Shift-Invariant Pan Sharpening With Moving Object Alignment for Satellite Imagery | Jaehyup Lee, Soomin Seo, Munchurl Kim | Pan-sharpening is a process of merging a high-resolution (HR) panchromatic (PAN) image and its corresponding low-resolution (LR) multi-spectral (MS) image to create an HR-MS and pan-sharpened image. However, due to the different sensors' locations, characteristics and acquisition time, PAN and MS image pairs often tend to have various amounts of misalignment. Conventional deep-learning-based methods that were trained with such misaligned PAN-MS image pairs suffer from diverse artifacts such as double-edge and blur artifacts in the resultant PAN-sharpened images. In this paper, we propose a novel framework called shift-invariant pan-sharpening with moving object alignment (SIPSA-Net) which is the first method to take into account such large misalignment of moving object regions for PAN sharpening. The SISPA-Net has a feature alignment module (FAM) that can adjust one feature to be aligned to another feature, even between the two different PAN and MS domains. For better alignment in pan-sharpened images, a shift-invariant spectral loss is newly designed, which ignores the inherent misalignment in the original MS input, thereby having the same effect as optimizing the spectral loss with a well-aligned MS image. Extensive experimental results show that our SIPSA-Net can generate pan-sharpened images with remarkable improvements in terms of visual quality and alignment, compared to the state-of-the-art methods. | https://openaccess.thecvf.com/content/CVPR2021/papers/Lee_SIPSA-Net_Shift-Invariant_Pan_Sharpening_With_Moving_Object_Alignment_for_Satellite_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Lee_SIPSA-Net_Shift-Invariant_Pan_Sharpening_With_Moving_Object_Alignment_for_Satellite_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Lee_SIPSA-Net_Shift-Invariant_Pan_Sharpening_With_Moving_Object_Alignment_for_Satellite_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Lee_SIPSA-Net_Shift-Invariant_Pan_CVPR_2021_supplemental.pdf | null |
Large-Scale Localization Datasets in Crowded Indoor Spaces | Donghwan Lee, Soohyun Ryu, Suyong Yeon, Yonghan Lee, Deokhwa Kim, Cheolho Han, Yohann Cabon, Philippe Weinzaepfel, Nicolas Guerin, Gabriela Csurka, Martin Humenberger | Estimating the precise location of a camera using visual localization enables interesting applications such as augmented reality or robot navigation. This is particularly useful in indoor environments where other localization technologies, such as GNSS, fail. Indoor spaces impose interesting challenges on visual localization algorithms: occlusions due to people, textureless surfaces, large viewpoint changes, low light, repetitive textures, etc. Existing indoor datasets are either comparably small or do only cover a subset of the mentioned challenges. In this paper, we introduce 5 new indoor datasets for visual localization in challenging real-world environments. They were captured in a large shopping mall and a large metro station in Seoul, South Korea, using a dedicated mapping platform consisting of 10 cameras and 2 laser scanners. In order to obtain accurate ground truth camera poses, we developed a robust LiDAR SLAM which provides initial poses that are then refined using a novel structure-from-motion based optimization. We present a benchmark of modern visual localization algorithms on these challenging datasets showing superior performance of structure-based methods using robust image features. The datasets are available at: https://naverlabs.com/datasets | https://openaccess.thecvf.com/content/CVPR2021/papers/Lee_Large-Scale_Localization_Datasets_in_Crowded_Indoor_Spaces_CVPR_2021_paper.pdf | http://arxiv.org/abs/2105.08941 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Lee_Large-Scale_Localization_Datasets_in_Crowded_Indoor_Spaces_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Lee_Large-Scale_Localization_Datasets_in_Crowded_Indoor_Spaces_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Lee_Large-Scale_Localization_Datasets_CVPR_2021_supplemental.pdf | null |
Distilling Causal Effect of Data in Class-Incremental Learning | Xinting Hu, Kaihua Tang, Chunyan Miao, Xian-Sheng Hua, Hanwang Zhang | We propose a causal framework to explain the catastrophic forgetting in Class-Incremental Learning (CIL) and then derive a novel distillation method that is orthogonal to the existing anti-forgetting techniques, such as data replay and feature/label distillation. We first 1) place CIL into the framework, 2) answer why the forgetting happens: the causal effect of the old data is lost in new training, and then 3) explain how the existing techniques mitigate it: they bring the causal effect back. Based on the causal framework, we propose to distill the Colliding Effect between the old and the new data, which is fundamentally equivalent to the causal effect of data replay, but without any cost of replay storage. Thanks to the causal effect analysis, we can further capture the Incremental Momentum Effect of the data stream, removing which can help to retain the old effect overwhelmed by the new data effect, and thus alleviate the forgetting of the old class in testing. Extensive experiments on three CIL benchmarks: CIFAR-100, ImageNet-Sub&Full, show that the proposed causal effect distillation can improve various state-of-the-art CIL methods by a large margin (0.72%-9.06%) | https://openaccess.thecvf.com/content/CVPR2021/papers/Hu_Distilling_Causal_Effect_of_Data_in_Class-Incremental_Learning_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.01737 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Hu_Distilling_Causal_Effect_of_Data_in_Class-Incremental_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Hu_Distilling_Causal_Effect_of_Data_in_Class-Incremental_Learning_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Hu_Distilling_Causal_Effect_CVPR_2021_supplemental.pdf | null |
Backdoor Attacks Against Deep Learning Systems in the Physical World | Emily Wenger, Josephine Passananti, Arjun Nitin Bhagoji, Yuanshun Yao, Haitao Zheng, Ben Y. Zhao | Backdoor attacks embed hidden malicious behaviors into deep learning models, which only activate and cause misclassifications on model inputs containing a specific "trigger." Existing works on backdoor attacks and defenses, however, mostly focus on digital attacks that apply digitally generated patterns as triggers. A critical question remains unanswered: "can backdoor attacks succeed using physical objects as triggers, making them a credible threat against deep learning systems in the real world?" We conduct a detailed empirical study to explore this question for facial recognition, a critical deep learning task. Using 7 physical objects as triggers, we collect a custom dataset of 3205 images of 10 volunteers and use it to study the feasibility of "physical" backdoor attacks under a variety of real-world conditions. Our study reveals two key findings. First, physical backdoor attacks can be highly successful if they are carefully configured to overcome the constraints imposed by physical objects. In particular, the placement of successful triggers is largely constrained by the victim model's dependence on key facial features. Second, four of today's state-of-the-art defenses against (digital) backdoors are ineffective against physical backdoors, because the use of physical objects breaks core assumptions used to construct these defenses. Our study confirms that (physical) backdoor attacks are not a hypothetical phenomenon but rather pose a serious real-world threat to critical classification tasks. We need new and more robust defenses against backdoors in the physical world. | https://openaccess.thecvf.com/content/CVPR2021/papers/Wenger_Backdoor_Attacks_Against_Deep_Learning_Systems_in_the_Physical_World_CVPR_2021_paper.pdf | http://arxiv.org/abs/2006.14580 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wenger_Backdoor_Attacks_Against_Deep_Learning_Systems_in_the_Physical_World_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wenger_Backdoor_Attacks_Against_Deep_Learning_Systems_in_the_Physical_World_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wenger_Backdoor_Attacks_Against_CVPR_2021_supplemental.pdf | null |
A Multiplexed Network for End-to-End, Multilingual OCR | Jing Huang, Guan Pang, Rama Kovvuri, Mandy Toh, Kevin J Liang, Praveen Krishnan, Xi Yin, Tal Hassner | Recent advances in OCR have shown that an end-to-end (E2E) training pipeline that includes both detection and recognition leads to the best results. However, many existing methods focus primarily on Latin-alphabet languages, often even only case-insensitive English characters. In this paper, we propose an E2E approach, Multiplexed Multilingual Mask TextSpotter, that performs script identification at the word level and handles different scripts with different recognition heads, all while maintaining a unified loss that simultaneously optimizes script identification and multiple recognition heads. Experiments show that our method outperforms single-head model with similar parameters in end-to-end recognition tasks, and achieves state-of-the-art results on MLT17 and MLT19 joint text detection and script identification benchmarks. We believe that our work is a step towards end-to-end trainable and scalable multilingual multi-purpose OCR system. | https://openaccess.thecvf.com/content/CVPR2021/papers/Huang_A_Multiplexed_Network_for_End-to-End_Multilingual_OCR_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.15992 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Huang_A_Multiplexed_Network_for_End-to-End_Multilingual_OCR_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Huang_A_Multiplexed_Network_for_End-to-End_Multilingual_OCR_CVPR_2021_paper.html | CVPR 2021 | null | null |
Semi-Supervised Semantic Segmentation With Directional Context-Aware Consistency | Xin Lai, Zhuotao Tian, Li Jiang, Shu Liu, Hengshuang Zhao, Liwei Wang, Jiaya Jia | Semantic segmentation has made tremendous progress in recent years. However, satisfying performance highly depends on a large number of pixel-level annotations. Therefore, in this paper, we focus on the semi-supervised segmentation problem where only a small set of labeled data is provided with a much larger collection of totally unlabeled images. Nevertheless, due to the limited annotations, models may overly rely on the contexts available in the training data, which causes poor generalization to the scenes unseen before. A preferred high-level representation should capture the contextual information while not losing self-awareness. Therefore, we propose to maintain the context-aware consistency between features of the same identity but with different contexts, making the representations robust to the varying environments. Moreover, we present the Directional Contrastive Loss (DC Loss) to accomplish the consistency in a pixel-to-pixel manner, only requiring the feature with lower quality to be aligned towards its counterpart. In addition, to avoid the false-negative samples and filter the uncertain positive samples, we put forward two sampling strategies. Extensive experiments show that our simple yet effective method surpasses current state-of-the-art methods by a large margin and also generalizes well with extra image-level annotations. | https://openaccess.thecvf.com/content/CVPR2021/papers/Lai_Semi-Supervised_Semantic_Segmentation_With_Directional_Context-Aware_Consistency_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Lai_Semi-Supervised_Semantic_Segmentation_With_Directional_Context-Aware_Consistency_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Lai_Semi-Supervised_Semantic_Segmentation_With_Directional_Context-Aware_Consistency_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Lai_Semi-Supervised_Semantic_Segmentation_CVPR_2021_supplemental.pdf | null |
Causal Hidden Markov Model for Time Series Disease Forecasting | Jing Li, Botong Wu, Xinwei Sun, Yizhou Wang | We propose a causal hidden Markov model to achieve robust prediction of irreversible disease at an early stage, which is safety-critical and vital for medical treatment in early stages. Specifically, we introduce the hidden variables which propagate to generate medical data at each time step. To avoid learning spurious correlation (e.g., confounding bias), we explicitly separate these hidden variables into three parts: a) the disease (clinical)-related part; b) the disease (non-clinical)-related part; c) others, with only a),b) causally related to the disease however c) may contain spurious correlations (with the disease) inherited from the data provided. With personal attributes and disease label respectively provided as side information and supervision, we prove that these disease-related hidden variables can be disentangled from others, implying the avoidance of spurious correlation for generalization to medical data from other (out-of-) distributions. Guaranteed by this result, we propose a sequential variational auto-encoder with a reformulated objective function. We apply our model to the early prediction of peripapillary atrophy and achieve promising results on out-of-distribution test data. Further, the ablation study empirically shows the effectiveness of each component in our method. And the visualization shows the accurate identification of lesion regions from others. | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_Causal_Hidden_Markov_Model_for_Time_Series_Disease_Forecasting_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.16391 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Causal_Hidden_Markov_Model_for_Time_Series_Disease_Forecasting_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Causal_Hidden_Markov_Model_for_Time_Series_Disease_Forecasting_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Li_Causal_Hidden_Markov_CVPR_2021_supplemental.pdf | null |
Generalizable Pedestrian Detection: The Elephant in the Room | Irtiza Hasan, Shengcai Liao, Jinpeng Li, Saad Ullah Akram, Ling Shao | Pedestrian detection is used in many vision based applications ranging from video surveillance to autonomous driving. Despite achieving high performance, it is still largely unknown how well existing detectors generalize to unseen data. This is important because a practical detector should be ready to use in various scenarios in applications. To this end, we conduct a comprehensive study in this paper, using a general principle of direct cross-dataset evaluation. Through this study, we find that existing state-of-the-art pedestrian detectors, though perform quite well when trained and tested on the same dataset, generalize poorly in cross dataset evaluation. We demonstrate that there are two reasons for this trend. Firstly, their designs (e.g. anchor settings) may be biased towards popular benchmarks in the traditional single-dataset training and test pipeline, but as a result largely limit their generalization capability. Secondly, the training source is generally not dense in pedestrians and diverse in scenarios. Under direct cross-dataset evaluation, surprisingly, we find that a general purpose object detector, without pedestrian-tailored adaptation in design, generalizes much better compared to existing state-of-the-art pedestrian detectors. Furthermore, we illustrate that diverse and dense datasets, collected by crawling the web, serve to be an efficient source of pre-training for pedestrian detection. Accordingly, we propose a progressive training pipeline and find that it works well for autonomous-driving oriented pedestrian detection. Consequently, the study conducted in this paper suggests that more emphasis should be put on cross-dataset evaluation for the future design of generalizable pedestrian detectors. Code and models can be accessed at https://github.com/hasanirtiza/Pedestron. | https://openaccess.thecvf.com/content/CVPR2021/papers/Hasan_Generalizable_Pedestrian_Detection_The_Elephant_in_the_Room_CVPR_2021_paper.pdf | http://arxiv.org/abs/2003.08799 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Hasan_Generalizable_Pedestrian_Detection_The_Elephant_in_the_Room_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Hasan_Generalizable_Pedestrian_Detection_The_Elephant_in_the_Room_CVPR_2021_paper.html | CVPR 2021 | null | null |
Focus on Local: Detecting Lane Marker From Bottom Up via Key Point | Zhan Qu, Huan Jin, Yang Zhou, Zhen Yang, Wei Zhang | Mainstream lane marker detection methods are implemented by predicting the overall structure and deriving parametric curves through post-processing. Complex lane line shapes require high-dimensional output of CNNs to model global structures, which further increases the demand for model capacity and training data. In contrast, the locality of a lane marker has finite geometric variations and spatial coverage. We propose a novel lane marker detection solution, FOLOLane, that focuses on modeling local patterns and achieving prediction of global structures in a bottom-up manner. Specifically, the CNN models low-complexity local patterns with two separate heads, the first one predicts the existence of key points, and the second refines the location of key points in the local range and correlates key points of the same lane line. The locality of the task is consistent with the limited FOV of the feature in CNN, which in turn leads to more stable training and better generalization. In addition, an efficiency-oriented decoding algorithm was proposed as well as a greedy one, which achieving 36% runtime gains at the cost of negligible performance degradation. Both of the two decoders integrated local information into the global geometry of lane markers. In the absence of a complex network architecture design, the proposed method greatly outperforms all existing methods on public datasets while achieving the best state-of-the-art results and real-time processing simultaneously. | https://openaccess.thecvf.com/content/CVPR2021/papers/Qu_Focus_on_Local_Detecting_Lane_Marker_From_Bottom_Up_via_CVPR_2021_paper.pdf | http://arxiv.org/abs/2105.13680 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Qu_Focus_on_Local_Detecting_Lane_Marker_From_Bottom_Up_via_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Qu_Focus_on_Local_Detecting_Lane_Marker_From_Bottom_Up_via_CVPR_2021_paper.html | CVPR 2021 | null | null |
Memory-Guided Unsupervised Image-to-Image Translation | Somi Jeong, Youngjung Kim, Eungbean Lee, Kwanghoon Sohn | We present a novel unsupervised framework for instance-level image-to-image translation. Although recent advances have been made by incorporating additional object annotations, existing methods often fail to handle images with multiple disparate objects. The main cause is that, during inference, they apply a global style to the whole image and do not consider the large style discrepancy between instance and background, or within instances. To address this problem, we propose a class-aware memory network that explicitly reasons about local style variations. A key-values memory structure, with a set of read/update operations, is introduced to record class-wise style variations and access them without requiring an object detector at the test time. The key stores a domain-agnostic content representation for allocating memory items, while the values encode domain-specific style representations. We also present a feature contrastive loss to boost the discriminative power of memory items. We show that by incorporating our memory, we can transfer class-aware and accurate style representations across domains. Experimental results demonstrate that our model outperforms recent instance-level methods and achieves state-of-the-art performance. | https://openaccess.thecvf.com/content/CVPR2021/papers/Jeong_Memory-Guided_Unsupervised_Image-to-Image_Translation_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.05170 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Jeong_Memory-Guided_Unsupervised_Image-to-Image_Translation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Jeong_Memory-Guided_Unsupervised_Image-to-Image_Translation_CVPR_2021_paper.html | CVPR 2021 | null | null |
Incremental Few-Shot Instance Segmentation | Dan Andrei Ganea, Bas Boom, Ronald Poppe | Few-shot instance segmentation methods are promising when labeled training data for novel classes is scarce. However, current approaches do not facilitate flexible addition of novel classes. They also require that examples of each class are provided at train and test time, which is memory intensive. In this paper, we address these limitations by presenting the first incremental approach to few-shot instance segmentation: iMTFA. We learn discriminative embeddings for object instances that are merged into class representatives. Storing embedding vectors rather than images effectively solves the memory overhead problem. We match these class embeddings at the RoI-level using cosine similarity. This allows us to add new classes without the need for further training or access to previous training data. In a series of experiments, we consistently outperform the current state-of-the-art. Moreover, the reduced memory requirements allow us to evaluate, for the first time, few-shot instance segmentation performance on all classes in COCO jointly. | https://openaccess.thecvf.com/content/CVPR2021/papers/Ganea_Incremental_Few-Shot_Instance_Segmentation_CVPR_2021_paper.pdf | http://arxiv.org/abs/2105.05312 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Ganea_Incremental_Few-Shot_Instance_Segmentation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Ganea_Incremental_Few-Shot_Instance_Segmentation_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Ganea_Incremental_Few-Shot_Instance_CVPR_2021_supplemental.pdf | null |
Mining Better Samples for Contrastive Learning of Temporal Correspondence | Sangryul Jeon, Dongbo Min, Seungryong Kim, Kwanghoon Sohn | We present a novel framework for contrastive learning of pixel-level representation using only unlabeled video. Without the need of ground-truth annotation, our method is capable of collecting well-defined positive correspondences by measuring their confidences and well-defined negative ones by appropriately adjusting their hardness during training. This allows us to suppress the adverse impact of ambiguous matches and prevent a trivial solution from being yielded by too hard or too easy negative samples. To accomplish this, we incorporate three different criteria that ranges from a pixel-level matching confidence to a video-level one into a bottom-up pipeline, and plan a curriculum that is aware of current representation power for the adaptive hardness of negative samples during training. With the proposed method, state-of-the-art performance is attained over the latest approaches on several video label propagation tasks. | https://openaccess.thecvf.com/content/CVPR2021/papers/Jeon_Mining_Better_Samples_for_Contrastive_Learning_of_Temporal_Correspondence_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Jeon_Mining_Better_Samples_for_Contrastive_Learning_of_Temporal_Correspondence_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Jeon_Mining_Better_Samples_for_Contrastive_Learning_of_Temporal_Correspondence_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Jeon_Mining_Better_Samples_CVPR_2021_supplemental.zip | null |
Scene-Aware Generative Network for Human Motion Synthesis | Jingbo Wang, Sijie Yan, Bo Dai, Dahua Lin | We revisit human motion synthesis, a task useful in various real-world applications, in this paper. Whereas a number of methods have been developed previously for this task, they are often limited in two aspects: 1) focus on the poses while leaving the location movement behind, and 2) ignore the impact of the environment on the human motion. In this paper, we propose a new framework, with the interaction between the scene and the human motion is taken into account. Considering the uncertainty of human motion, we formulate this task as a generative task, whose objective is to generate plausible human motion conditioned on both the scene and the human's initial position. This framework factorizes the distribution of human motions into a distribution of movement trajectories conditioned on scenes and that of body pose dynamics conditioned on both scenes and trajectories. We further derive a GAN-based learning approach, with discriminators to enforce the compatibility between the human motion and the contextual scene as well as the 3D-to-2D projection constraints. We assess the effectiveness of the proposed method on two challenging datasets, which cover both synthetic and real-world environmentemphasizes local structural constraints via depth-map crops, and a projection discriminator that emphasizes global structural constraints via 3D-to-2D motion projections. The effectiveness of our framework is comprehensively evaluated on two large challenging datasets, covering both a synthetic environment (GTA-IM) and a real environment (PROX) | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_Scene-Aware_Generative_Network_for_Human_Motion_Synthesis_CVPR_2021_paper.pdf | http://arxiv.org/abs/2105.14804 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Scene-Aware_Generative_Network_for_Human_Motion_Synthesis_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Scene-Aware_Generative_Network_for_Human_Motion_Synthesis_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wang_Scene-Aware_Generative_Network_CVPR_2021_supplemental.zip | null |
Learning Neural Representation of Camera Pose with Matrix Representation of Pose Shift via View Synthesis | Yaxuan Zhu, Ruiqi Gao, Siyuan Huang, Song-Chun Zhu, Ying Nian Wu | How to efficiently represent camera pose is an essential problem in 3D computer vision, especially in tasks like camera pose regression and novel view synthesis. Traditionally, 3D position of the camera is represented by Cartesian coordinate and the orientation is represented by Euler angle or quaternions. These representations are manually designed, which may not be the most efficient representation for downstream tasks. In this work, we propose an approach to learn neural representations of camera poses and 3D scenes, coupled with neural representations of local camera movements. Specifically, the camera pose and 3D scene are represented as vectors and the local camera movement is represented as a matrix operating on the vector of the camera pose. We demonstrate that the camera movement can further be parametrized as a matrix Lie algebra that underlies a rotation system in the neural space. The vector representations are then concatenated and generate the posed 2D image through a decoder network. The model is learned from only posed 2D images and corresponding camera poses, without access to depth or shape. We conduct extensive experiments on synthetic and real datasets. The results show that compared with other camera pose representations, our learned representation is more robust to noise in novel view synthesis and more effective in camera pose regression. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhu_Learning_Neural_Representation_of_Camera_Pose_with_Matrix_Representation_of_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.01508 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhu_Learning_Neural_Representation_of_Camera_Pose_with_Matrix_Representation_of_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhu_Learning_Neural_Representation_of_Camera_Pose_with_Matrix_Representation_of_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhu_Learning_Neural_Representation_CVPR_2021_supplemental.pdf | null |
PML: Progressive Margin Loss for Long-Tailed Age Classification | Zongyong Deng, Hao Liu, Yaoxing Wang, Chenyang Wang, Zekuan Yu, Xuehong Sun | In this paper, we propose a progressive margin loss (PML) approach for unconstrained facial age classification. Conventional methods make strong assumption on that each class owns adequate instances to outline its data distribution, likely leading to bias prediction where the training samples are sparse across age classes. Instead, our PML aims to adaptively refine the age label pattern by enforcing a couple of margins, which fully takes in the in-between discrepancy of the intra-class variance, inter-class variance and class-center. Our PML typically incorporates with the ordinal margin and the variational margin, simultaneously plugging in the globally-tuned deep neural network paradigm. More specifically, the ordinal margin learns to exploit the correlated relationship of the real-world age labels. Accordingly, the variational margin is leveraged to minimize the influence of head classes that misleads the prediction of tailed samples. Moreover, our optimization carefully seeks a series of indicator curricula to achieve robust and efficient model training. Extensive experimental results on three face aging datasets demonstrate that our PML achieves compelling performance compared to state of the arts. Code will be made publicly. | https://openaccess.thecvf.com/content/CVPR2021/papers/Deng_PML_Progressive_Margin_Loss_for_Long-Tailed_Age_Classification_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.02140 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Deng_PML_Progressive_Margin_Loss_for_Long-Tailed_Age_Classification_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Deng_PML_Progressive_Margin_Loss_for_Long-Tailed_Age_Classification_CVPR_2021_paper.html | CVPR 2021 | null | null |
Single Image Depth Prediction With Wavelet Decomposition | Michael Ramamonjisoa, Michael Firman, Jamie Watson, Vincent Lepetit, Daniyar Turmukhambetov | We present a novel method for predicting accurate depths from monocular images with high efficiency. This optimal efficiency is achieved by exploiting wavelet decomposition, which is integrated in a fully differentiable encoder-decoder architecture. We demonstrate that we can reconstruct high-fidelity depth maps by predicting sparse wavelet coefficients. In contrast with previous works, we show that wavelet coefficients can be learned without direct supervision on coefficients. Instead we supervise only the final depth image that is reconstructed through the inverse wavelet transform. We additionally show that wavelet coefficients can be learned in fully self-supervised scenarios, without access to ground-truth depth. Finally, we apply our method to different state-of-the-art monocular depth estimation models, in each case giving similar or better results compared to the original model, while requiring less than half the multiply-adds in the decoder network. | https://openaccess.thecvf.com/content/CVPR2021/papers/Ramamonjisoa_Single_Image_Depth_Prediction_With_Wavelet_Decomposition_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Ramamonjisoa_Single_Image_Depth_Prediction_With_Wavelet_Decomposition_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Ramamonjisoa_Single_Image_Depth_Prediction_With_Wavelet_Decomposition_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Ramamonjisoa_Single_Image_Depth_CVPR_2021_supplemental.pdf | null |
PVGNet: A Bottom-Up One-Stage 3D Object Detector With Integrated Multi-Level Features | Zhenwei Miao, Jikai Chen, Hongyu Pan, Ruiwen Zhang, Kaixuan Liu, Peihan Hao, Jun Zhu, Yang Wang, Xin Zhan | Quantization-based methods are widely used in LiDAR points 3D object detection for its efficiency in extracting context information. Unlike image where the context information is distributed evenly over the object, most LiDAR points are distributed along the object boundary, which means the boundary features are more critical in LiDAR points 3D detection. However, quantization inevitably introduces ambiguity during both the training and inference stages. To alleviate this problem, we propose a one-stage and voting-based 3D detector, named Point-Voxel-Grid Network (PVGNet). In particular, PVGNet extracts point, voxel and grid-level features in a unified backbone architecture and produces point-wise fusion features. It segments LiDAR points into foreground and background, predicts a 3D bounding box for each foreground point, and performs group voting to get the final detection results. Moreover, we observe that instance-level point imbalance due to occlusion and observation distance also degrades the detection performance. A novel instance-aware focal loss is proposed to alleviate this problem and further improve the detection ability. We conduct experiments on the KITTI and Waymo datasets. Our proposed PVGNet outperforms previous state-of-the-art methods and ranks at the top of KITTI 3D/BEV detection leaderboards. | https://openaccess.thecvf.com/content/CVPR2021/papers/Miao_PVGNet_A_Bottom-Up_One-Stage_3D_Object_Detector_With_Integrated_Multi-Level_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Miao_PVGNet_A_Bottom-Up_One-Stage_3D_Object_Detector_With_Integrated_Multi-Level_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Miao_PVGNet_A_Bottom-Up_One-Stage_3D_Object_Detector_With_Integrated_Multi-Level_CVPR_2021_paper.html | CVPR 2021 | null | null |
Exemplar-Based Open-Set Panoptic Segmentation Network | Jaedong Hwang, Seoung Wug Oh, Joon-Young Lee, Bohyung Han | We extend panoptic segmentation to the open-world and introduce an open-set panoptic segmentation (OPS) task. The task requires to perform panoptic segmentation for not only known classes but also unknown ones that are not acknowledged during training. We investigate challenges of the task and present a benchmark dataset on top of an existing dataset, COCO. In addition, we propose a novel exemplar-based open-set panoptic segmentation network (EOPSN) inspired by exemplar theory. Our approach identifies a new class with exemplars, which constructs pseudo-ground-truths, based on clustering and augments the size of each class by adding new exemplars based on their similarity during training. We evaluate the proposed method on our benchmark and demonstrate the effectiveness of our proposals. The goal of our work is to draw the attention of the community to the recognition in open-world scenarios. | https://openaccess.thecvf.com/content/CVPR2021/papers/Hwang_Exemplar-Based_Open-Set_Panoptic_Segmentation_Network_CVPR_2021_paper.pdf | http://arxiv.org/abs/2105.08336 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Hwang_Exemplar-Based_Open-Set_Panoptic_Segmentation_Network_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Hwang_Exemplar-Based_Open-Set_Panoptic_Segmentation_Network_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Hwang_Exemplar-Based_Open-Set_Panoptic_CVPR_2021_supplemental.pdf | null |
KOALAnet: Blind Super-Resolution Using Kernel-Oriented Adaptive Local Adjustment | Soo Ye Kim, Hyeonjun Sim, Munchurl Kim | Blind super-resolution (SR) methods aim to generate a high quality high resolution image from a low resolution image containing unknown degradations. However, natural images contain various types and amounts of blur: some may be due to the inherent degradation characteristics of the camera, but some may even be intentional, for aesthetic purposes (e.g. Bokeh effect). In the case of the latter, it becomes highly difficult for SR methods to disentangle the blur to remove, and that to leave as is. In this paper, we propose a novel blind SR framework based on kernel-oriented adaptive local adjustment (KOALA) of SR features, called KOALAnet, which jointly learns spatially-variant degradation and restoration kernels in order to adapt to the spatially-variant blur characteristics in real images. Our KOALAnet outperforms recent blind SR methods for synthesized LR images obtained with randomized degradations, and we further show that the proposed KOALAnet produces the most natural results for artistic photographs with intentional blur, which are not over-sharpened, by effectively handling images mixed with in-focus and out-of-focus areas. | https://openaccess.thecvf.com/content/CVPR2021/papers/Kim_KOALAnet_Blind_Super-Resolution_Using_Kernel-Oriented_Adaptive_Local_Adjustment_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.08103 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Kim_KOALAnet_Blind_Super-Resolution_Using_Kernel-Oriented_Adaptive_Local_Adjustment_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Kim_KOALAnet_Blind_Super-Resolution_Using_Kernel-Oriented_Adaptive_Local_Adjustment_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Kim_KOALAnet_Blind_Super-Resolution_CVPR_2021_supplemental.pdf | null |
Learning Deep Classifiers Consistent With Fine-Grained Novelty Detection | Jiacheng Cheng, Nuno Vasconcelos | The problem of novelty detection in fine-grained visual classification (FGVC) is considered. An integrated understanding of the probabilistic and distance-based approaches to novelty detection is developed within the framework of convolutional neural networks (CNNs). It is shown that softmax CNN classifiers are inconsistent with novelty detection, because their learned class-conditional distributions and associated distance metrics are unidentifiable. A new regularization constraint, the class-conditional Gaussianity loss, is then proposed to eliminate this unidentifiability, and enforce Gaussian class-conditional distributions. This enables training Novelty Detection Consistent Classifiers (NDCCs) that are jointly optimal for classification and novelty detection. Empirical evaluations show that NDCCs achieve significant improvements over the state-of-the-art on both small- and large-scale FGVC datasets. | https://openaccess.thecvf.com/content/CVPR2021/papers/Cheng_Learning_Deep_Classifiers_Consistent_With_Fine-Grained_Novelty_Detection_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Cheng_Learning_Deep_Classifiers_Consistent_With_Fine-Grained_Novelty_Detection_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Cheng_Learning_Deep_Classifiers_Consistent_With_Fine-Grained_Novelty_Detection_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Cheng_Learning_Deep_Classifiers_CVPR_2021_supplemental.pdf | null |
Multiple Object Tracking With Correlation Learning | Qiang Wang, Yun Zheng, Pan Pan, Yinghui Xu | Recent works have shown that convolutional networks have substantially improved the performance of multiple object tracking by simultaneously learning detection and appearance features. However, due to the local perception of the convolutional network structure itself, the long-range dependencies in both the spatial and temporal cannot be obtained efficiently. To incorporate the spatial layout, we propose to exploit the local correlation module to model the topological relationship between targets and their surrounding environment, which can enhance the discriminative power of our model in crowded scenes. Specifically, we establish dense correspondences of each spatial location and its context, and explicitly constrain the correlation volumes through self-supervised learning. To exploit the temporal context, existing approaches generally utilize two or more adjacent frames to construct an enhanced feature representation, but the dynamic motion scene is inherently difficult to depict via CNNs. Instead, our paper proposes a learnable correlation operator to establish frame-to-frame matches over convolutional feature maps in the different layers to align and propagate temporal context. With extensive experimental results on the MOT datasets, our approach demonstrates the effectiveness of correlation learning with the superior performance and obtains state-of-the-art MOTA of 76.5% and IDF1 of 73.6% on MOT17. | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_Multiple_Object_Tracking_With_Correlation_Learning_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.03541 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Multiple_Object_Tracking_With_Correlation_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Multiple_Object_Tracking_With_Correlation_Learning_CVPR_2021_paper.html | CVPR 2021 | null | null |
SAIL-VOS 3D: A Synthetic Dataset and Baselines for Object Detection and 3D Mesh Reconstruction From Video Data | Yuan-Ting Hu, Jiahong Wang, Raymond A. Yeh, Alexander G. Schwing | Extracting detailed 3D information of objects from video data is an important goal for holistic scene understanding. While recent methods have shown impressive results when reconstructing meshes of objects from a single image, results often remain ambiguous as part of the object is unobserved. Moreover, existing image-based datasets for mesh reconstruction don't permit to study models which integrate temporal information. To alleviate both concerns we present SAIL-VOS 3D: a synthetic video dataset with frame-by-frame mesh annotations which extends SAIL-VOS. We also develop first baselines for reconstruction of 3D meshes from video data via temporal models. We demonstrate efficacy of the proposed baseline on SAIL-VOS 3D and Pix3D, showing that temporal information improves reconstruction quality. Resources and additional information are available at http://sailvos.web.illinois.edu. | https://openaccess.thecvf.com/content/CVPR2021/papers/Hu_SAIL-VOS_3D_A_Synthetic_Dataset_and_Baselines_for_Object_Detection_CVPR_2021_paper.pdf | http://arxiv.org/abs/2105.08612 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Hu_SAIL-VOS_3D_A_Synthetic_Dataset_and_Baselines_for_Object_Detection_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Hu_SAIL-VOS_3D_A_Synthetic_Dataset_and_Baselines_for_Object_Detection_CVPR_2021_paper.html | CVPR 2021 | null | null |
PixMatch: Unsupervised Domain Adaptation via Pixelwise Consistency Training | Luke Melas-Kyriazi, Arjun K. Manrai | Unsupervised domain adaptation is a promising technique for semantic segmentation and other computer vision tasks for which large-scale data annotation is costly and time-consuming. In semantic segmentation particularly, it is attractive to train models on annotated images from a simulated (source) domain and deploy them on real (target) domains. In this work, we present a novel framework for unsupervised domain adaptation based on the notion of target-domain consistency training. Intuitively, our work is based on the insight that in order to perform well on the target domain, a model's output should be consistent with respect to small perturbations of inputs in the target domain. Specifically, we introduce a new loss term to enforce pixelwise consistency between the model's predictions on a target image and perturbed version of the same image. In comparison to popular adversarial adaptation methods, our approach is simpler, easier to implement, and more memory-efficient during training. Experiments and ablation studies demonstrate that our simple approach achieves remarkably strong results on two challenging synthetic-to-real benchmarks, GTA5-to-Cityscapes and SYNTHIA-to-Cityscapes. | https://openaccess.thecvf.com/content/CVPR2021/papers/Melas-Kyriazi_PixMatch_Unsupervised_Domain_Adaptation_via_Pixelwise_Consistency_Training_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Melas-Kyriazi_PixMatch_Unsupervised_Domain_Adaptation_via_Pixelwise_Consistency_Training_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Melas-Kyriazi_PixMatch_Unsupervised_Domain_Adaptation_via_Pixelwise_Consistency_Training_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Melas-Kyriazi_PixMatch_Unsupervised_Domain_CVPR_2021_supplemental.pdf | null |
Deep RGB-D Saliency Detection With Depth-Sensitive Attention and Automatic Multi-Modal Fusion | Peng Sun, Wenhu Zhang, Huanyu Wang, Songyuan Li, Xi Li | RGB-D salient object detection (SOD) is usually formulated as a problem of classification or regression over two modalities, i.e., RGB and depth. Hence, effective RGB-D feature modeling and multi-modal feature fusion both play a vital role in RGB-D SOD. In this paper, we propose a depth-sensitive RGB feature modeling scheme using the depth-wise geometric prior of salient objects. In principle, the feature modeling scheme is carried out in a depth-sensitive attention module, which leads to the RGB feature enhancement as well as the background distraction reduction by capturing the depth geometry prior. Moreover, to perform effective multi-modal feature fusion, we further present an automatic architecture search approach for RGB-D SOD, which does well in finding out a feasible architecture from our specially designed multi-modal multi-scale search space. Extensive experiments on seven standard benchmarks demonstrate the effectiveness of the proposed approach against the state-of-the-art. | https://openaccess.thecvf.com/content/CVPR2021/papers/Sun_Deep_RGB-D_Saliency_Detection_With_Depth-Sensitive_Attention_and_Automatic_Multi-Modal_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Sun_Deep_RGB-D_Saliency_Detection_With_Depth-Sensitive_Attention_and_Automatic_Multi-Modal_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Sun_Deep_RGB-D_Saliency_Detection_With_Depth-Sensitive_Attention_and_Automatic_Multi-Modal_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Sun_Deep_RGB-D_Saliency_CVPR_2021_supplemental.pdf | null |
Exploring Sparsity in Image Super-Resolution for Efficient Inference | Longguang Wang, Xiaoyu Dong, Yingqian Wang, Xinyi Ying, Zaiping Lin, Wei An, Yulan Guo | Current CNN-based super-resolution (SR) methods process all locations equally with computational resources being uniformly assigned in space. However, since missing details in low-resolution (LR) images mainly exist in regions of edges and textures, less computational resources are required for those flat regions. Therefore, existing CNN-based methods involve redundant computation in flat regions, which increases their computational cost and limits their applications on mobile devices. In this paper, we explore the sparsity in image SR to improve inference efficiency of SR networks. Specifically, we develop a Sparse Mask SR (SMSR) network to learn sparse masks to prune redundant computation. Within our SMSR, spatial masks learn to identify "important" regions while channel masks learn to mark redundant channels in those "unimportant" regions. Consequently, redundant computation can be accurately localized and skipped while maintaining comparable performance. It is demonstrated that our SMSR achieves state-of-the-art performance with 41%/33%/27% FLOPs being reduced for x2/3/4 SR. Code is available at: https://github.com/LongguangWang/SMSR. | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_Exploring_Sparsity_in_Image_Super-Resolution_for_Efficient_Inference_CVPR_2021_paper.pdf | http://arxiv.org/abs/2006.09603 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Exploring_Sparsity_in_Image_Super-Resolution_for_Efficient_Inference_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Exploring_Sparsity_in_Image_Super-Resolution_for_Efficient_Inference_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wang_Exploring_Sparsity_in_CVPR_2021_supplemental.pdf | null |
Positive Sample Propagation Along the Audio-Visual Event Line | Jinxing Zhou, Liang Zheng, Yiran Zhong, Shijie Hao, Meng Wang | Visual and audio signals often coexist in natural environments, forming audio-visual events (AVEs). Given a video, we aim to localize video segments containing an AVE and identify its category. In order to learn discriminative features for a classifier, it is pivotal to identify the helpful (or positive) audio-visual segment pairs while filtering out the irrelevant ones, regardless whether they are synchronized or not. To this end, we propose a new positive sample propagation (PSP) module to discover and exploit the closely related audio-visual pairs by evaluating the relationship within every possible pair. It can be done by constructing an all-pair similarity map between each audio and visual segment, and only aggregating the features from the pairs with high similarity scores. To encourage the network to extract high correlated features for positive samples, a new audio-visual pair similarity loss is proposed. We also propose a new weighting branch to better exploit the temporal correlations in weakly supervised setting. We perform extensive experiments on the public AVE dataset and achieve new state-of-the-art accuracy in both fully and weakly supervised settings, thus verifying the effectiveness of our method. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhou_Positive_Sample_Propagation_Along_the_Audio-Visual_Event_Line_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.00239 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhou_Positive_Sample_Propagation_Along_the_Audio-Visual_Event_Line_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhou_Positive_Sample_Propagation_Along_the_Audio-Visual_Event_Line_CVPR_2021_paper.html | CVPR 2021 | null | null |
Understanding the Behaviour of Contrastive Loss | Feng Wang, Huaping Liu | Unsupervised contrastive learning has achieved outstanding success, while the mechanism of contrastive loss has been less studied. In this paper, we concentrate on the understanding of the behaviours of unsupervised contrastive loss. We will show that the contrastive loss is a hardness-aware loss function, and the temperature t controls the strength of penalties on hard negative samples. The previous study has shown that uniformity is a key property of contrastive learning. We build relations between the uniformity and the temperature t. We will show that uniformity helps the contrastive learning to learn separable features, however excessive pursuit to the uniformity makes the contrastive loss not tolerant to semantically similar samples, which may break the underlying semantic structure and be harmful to the formation of features useful for downstream tasks. This is caused by the inherent defect of the instance discrimination objective. Specifically, instance discrimination objective tries to push all different instances apart, ignoring the underlying relations between samples. Pushing semantically consistent samples apart has no positive effect for acquiring a prior informative to general downstream tasks. A well-designed contrastive loss should have some extents of tolerance to the closeness of semantically similar samples. Therefore, we find that the contrastive loss meets a uniformity-tolerance dilemma, and a good choice of temperature can compromise these two properties properly to both learn separable features and tolerant to semantically similar samples, improving the feature qualities and the downstream performances. | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_Understanding_the_Behaviour_of_Contrastive_Loss_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.09740 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Understanding_the_Behaviour_of_Contrastive_Loss_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Understanding_the_Behaviour_of_Contrastive_Loss_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wang_Understanding_the_Behaviour_CVPR_2021_supplemental.pdf | null |
Variational Prototype Learning for Deep Face Recognition | Jiankang Deng, Jia Guo, Jing Yang, Alexandros Lattas, Stefanos Zafeiriou | Deep face recognition has achieved remarkable improvements due to the introduction of margin-based softmax loss, in which the prototype stored in the last linear layer represents the center of each class. In these methods, training samples are enforced to be close to positive prototypes and far apart from negative prototypes by a clear margin. However, we argue that prototype learning only employs sample-to-prototype comparisons without considering sample-to-sample comparisons during training and the low loss value gives us an illusion of perfect feature embedding, impeding the further exploration of SGD. To this end, we propose Variational Prototype Learning (VPL), which represents every class as a distribution instead of a point in the latent space. By identifying the slow feature drift phenomenon, we directly inject memorized features into prototypes to approximate variational prototype sampling. The proposed VPL can simulate sample-to-sample comparisons within the classification framework, encouraging the SGD solver to be more exploratory, while boosting performance. Moreover, VPL is conceptually simple, easy to implement, computationally efficient and memory saving. We present extensive experimental results on popular benchmarks, which demonstrate the superiority of the proposed VPL method over the state-of-the-art competitors. | https://openaccess.thecvf.com/content/CVPR2021/papers/Deng_Variational_Prototype_Learning_for_Deep_Face_Recognition_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Deng_Variational_Prototype_Learning_for_Deep_Face_Recognition_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Deng_Variational_Prototype_Learning_for_Deep_Face_Recognition_CVPR_2021_paper.html | CVPR 2021 | null | null |
StylePeople: A Generative Model of Fullbody Human Avatars | Artur Grigorev, Karim Iskakov, Anastasia Ianina, Renat Bashirov, Ilya Zakharkin, Alexander Vakhitov, Victor Lempitsky | We propose a new type of full-body human avatars, which combines parametric mesh-based body model with a neural texture. We show that with the help of neural textures, such avatars can successfully model clothing and hair, which usually poses a problem for mesh-based approaches. We also show how these avatars can be created from multiple frames of a video using backpropagation. We then propose a generative model for such avatars that can be trained from datasets of images and videos of people. The generative model allows us to sample random avatars as well as to create dressed avatars of people from one or few images. | https://openaccess.thecvf.com/content/CVPR2021/papers/Grigorev_StylePeople_A_Generative_Model_of_Fullbody_Human_Avatars_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.08363 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Grigorev_StylePeople_A_Generative_Model_of_Fullbody_Human_Avatars_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Grigorev_StylePeople_A_Generative_Model_of_Fullbody_Human_Avatars_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Grigorev_StylePeople_A_Generative_CVPR_2021_supplemental.zip | null |
Optimal Quantization Using Scaled Codebook | Yerlan Idelbayev, Pavlo Molchanov, Maying Shen, Hongxu Yin, Miguel A. Carreira-Perpinan, Jose M. Alvarez | We study the problem of quantizing N sorted, scalar datapoints with a fixed codebook containing K entries that are allowed to be rescaled. The problem is defined as finding the optimal scaling factor \alpha and the datapoint assignments into the \alpha-scaled codebook to minimize the squared error between original and quantized points. Previously, the globally optimal algorithms for this problem were derived only for certain codebooks (binary and ternary) or under the assumption of certain distributions (Gaussian, Laplacian). By studying the properties of the optimal quantizer, we derive an \calO(NK \log K) algorithm that is guaranteed to find the optimal quantization parameters for any fixed codebook regardless of data distribution. We apply our algorithm to synthetic and real-world neural network quantization problems and demonstrate the effectiveness of our approach. | https://openaccess.thecvf.com/content/CVPR2021/papers/Idelbayev_Optimal_Quantization_Using_Scaled_Codebook_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Idelbayev_Optimal_Quantization_Using_Scaled_Codebook_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Idelbayev_Optimal_Quantization_Using_Scaled_Codebook_CVPR_2021_paper.html | CVPR 2021 | null | null |
RPN Prototype Alignment for Domain Adaptive Object Detector | Yixin Zhang, Zilei Wang, Yushi Mao | Recent years have witnessed great progress in object detection. However, due to the domain shift problem, applying the knowledge of an object detector learned from one specific domain to another one often suffers severe performance degradation. Most existing methods adopt feature alignment either on the backbone network or instance classifier to increase the transferability of object detector. Different from existing methods, we propose to perform feature alignment of foreground and background in the RPN stage such that the foreground and background RPN proposals in target domain can be effectively separated. Specifically, we first construct one set of learnable RPN prototypes, and then enforce the RPN features to align with the prototypes for both source and target domains. It essentially cooperates the learning of RPN prototypes and features to align the source and target RPN features. In this paradigm, the pseudo label of proposals in target domain need be first generated, and we propose a simple yet effective method suitable for RPN feature alignment,i.e., using the filtered detection results to guide the pseudo label generation of RPN proposals by IoU. Furthermore, we adopt Grad CAM to find the discriminative region within a proposal and use it to increase the discriminability of RPN features for alignment by spatially weighting. We conduct extensive experiments on multiple cross-domain detection scenarios. The results show the effectiveness of our proposed method against previous state-of-the-art methods. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_RPN_Prototype_Alignment_for_Domain_Adaptive_Object_Detector_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_RPN_Prototype_Alignment_for_Domain_Adaptive_Object_Detector_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_RPN_Prototype_Alignment_for_Domain_Adaptive_Object_Detector_CVPR_2021_paper.html | CVPR 2021 | null | null |
Dual Contradistinctive Generative Autoencoder | Gaurav Parmar, Dacheng Li, Kwonjoon Lee, Zhuowen Tu | We present a new generative autoencoder model with dual contradistinctive losses to improve generative autoencoder that performs simultaneous inference (reconstruction) and synthesis (sampling). Our model, named dual contradistinctive generative autoencoder (DC-VAE), integrates an instance-level discriminative loss (maintaining the instancelevel fidelity for the reconstruction / synthesis) with a set-level adversarial loss (encouraging the set-level fidelity for the reconstruction/synthesis), both being contradistinctive. Extensive experimental results by DC-VAE across different resolutions including 32x32, 64x64, 128x128, and 512x512 are reported. The two contradistinctive losses in VAE work harmoniously in DC-VAE leading to a significant qualitative and quantitative performance enhancement over the baseline VAEs without architectural changes. State-of-the-art or competitive results among generative autoencoders for image reconstruction, image synthesis, image interpolation, and representation learning are observed. DC-VAE is a general-purpose VAE model, applicable to a wide variety of downstream tasks in computer vision and machine learning. | https://openaccess.thecvf.com/content/CVPR2021/papers/Parmar_Dual_Contradistinctive_Generative_Autoencoder_CVPR_2021_paper.pdf | http://arxiv.org/abs/2011.10063 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Parmar_Dual_Contradistinctive_Generative_Autoencoder_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Parmar_Dual_Contradistinctive_Generative_Autoencoder_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Parmar_Dual_Contradistinctive_Generative_CVPR_2021_supplemental.pdf | null |
Binary TTC: A Temporal Geofence for Autonomous Navigation | Abhishek Badki, Orazio Gallo, Jan Kautz, Pradeep Sen | Time-to-contact (TTC), the time for an object to collide with the observer's plane, is a powerful tool for path planning: it is potentially more informative than the depth, velocity, and acceleration of objects in the scene---even for humans. TTC presents several advantages, including requiring only a monocular, uncalibrated camera. However, regressing TTC for each pixel is not straightforward, and most existing methods make over-simplifying assumptions about the scene. We address this challenge by estimating TTC via a series of simpler, binary classifications. We predict with low latency whether the observer will collide with an obstacle within a certain time, which is often more critical than knowing exact, per-pixel TTC. For such scenarios, our method offers a temporal geofence in 6.4 ms---over 25x faster than existing methods. Our approach can also estimate per-pixel TTC with arbitrarily fine quantization (including continuous values), when the computational budget allows for it. To the best of our knowledge, our method is the first to offer TTC information (binary or coarsely quantized) at sufficiently high frame-rates for practical use. | https://openaccess.thecvf.com/content/CVPR2021/papers/Badki_Binary_TTC_A_Temporal_Geofence_for_Autonomous_Navigation_CVPR_2021_paper.pdf | http://arxiv.org/abs/2101.04777 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Badki_Binary_TTC_A_Temporal_Geofence_for_Autonomous_Navigation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Badki_Binary_TTC_A_Temporal_Geofence_for_Autonomous_Navigation_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Badki_Binary_TTC_A_CVPR_2021_supplemental.pdf | null |
Semantic-Aware Video Text Detection | Wei Feng, Fei Yin, Xu-Yao Zhang, Cheng-Lin Liu | Most existing video text detection methods track texts with appearance features, which are easily influenced by the change of perspective and illumination. Compared with appearance features, semantic features are more robust cues for matching text instances. In this paper, we propose an end-to-end trainable video text detector that tracks texts based on semantic features. First, we introduce a new character center segmentation branch to extract semantic features, which encode the category and position of characters. Then we propose a novel appearance-semantic-geometry descriptor to track text instances, in which semantic features can improve the robustness against appearance changes. To overcome the lack of character-level annotations, we propose a novel weakly-supervised character center detection module, which only uses word-level annotated real images to generate character-level labels. The proposed method achieves state-of-the-art performance on three video text benchmarks ICDAR 2013 Video, Minetto and RT-1K, and two Chinese scene text benchmarks CASIA10K and MSRA-TD500. | https://openaccess.thecvf.com/content/CVPR2021/papers/Feng_Semantic-Aware_Video_Text_Detection_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Feng_Semantic-Aware_Video_Text_Detection_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Feng_Semantic-Aware_Video_Text_Detection_CVPR_2021_paper.html | CVPR 2021 | null | null |
Real-Time High-Resolution Background Matting | Shanchuan Lin, Andrey Ryabtsev, Soumyadip Sengupta, Brian L. Curless, Steven M. Seitz, Ira Kemelmacher-Shlizerman | We introduce a real-time, high-resolution background replacement technique which operates at 30fps in 4K resolution, and 60fps for HD on a modern GPU. Our technique is based on background matting, where an additional frame of the background is captured and used to inform the alpha matte and the foreground layer. The main challenge is to compute a high-quality alpha matte, preserving strand-level hair details, while processing high-resolution images in real-time. To achieve this goal, we employ two neural networks; the base network computes a low-resolution result which is refined by a second network operating at high-resolution on selective patches. We introduce two large-scale video and image matting datasets: VideoMatte240K and PhotoMatte13K/85. Our approach yields higher quality results compared to the previous state-of-the-art in background matting, while simultaneously yielding a dramatic boost in both speed and resolution. | https://openaccess.thecvf.com/content/CVPR2021/papers/Lin_Real-Time_High-Resolution_Background_Matting_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.07810 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Lin_Real-Time_High-Resolution_Background_Matting_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Lin_Real-Time_High-Resolution_Background_Matting_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Lin_Real-Time_High-Resolution_Background_CVPR_2021_supplemental.pdf | null |
Interpretable Social Anchors for Human Trajectory Forecasting in Crowds | Parth Kothari, Brian Sifringer, Alexandre Alahi | Human trajectory forecasting in crowds, at its core, is a sequence prediction problem with specific challenges of capturing inter-sequence dependencies (social interactions) and consequently predicting socially-compliant multimodal distributions. In recent years, neural network-based methods have been shown to outperform hand-crafted methods on distance-based metrics. However, these data-driven methods still suffer from one crucial limitation: lack of interpretability. To overcome this limitation, we leverage the power of discrete choice models to learn interpretable rule-based intents, and subsequently utilise the expressibility of neural networks to model scene-specific residual. Extensive experimentation on the interaction-centric benchmark TrajNet++ demonstrates the effectiveness of our proposed architecture to explain its predictions without compromising the accuracy. | https://openaccess.thecvf.com/content/CVPR2021/papers/Kothari_Interpretable_Social_Anchors_for_Human_Trajectory_Forecasting_in_Crowds_CVPR_2021_paper.pdf | http://arxiv.org/abs/2105.03136 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Kothari_Interpretable_Social_Anchors_for_Human_Trajectory_Forecasting_in_Crowds_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Kothari_Interpretable_Social_Anchors_for_Human_Trajectory_Forecasting_in_Crowds_CVPR_2021_paper.html | CVPR 2021 | null | null |
Trajectory Prediction With Latent Belief Energy-Based Model | Bo Pang, Tianyang Zhao, Xu Xie, Ying Nian Wu | Human trajectory prediction is critical for autonomous platforms like self-driving cars or social robots. We present a latent belief energy-based model (LB-EBM) for diverse human trajectory forecast. LB-EBM is a probabilistic model with cost function defined in the latent space to account for the movement history and social context. The low-dimensionality of the latent space and the high expressivity of the EBM make it easy for the model to capture the multimodality of pedestrian trajectory distributions. LB-EBM is learned from expert demonstrations (i.e., human trajectories) projected into the latent space. Sampling from or optimizing the learned LB-EBM yields a belief vector which is used to make a path plan, which then in turn helps to predict a long-range trajectory. The effectiveness of LB-EBM and the two-step approach are supported by strong empirical results. Our model is able to make accurate, multi-modal, and social compliant trajectory predictions and improves over prior state-of-the-arts performance on the Stanford Drone trajectory prediction benchmark by 10.9% and on the ETH-UCY benchmark by 27.6%. | https://openaccess.thecvf.com/content/CVPR2021/papers/Pang_Trajectory_Prediction_With_Latent_Belief_Energy-Based_Model_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.03086 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Pang_Trajectory_Prediction_With_Latent_Belief_Energy-Based_Model_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Pang_Trajectory_Prediction_With_Latent_Belief_Energy-Based_Model_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Pang_Trajectory_Prediction_With_CVPR_2021_supplemental.pdf | null |
Metadata Normalization | Mandy Lu, Qingyu Zhao, Jiequan Zhang, Kilian M. Pohl, Li Fei-Fei, Juan Carlos Niebles, Ehsan Adeli | Batch Normalization (BN) and its variants have delivered tremendous success in combating the covariate shift induced by the training step of deep learning methods. While these techniques normalize the feature distribution by standardizing with batch statistics, they do not correct the influence on features from extraneous variables or multiple distributions. Such extra variables, referred to as metadata here, may create bias or confounding effects (e.g., race when classifying gender from face images). We introduce the Metadata Normalization (MDN) layer, a new batch-level operation which can be used end-to-end within the training framework, to correct the influence of metadata on the feature distribution. MDN adopts a regression analysis technique traditionally used for preprocessing to remove (regress out) the metadata effects on model features during training. We utilize a metric based on distance correlation to quantify the distribution bias from the metadata and demonstrate that our method successfully removes metadata effects on four diverse settings: one synthetic, one 2D image, one video, and one 3D medical image dataset. | https://openaccess.thecvf.com/content/CVPR2021/papers/Lu_Metadata_Normalization_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.09052 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Lu_Metadata_Normalization_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Lu_Metadata_Normalization_CVPR_2021_paper.html | CVPR 2021 | null | null |
Multi-Objective Interpolation Training for Robustness To Label Noise | Diego Ortego, Eric Arazo, Paul Albert, Noel E. O'Connor, Kevin McGuinness | Deep neural networks trained with standard cross-entropy loss memorize noisy labels, which degrades their performance. Most research to mitigate this memorization proposes new robust classification loss functions. Conversely, we propose a Multi-Objective Interpolation Training (MOIT) approach that jointly exploits contrastive learning and classification to mutually help each other and boost performance against label noise. We show that standard supervised contrastive learning degrades in the presence of label noise and propose an interpolation training strategy to mitigate this behavior. We further propose a novel label noise detection method that exploits the robust feature representations learned via contrastive learning to estimate per-sample soft-labels whose disagreements with the original labels accurately identify noisy samples. This detection allows treating noisy samples as unlabeled and training a classifier in a semi-supervised manner to prevent noise memorization and improve representation learning. We further propose MOIT+, a refinement of MOIT by fine-tuning on detected clean samples. Hyperparameter and ablation studies verify the key components of our method. Experiments on synthetic and real-world noise benchmarks demonstrate that MOIT/MOIT+ achieves state-of-the-art results. Code is available at https://git.io/JI40X. | https://openaccess.thecvf.com/content/CVPR2021/papers/Ortego_Multi-Objective_Interpolation_Training_for_Robustness_To_Label_Noise_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.04462 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Ortego_Multi-Objective_Interpolation_Training_for_Robustness_To_Label_Noise_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Ortego_Multi-Objective_Interpolation_Training_for_Robustness_To_Label_Noise_CVPR_2021_paper.html | CVPR 2021 | null | null |
PhySG: Inverse Rendering With Spherical Gaussians for Physics-Based Material Editing and Relighting | Kai Zhang, Fujun Luan, Qianqian Wang, Kavita Bala, Noah Snavely | We present an end-to-end inverse rendering pipeline that includes a fully differentiable renderer, and can reconstruct geometry, materials, and illumination from scratch from a set of images. Our rendering framework represents specular BRDFs and environmental illumination using mixtures of spherical Gaussians, and represents geometry as a signed distance function parameterized as a Multi-Layer Perceptron. The use of spherical Gaussians allows us to efficiently solve for approximate light transport, and our method works on scenes with challenging non-Lambertian reflectance captured under natural, static illumination. We demonstrate, with both synthetic and real data, that our reconstruction not only can render novel viewpoints, but also enables physics-based appearance editing of materials and illumination. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_PhySG_Inverse_Rendering_With_Spherical_Gaussians_for_Physics-Based_Material_Editing_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.00674 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_PhySG_Inverse_Rendering_With_Spherical_Gaussians_for_Physics-Based_Material_Editing_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_PhySG_Inverse_Rendering_With_Spherical_Gaussians_for_Physics-Based_Material_Editing_CVPR_2021_paper.html | CVPR 2021 | null | null |
Predator: Registration of 3D Point Clouds With Low Overlap | Shengyu Huang, Zan Gojcic, Mikhail Usvyatsov, Andreas Wieser, Konrad Schindler | We introduce PREDATOR, a model for pairwise pointcloud registration with deep attention to the overlap region. Different from previous work, our model is specifically designed to handle (also) point-cloud pairs with low overlap. Its key novelty is an overlap-attention block for early information exchange between the latent encodings of the two point clouds. In this way the subsequent decoding of the latent representations into per-point features is conditioned on the respective other point cloud, and thus can predict which points are not only salient, but also lie in the overlap region between the two point clouds. The ability to focus on points that are relevant for matching greatly improves performance: PREDATOR raises the rate of successful registrations by more than 20% in the low-overlap scenario, and also sets a new state of the art for the 3DMatch benchmark with 89% registration recall. | https://openaccess.thecvf.com/content/CVPR2021/papers/Huang_Predator_Registration_of_3D_Point_Clouds_With_Low_Overlap_CVPR_2021_paper.pdf | http://arxiv.org/abs/2011.13005 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Huang_Predator_Registration_of_3D_Point_Clouds_With_Low_Overlap_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Huang_Predator_Registration_of_3D_Point_Clouds_With_Low_Overlap_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Huang_Predator_Registration_of_CVPR_2021_supplemental.pdf | null |
Hierarchical Motion Understanding via Motion Programs | Sumith Kulal, Jiayuan Mao, Alex Aiken, Jiajun Wu | Current approaches to video analysis of human motion focus on raw pixels or keypoints as the basic units of reasoning. We posit that adding higher-level motion primitives, which can capture natural coarser units of motion such as backswing or follow-through, can be used to improve downstream analysis tasks. This higher level of abstraction can also capture key features, such as loops of repeated primitives, that are currently inaccessible at lower levels of representation. We therefore introduce Motion Programs, a neuro-symbolic, program-like representation that expresses motions as a composition of high-level primitives. We also present a system for automatically inducing motion programs from videos of human motion and for leveraging motion programs in video synthesis. Experiments show that motion programs can accurately describe a diverse set of human motions and the inferred programs contain semantically meaningful motion primitives, such as arm swings and jumping jacks. Our representation also benefits downstream tasks such as video interpolation and video prediction and outperforms off-the-shelf models. We further demonstrate how these programs can detect diverse kinds of repetitive motion and facilitate interactive video editing. | https://openaccess.thecvf.com/content/CVPR2021/papers/Kulal_Hierarchical_Motion_Understanding_via_Motion_Programs_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.11216 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Kulal_Hierarchical_Motion_Understanding_via_Motion_Programs_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Kulal_Hierarchical_Motion_Understanding_via_Motion_Programs_CVPR_2021_paper.html | CVPR 2021 | null | null |
Neural Side-by-Side: Predicting Human Preferences for No-Reference Super-Resolution Evaluation | Valentin Khrulkov, Artem Babenko | Super-resolution based on deep convolutional networks is currently gaining much attention from both academia and industry. However, lack of proper evaluation measures makes it difficult to compare approaches, hampering progress in the field. Traditional measures, such as PSNR or SSIM, are known to poorly correlate with the human perception of image quality. Therefore, in existing works common practice is also to report Mean-Opinion-Score (MOS) -- the results of human evaluation of super-resolved images. Unfortunately, the MOS values from different papers are not directly comparable, due to the varying number of raters, their subjectivity, etc. By this paper, we introduce Neural Side-By-Side -- a new measure that allows super-resolution models to be compared automatically, effectively approximating human preferences. Namely, we collect a large dataset of aligned image pairs, which were produced by different super-resolution models. Then each pair is annotated by several raters, who were instructed to choose a more visually appealing image. Given the dataset and the labels, we trained a CNN model that obtains a pair of images and for each image predicts a probability of being more preferable than its counterpart. In this work, we show that Neural Side-By-Side generalizes across both new models and new data. Hence, it can serve as a natural approximation of human preferences, which can be used to compare models or tune hyperparameters without raters' assistance. We open-source the dataset and the pretrained model and expect that it will become a handy tool for researchers and practitioners. | https://openaccess.thecvf.com/content/CVPR2021/papers/Khrulkov_Neural_Side-by-Side_Predicting_Human_Preferences_for_No-Reference_Super-Resolution_Evaluation_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Khrulkov_Neural_Side-by-Side_Predicting_Human_Preferences_for_No-Reference_Super-Resolution_Evaluation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Khrulkov_Neural_Side-by-Side_Predicting_Human_Preferences_for_No-Reference_Super-Resolution_Evaluation_CVPR_2021_paper.html | CVPR 2021 | null | null |
Coordinate Attention for Efficient Mobile Network Design | Qibin Hou, Daquan Zhou, Jiashi Feng | Recent studies on mobile network design have demonstrated the remarkable effectiveness of channel attention (e.g., the Squeeze-and-Excitation attention) for lifting model performance, but they generally neglect the positional information, which is important for generating spatially selective attention maps. In this paper, we propose a novel attention mechanism for mobile networks by embedding positional information into channel attention, which we call "coordinate attention". Unlike channel attention that transforms a feature tensor to a single feature vector via 2D global pooling, the coordinate attention factorizes channel attention into two 1D feature encoding processes that aggregate features along the two spatial directions, respectively. In this way, long-range dependencies can be captured along one spatial direction and meanwhile precise positional information can be preserved along the other spatial direction. The resulting feature maps are then encoded separately into a pair of direction-aware and position-sensitive attention maps that can be complementarily applied to the input feature map to augment the representations of the objects of interest. Our coordinate attention is simple and can be flexibly plugged into classic mobile networks, such as MobileNetV2, MobileNeXt, and EfficientNet with nearly no computational overhead. Extensive experiments demonstrate that our coordinate attention is not only beneficial to ImageNet classification but more interestingly, behaves better in down-stream tasks, such as object detection and semantic segmentation. Code is available at https://github.com/Andrew-Qibin/CoordAttention. | https://openaccess.thecvf.com/content/CVPR2021/papers/Hou_Coordinate_Attention_for_Efficient_Mobile_Network_Design_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.02907 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Hou_Coordinate_Attention_for_Efficient_Mobile_Network_Design_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Hou_Coordinate_Attention_for_Efficient_Mobile_Network_Design_CVPR_2021_paper.html | CVPR 2021 | null | null |
Stylized Neural Painting | Zhengxia Zou, Tianyang Shi, Shuang Qiu, Yi Yuan, Zhenwei Shi | This paper proposes an image-to-painting translation method that generates vivid and realistic painting artworks with controllable styles. Different from previous image-to-image translation methods that formulate the translation as pixel-wise prediction, we deal with such an artistic creation process in a vectorized environment and produce a sequence of physically meaningful stroke parameters that can be further used for rendering. Since a typical vector render is not differentiable, we design a novel neural renderer which imitates the behavior of the vector renderer and then frame the stroke prediction as a parameter searching process that maximizes the similarity between the input and the rendering output. We explored the zero-gradient problem on parameter searching and propose to solve this problem from an optimal transportation perspective. We also show that previous neural renderers have a parameter coupling problem and we re-design the rendering network with a rasterization network and a shading network that better handles the disentanglement of shape and color. Experiments show that the paintings generated by our method have a high degree of fidelity in both global appearance and local textures. Our method can be also jointly optimized with neural style transfer that further transfers visual style from other images. Our code and animated results are available at https://jiupinjia.github.io/neuralpainter/. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zou_Stylized_Neural_Painting_CVPR_2021_paper.pdf | http://arxiv.org/abs/2011.08114 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zou_Stylized_Neural_Painting_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zou_Stylized_Neural_Painting_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zou_Stylized_Neural_Painting_CVPR_2021_supplemental.pdf | null |
Image Change Captioning by Learning From an Auxiliary Task | Mehrdad Hosseinzadeh, Yang Wang | We tackle the challenging task of image change captioning. The goal is to describe the subtle difference between two very similar images by generating a sentence caption. While the recent methods mainly focus on proposing new model architectures for this problem, we instead focus on an alternative training scheme. Inspired by the success of multi-task learning, we formulate a training scheme that uses an auxiliary task to improve the training of the change captioning network. We argue that the task of composed query image retrieval is a natural choice as the auxiliary task. Given two almost similar images as the input, the primary network generates a caption describing the fine change between those two images. Next, the auxiliary network is provided with the generated caption and one of those two images. It then tries to pick the second image among a set of candidates. This forces the primary network to generate detailed and precise captions via having an extra supervision loss by the auxiliary network. Furthermore, we propose a new scheme for selecting a negative set of candidates for the retrieval task that can effectively improve the performance. We show that the proposed training strategy performs well on the task of change captioning on benchmark datasets. | https://openaccess.thecvf.com/content/CVPR2021/papers/Hosseinzadeh_Image_Change_Captioning_by_Learning_From_an_Auxiliary_Task_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Hosseinzadeh_Image_Change_Captioning_by_Learning_From_an_Auxiliary_Task_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Hosseinzadeh_Image_Change_Captioning_by_Learning_From_an_Auxiliary_Task_CVPR_2021_paper.html | CVPR 2021 | null | null |
Learning to Generalize Unseen Domains via Memory-based Multi-Source Meta-Learning for Person Re-Identification | Yuyang Zhao, Zhun Zhong, Fengxiang Yang, Zhiming Luo, Yaojin Lin, Shaozi Li, Nicu Sebe | Recent advances in person re-identification (ReID) obtain impressive accuracy in the supervised and unsupervised learning settings. However, most of the existing methods need to train a new model for a new domain by accessing data. Due to public privacy, the new domain data are not always accessible, leading to a limited applicability of these methods. In this paper, we study the problem of multi-source domain generalization in ReID, which aims to learn a model that can perform well on unseen domains with only several labeled source domains. To address this problem, we propose the Memory-based Multi-Source Meta-Learning (M^3L) framework to train a generalizable model for unseen domains. Specifically, a meta-learning strategy is introduced to simulate the train-test process of domain generalization for learning more generalizable models. To overcome the unstable meta-optimization caused by the parametric classifier, we propose a memory-based identification loss that is non-parametric and harmonizes with meta-learning. We also present a meta batch normalization layer (MetaBN) to diversify meta-test features, further establishing the advantage of meta-learning. Experiments demonstrate that our M^3L can effectively enhance the generalization ability of the model for unseen domains and can outperform the state-of-the-art methods on four large-scale ReID datasets. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhao_Learning_to_Generalize_Unseen_Domains_via_Memory-based_Multi-Source_Meta-Learning_for_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.00417 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhao_Learning_to_Generalize_Unseen_Domains_via_Memory-based_Multi-Source_Meta-Learning_for_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhao_Learning_to_Generalize_Unseen_Domains_via_Memory-based_Multi-Source_Meta-Learning_for_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhao_Learning_to_Generalize_CVPR_2021_supplemental.pdf | null |
Discriminative Appearance Modeling With Multi-Track Pooling for Real-Time Multi-Object Tracking | Chanho Kim, Li Fuxin, Mazen Alotaibi, James M. Rehg | In multi-object tracking, the tracker maintains in its memory the appearance and motion information for each object in the scene. This memory is utilized for finding matches between tracks and detections, and is updated based on the matching. Many approaches model each target in isolation and lack the ability to use all the targets in the scene to jointly update the memory. This can be problematic when there are similarly looking objects in the scene. In this paper, we solve the problem of simultaneously considering all tracks during memory updating, with only a small spatial overhead, via a novel multi-track pooling module. We additionally propose a training strategy adapted to multi-track pooling which generates hard tracking episodes online. We show that the combination of these innovations results in a strong discriminative appearance model under the bilinear LSTM tracking framework, enabling the use of greedy data association to achieve online tracking performance. Our experiments demonstrate real-time, state-of-the-art online tracking performance on public multi-object tracking (MOT) datasets. | https://openaccess.thecvf.com/content/CVPR2021/papers/Kim_Discriminative_Appearance_Modeling_With_Multi-Track_Pooling_for_Real-Time_Multi-Object_Tracking_CVPR_2021_paper.pdf | http://arxiv.org/abs/2101.12159 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Kim_Discriminative_Appearance_Modeling_With_Multi-Track_Pooling_for_Real-Time_Multi-Object_Tracking_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Kim_Discriminative_Appearance_Modeling_With_Multi-Track_Pooling_for_Real-Time_Multi-Object_Tracking_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Kim_Discriminative_Appearance_Modeling_CVPR_2021_supplemental.pdf | null |
LASR: Learning Articulated Shape Reconstruction From a Monocular Video | Gengshan Yang, Deqing Sun, Varun Jampani, Daniel Vlasic, Forrester Cole, Huiwen Chang, Deva Ramanan, William T. Freeman, Ce Liu | Remarkable progress has been made in 3D reconstruction of rigid structures from a video or a collection of images. However, it is still challenging to reconstruct nonrigid structures from RGB inputs, due to the under-constrained nature of this problem. While template-based approaches, such as parametric shape models, have achieved great success in terms of modeling the "closed world" of known object categories, their ability to handle the "open-world" of novel object categories and outlier shapes is still limited. In this work, we introduce a template-free approach for 3D shape learning from a single video. It adopts an analysis-by-synthesis strategy that forward-renders object silhouette, optical flow, and pixels intensities to compare against video observations, which generates gradients signals to adjust the camera, shape and motion parameters. Without relying on a category-specific shape template, our method faithfully reconstructs nonrigid 3D structures from videos of human, animals, and objects of unknown classes in the wild. | https://openaccess.thecvf.com/content/CVPR2021/papers/Yang_LASR_Learning_Articulated_Shape_Reconstruction_From_a_Monocular_Video_CVPR_2021_paper.pdf | http://arxiv.org/abs/2105.02976 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Yang_LASR_Learning_Articulated_Shape_Reconstruction_From_a_Monocular_Video_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Yang_LASR_Learning_Articulated_Shape_Reconstruction_From_a_Monocular_Video_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yang_LASR_Learning_Articulated_CVPR_2021_supplemental.pdf | null |
FVC: A New Framework Towards Deep Video Compression in Feature Space | Zhihao Hu, Guo Lu, Dong Xu | Learning based video compression attracts increasing attention in the past few years. The previous hybrid coding approaches rely on pixel space operations to reduce spatial and temporal redundancy, which may suffer from inaccurate motion estimation or less effective motion compensation. In this work, we propose a feature-space video coding network (FVC) by performing all major operations (i.e., motion estimation, motion compression, motion compensation and residual compression) in the feature space. Specifically, in the proposed deformable compensation module, we first apply motion estimation in the feature space to produce motion information (i.e., the offset maps), which will be compressed by using the auto-encoder style network. Then we perform motion compensation by using deformable convolution and generate the predicted feature. After that, we compress the residual feature between the feature from the current frame and the predicted feature from our deformable compensation module. For better frame reconstruction, the reference features from multiple previous reconstructed frames are also fused by using the non-local attention mechanism in the multi-frame feature fusion module. Comprehensive experimental results demonstrate that the proposed framework achieves the state-of-the-art performance on four benchmark datasets including HEVC, UVG, VTL and MCL-JCV. | https://openaccess.thecvf.com/content/CVPR2021/papers/Hu_FVC_A_New_Framework_Towards_Deep_Video_Compression_in_Feature_CVPR_2021_paper.pdf | http://arxiv.org/abs/2105.09600 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Hu_FVC_A_New_Framework_Towards_Deep_Video_Compression_in_Feature_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Hu_FVC_A_New_Framework_Towards_Deep_Video_Compression_in_Feature_CVPR_2021_paper.html | CVPR 2021 | null | null |
Exponential Moving Average Normalization for Self-Supervised and Semi-Supervised Learning | Zhaowei Cai, Avinash Ravichandran, Subhransu Maji, Charless Fowlkes, Zhuowen Tu, Stefano Soatto | We present a plug-in replacement for batch normalization (BN) called exponential moving average normalization (EMAN), which improves the performance of existing student-teacher based self- and semi-supervised learning techniques. Unlike the standard BN, where the statistics are computed within each batch, EMAN, used in the teacher, updates its statistics by exponential moving average from the BN statistics of the student. This design reduces the intrinsic cross-sample dependency of BN and enhances the generalization of the teacher. EMAN improves strong baselines for self-supervised learning by 4-6/1-2 points and semi-supervised learning by about 7/2 points, when 1%/10% supervised labels are available on ImageNet. These improvements are consistent across methods, network architectures, training duration, and datasets, demonstrating the general effectiveness of this technique. The code will be made available online. | https://openaccess.thecvf.com/content/CVPR2021/papers/Cai_Exponential_Moving_Average_Normalization_for_Self-Supervised_and_Semi-Supervised_Learning_CVPR_2021_paper.pdf | http://arxiv.org/abs/2101.08482 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Cai_Exponential_Moving_Average_Normalization_for_Self-Supervised_and_Semi-Supervised_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Cai_Exponential_Moving_Average_Normalization_for_Self-Supervised_and_Semi-Supervised_Learning_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Cai_Exponential_Moving_Average_CVPR_2021_supplemental.pdf | null |
Confluent Vessel Trees With Accurate Bifurcations | Zhongwen Zhang, Dmitrii Marin, Maria Drangova, Yuri Boykov | We are interested in unsupervised reconstruction of complex near-capillary vasculature with thousands of bifurcations where supervision and learning are infeasible. Unsupervised methods can use many structural constraints, e.g. topology, geometry, physics. Common techniques use variants of MST on geodesic "tubular graphs" minimizing symmetric pairwise costs, i.e. distances. We show limitations of such standard undirected tubular graphs producing typical errors at bifurcations where flow "directedness" is critical. We introduce a new general concept of "confluence" for continuous oriented curves forming vessel trees and show how to enforce it on discrete tubular graphs. While confluence is a high-order property, we present an efficient practical algorithm for reconstructing confluent vessel trees using minimum arborescence on a directed graph enforcing confluence via simple flow-extrapolating arc construction. Empirical tests on large near-capillary sub-voxel vasculature volumes demonstrate significantly improved reconstruction accuracy at bifurcations. Our code has also been made publicly available. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_Confluent_Vessel_Trees_With_Accurate_Bifurcations_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.14268 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Confluent_Vessel_Trees_With_Accurate_Bifurcations_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Confluent_Vessel_Trees_With_Accurate_Bifurcations_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhang_Confluent_Vessel_Trees_CVPR_2021_supplemental.pdf | null |
Intentonomy: A Dataset and Study Towards Human Intent Understanding | Menglin Jia, Zuxuan Wu, Austin Reiter, Claire Cardie, Serge Belongie, Ser-Nam Lim | An image is worth a thousand words, conveying information that goes beyond the physical visual content therein. In this paper, we study the intent behind social media images with an aim to analyze how visual information can help the recognition of human intent. Towards this goal, we introduce an intent dataset, Intentonomy, comprising 14K images covering a wide range of everyday scenes. These images are manually annotated with 28 intent categories that are derived from a social psychology taxonomy. We then systematically study whether, and to what extent, commonly used visual information, i.e., object and context, contribute to human motive understanding. Based on our findings, we conduct further study to quantify the effect of attending to object and context classes as well as textual information in the form of hashtags when training an intent classifier. Our results quantitatively and qualitatively shed light on how visual and textual information can produce observable effects when predicting intent. | https://openaccess.thecvf.com/content/CVPR2021/papers/Jia_Intentonomy_A_Dataset_and_Study_Towards_Human_Intent_Understanding_CVPR_2021_paper.pdf | http://arxiv.org/abs/2011.05558 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Jia_Intentonomy_A_Dataset_and_Study_Towards_Human_Intent_Understanding_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Jia_Intentonomy_A_Dataset_and_Study_Towards_Human_Intent_Understanding_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Jia_Intentonomy_A_Dataset_CVPR_2021_supplemental.pdf | null |
End-to-End Rotation Averaging With Multi-Source Propagation | Luwei Yang, Heng Li, Jamal Ahmed Rahim, Zhaopeng Cui, Ping Tan | This paper presents an end-to-end neural network for multiple rotation averaging in SfM. Due to the manifold constraint of rotations, conventional methods usually take two separate steps involving spanning tree based initialization and iterative nonlinear optimization respectively. These methods can suffer from bad initializations due to the noisy spanning tree or outliers in input relative rotations. To handle these problems, we propose to integrate initialization and optimization together in an unified graph neural network via a novel differentiable multi-source propagation module. Specifically, our network utilizes image context and geometric cues in feature correspondences to reduce the impact of outliers. Furthermore, unlike the methods that utilize the spanning tree to initialize orientations according to a single reference node in a top-down manner, our network initializes orientations according to multiple sources while utilizing information from all neighbors in a differentiable way.More importantly, our end-to-end formulation also enables iterative re-weighting of input relative orientations at test time to improve the accuracy of the final estimation by minimizing the impact of outliers. We demonstrate the effectiveness of our method on two real-world datasets, achieving state-of-the-art performance. | https://openaccess.thecvf.com/content/CVPR2021/papers/Yang_End-to-End_Rotation_Averaging_With_Multi-Source_Propagation_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Yang_End-to-End_Rotation_Averaging_With_Multi-Source_Propagation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Yang_End-to-End_Rotation_Averaging_With_Multi-Source_Propagation_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yang_End-to-End_Rotation_Averaging_CVPR_2021_supplemental.pdf | null |
Controllable Image Restoration for Under-Display Camera in Smartphones | Kinam Kwon, Eunhee Kang, Sangwon Lee, Su-Jin Lee, Hyong-Euk Lee, ByungIn Yoo, Jae-Joon Han | Under-display camera (UDC) technology is essential for full-screen display in smartphones and is achieved by removing the concept of drilling holes on display. However, this causes inevitable image degradation in the form of spatially variant blur and noise because of the opaque display in front of the camera. To address spatially variant blur and noise in UDC images, we propose a novel controllable image restoration algorithm utilizing pixel-wise UDC-specific kernel representation and a noise estimator. The kernel representation is derived from an elaborate optical model that reflects the effect of both normal and oblique light incidence. Also, noise-adaptive learning is introduced to control noise levels, which can be utilized to provide optimal results depending on the user preferences. The experiments showed that the proposed method achieved superior quantitative performance as well as higher perceptual quality on both a real-world dataset and a monitor-based aligned dataset compared to conventional image restoration algorithms. | https://openaccess.thecvf.com/content/CVPR2021/papers/Kwon_Controllable_Image_Restoration_for_Under-Display_Camera_in_Smartphones_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Kwon_Controllable_Image_Restoration_for_Under-Display_Camera_in_Smartphones_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Kwon_Controllable_Image_Restoration_for_Under-Display_Camera_in_Smartphones_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Kwon_Controllable_Image_Restoration_CVPR_2021_supplemental.pdf | null |
Farewell to Mutual Information: Variational Distillation for Cross-Modal Person Re-Identification | Xudong Tian, Zhizhong Zhang, Shaohui Lin, Yanyun Qu, Yuan Xie, Lizhuang Ma | The Information Bottleneck (IB) provides an information theoretic principle for representation learning, by retaining all information relevant for predicting label while minimizing the redundancy. Though IB principle has been applied to a wide range of applications, its optimization remains a challenging problem which heavily relies on the accurate estimation of mutual information. In this paper, we present a new strategy, Variational Self-Distillation (VSD), which provides a scalable, flexible and analytic solution to essentially fitting the mutual information but without explicitly estimating it. Under rigorously theoretical guarantee, VSD enables the IB to grasp the intrinsic correlation between representation and label for supervised training. Furthermore, by extending VSD to multi-view learning, we introduce two other strategies, Variational Cross-Distillation (VCD) and Variational Mutual Learning (VML), which significantly improve the robustness of representation to view-changes by eliminating view-specific and task-irrelevant information. To verify our theoretically grounded strategies, we apply our approaches to cross-modal person Re-ID, and conduct extensive experiments, where the superior performance against state-of-the-art methods are demonstrated. Our intriguing findings highlight the need to rethink the way to estimate mutual information. | https://openaccess.thecvf.com/content/CVPR2021/papers/Tian_Farewell_to_Mutual_Information_Variational_Distillation_for_Cross-Modal_Person_Re-Identification_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.02862 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Tian_Farewell_to_Mutual_Information_Variational_Distillation_for_Cross-Modal_Person_Re-Identification_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Tian_Farewell_to_Mutual_Information_Variational_Distillation_for_Cross-Modal_Person_Re-Identification_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Tian_Farewell_to_Mutual_CVPR_2021_supplemental.pdf | null |
Context-Aware Biaffine Localizing Network for Temporal Sentence Grounding | Daizong Liu, Xiaoye Qu, Jianfeng Dong, Pan Zhou, Yu Cheng, Wei Wei, Zichuan Xu, Yulai Xie | This paper addresses the problem of temporal sentence grounding (TSG), which aims to identify the temporal boundary of a specific segment from an untrimmed video by a sentence query. Previous works either compare pre-defined candidate segments with the query and select the best one by ranking, or directly regress the boundary timestamps of the target segment. In this paper, we propose a novel localization framework that scores all pairs of start and end indices within the video simultaneously with a biaffine mechanism. In particular, we present a Context-aware Biaffine Localizing Network (CBLN) which incorporates both local and global contexts into features of each start/end position for biaffine-based localization. The local contexts from the adjacent frames help distinguish the visually similar appearance, and the global contexts from the entire video contribute to reasoning the temporal relation. Besides, we also develop a multi-modal self-attention module to provide fine-grained query-guided video representation for this biaffine strategy. Extensive experiments show that our CBLN significantly outperforms state-of-the-arts on three public datasets (ActivityNet Captions, TACoS, and Charades-STA), demonstrating the effectiveness of the proposed localization framework. | https://openaccess.thecvf.com/content/CVPR2021/papers/Liu_Context-Aware_Biaffine_Localizing_Network_for_Temporal_Sentence_Grounding_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.11555 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_Context-Aware_Biaffine_Localizing_Network_for_Temporal_Sentence_Grounding_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_Context-Aware_Biaffine_Localizing_Network_for_Temporal_Sentence_Grounding_CVPR_2021_paper.html | CVPR 2021 | null | null |
NewtonianVAE: Proportional Control and Goal Identification From Pixels via Physical Latent Spaces | Miguel Jaques, Michael Burke, Timothy M. Hospedales | Learning low-dimensional latent state space dynamics models has proven powerful for enabling vision-based planning and learning for control. We introduce a latent dynamics learning framework that is uniquely designed to induce proportional controlability in the latent space, thus enabling the use of simple and well-known PID controllers. We show that our learned dynamics model enables proportional control from pixels, dramatically simplifies and accelerates behavioural cloning of vision-based controllers, and provides interpretable goal discovery when applied to imitation learning of switching controllers from demonstration. Notably, such proportional controlability also allows for robust path following from visual demonstrations using Dynamic Movement Primitives in the learned latent space. | https://openaccess.thecvf.com/content/CVPR2021/papers/Jaques_NewtonianVAE_Proportional_Control_and_Goal_Identification_From_Pixels_via_Physical_CVPR_2021_paper.pdf | http://arxiv.org/abs/2006.01959 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Jaques_NewtonianVAE_Proportional_Control_and_Goal_Identification_From_Pixels_via_Physical_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Jaques_NewtonianVAE_Proportional_Control_and_Goal_Identification_From_Pixels_via_Physical_CVPR_2021_paper.html | CVPR 2021 | null | null |
Auto-Exposure Fusion for Single-Image Shadow Removal | Lan Fu, Changqing Zhou, Qing Guo, Felix Juefei-Xu, Hongkai Yu, Wei Feng, Yang Liu, Song Wang | Shadow removal is still a challenging task due to its inherent background-dependent and spatial-variant properties, leading to unknown and diverse shadow patterns. Even powerful deep neural networks could hardly recover traceless shadow-removed background. This paper proposes a new solution for this task by formulating it as an exposure fusion problem to address the challenges. Intuitively, we first estimate multiple over-exposure images w.r.t. the input image to let the shadow regions in these images have the same color with shadow-free areas in the input image. Then, we fuse the original input with the over-exposure images to generate the final shadow-free counterpart. Nevertheless, the spatial-variant property of the shadow requires the fusion to be sufficiently `smart', that is, it should automatically select proper over-exposure pixels from different images to make the final output natural. To address this challenge, we propose the shadow-aware FusionNet that takes the shadow image as input to generate fusion weight maps across all the over-exposure images. Moreover, we propose the boundary-aware RefineNet to eliminate the remaining shadow trace further. We conduct extensive experiments on the ISTD, ISTD+, and SRD datasets to validate our method's effectiveness and show better performance in shadow regions and comparable performance in non-shadow regions over the state-of-the-art methods. We release the code in https://github.com/tsingqguo/exposure-fusion-shadow-removal. | https://openaccess.thecvf.com/content/CVPR2021/papers/Fu_Auto-Exposure_Fusion_for_Single-Image_Shadow_Removal_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.01255 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Fu_Auto-Exposure_Fusion_for_Single-Image_Shadow_Removal_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Fu_Auto-Exposure_Fusion_for_Single-Image_Shadow_Removal_CVPR_2021_paper.html | CVPR 2021 | null | null |
Anticipating Human Actions by Correlating Past With the Future With Jaccard Similarity Measures | Basura Fernando, Samitha Herath | We propose a framework for early action recognition and anticipation by correlating past features with the future using three novel similarity measures called Jaccard vector similarity, Jaccard cross-correlation and Jaccard Frobenius inner product over covariances. Using these combinations of novel losses and using our framework, we obtain state-of-the-art results for early action recognition in UCF101 and JHMDB datasets by obtaining 91.7 % and 83.5 % accuracy respectively for an observation percentage of 20. Similarly, we obtain state-of-the-art results for Epic-Kitchen55 and Breakfast datasets for action anticipation by obtaining 20.35 and 41.8 top-1 accuracy respectively. | https://openaccess.thecvf.com/content/CVPR2021/papers/Fernando_Anticipating_Human_Actions_by_Correlating_Past_With_the_Future_With_CVPR_2021_paper.pdf | http://arxiv.org/abs/2105.12414 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Fernando_Anticipating_Human_Actions_by_Correlating_Past_With_the_Future_With_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Fernando_Anticipating_Human_Actions_by_Correlating_Past_With_the_Future_With_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Fernando_Anticipating_Human_Actions_CVPR_2021_supplemental.pdf | null |
LipSync3D: Data-Efficient Learning of Personalized 3D Talking Faces From Video Using Pose and Lighting Normalization | Avisek Lahiri, Vivek Kwatra, Christian Frueh, John Lewis, Chris Bregler | In this paper, we present a video-based learning framework for animating personalized 3D talking faces from audio. We introduce two training-time data normalizations that significantly improve data sample efficiency. First, we isolate and represent faces in a normalized space that decouples 3D geometry, head pose, and texture. This decomposes the prediction problem into regressions over the 3D face shape and the corresponding 2D texture atlas. Second, we leverage facial symmetry and approximate albedo constancy of skin to isolate and remove spatiotemporal lighting variations. Together, these normalizations allow simple networks to generate high fidelity lip-sync videos under novel ambient illumination while training with just a single video (of usually < 5 minutes). Further, to stabilize temporal dynamics, we introduce an auto-regressive approach that conditions the model on its previous visual state. Human ratings and objective metrics demonstrate that our method outperforms contemporary state-of-the-art audio-driven video reenactment benchmarks in terms of realism, lip-sync and visual quality scores. We illustrate several applications enabled by our framework. | https://openaccess.thecvf.com/content/CVPR2021/papers/Lahiri_LipSync3D_Data-Efficient_Learning_of_Personalized_3D_Talking_Faces_From_Video_CVPR_2021_paper.pdf | http://arxiv.org/abs/2106.04185 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Lahiri_LipSync3D_Data-Efficient_Learning_of_Personalized_3D_Talking_Faces_From_Video_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Lahiri_LipSync3D_Data-Efficient_Learning_of_Personalized_3D_Talking_Faces_From_Video_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Lahiri_LipSync3D_Data-Efficient_Learning_CVPR_2021_supplemental.pdf | null |
Simpler Certified Radius Maximization by Propagating Covariances | Xingjian Zhen, Rudrasis Chakraborty, Vikas Singh | One strategy for adversarially training a robust model is to maximize its certified radius -- the neighborhood around a given training sample for which the model's prediction remains unchanged. The scheme typically involves analyzing a "smoothed" classifier where one estimates the prediction corresponding to Gaussian samples in the neighborhood of each sample in the mini-batch, accomplished in practice by Monte Carlo sampling. In this paper, we investigate the hypothesis that this sampling bottleneck can potentially be mitigated by identifying ways to directly propagate the covariance matrix of the smoothed distribution through the network. To this end, we find that other than certain adjustments to the network, propagating the covariances must also be accompanied by additional accounting that keeps track of how the distributional moments transform and interact at each stage in the network. We show how satisfying these criteria yields an algorithm for maximizing the certified radius on datasets including Cifar-10, ImageNet, and Places365 while offering runtime savings on networks with moderate depth, with a small compromise in overall accuracy. We describe the details of the key modifications that enable practical use. Via various experiments, we evaluate when our simplifications are sensible, and what the key benefits and limitations are. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhen_Simpler_Certified_Radius_Maximization_by_Propagating_Covariances_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.05888 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhen_Simpler_Certified_Radius_Maximization_by_Propagating_Covariances_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhen_Simpler_Certified_Radius_Maximization_by_Propagating_Covariances_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhen_Simpler_Certified_Radius_CVPR_2021_supplemental.pdf | null |
A 3D GAN for Improved Large-Pose Facial Recognition | Richard T. Marriott, Sami Romdhani, Liming Chen | Facial recognition using deep convolutional neural networks relies on the availability of large datasets of face images. Many examples of identities are needed, and for each identity, a large variety of images are needed in order for the network to learn robustness to intra-class variation. In practice, such datasets are difficult to obtain, particularly those containing adequate variation of pose. Generative Adversarial Networks (GANs) provide a potential solution to this problem due to their ability to generate realistic, synthetic images. However, recent studies have shown that current methods of disentangling pose from identity are inadequate. In this work we incorporate a 3D morphable model into the generator of a GAN in order to learn a nonlinear texture model from in-the-wild images. This allows generation of new, synthetic identities, and manipulation of pose, illumination and expression without compromising the identity. Our synthesised data is used to augment training of facial recognition networks with performance evaluated on the challenging CFP and CPLFW datasets. | https://openaccess.thecvf.com/content/CVPR2021/papers/Marriott_A_3D_GAN_for_Improved_Large-Pose_Facial_Recognition_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.10545 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Marriott_A_3D_GAN_for_Improved_Large-Pose_Facial_Recognition_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Marriott_A_3D_GAN_for_Improved_Large-Pose_Facial_Recognition_CVPR_2021_paper.html | CVPR 2021 | null | null |
Repopulating Street Scenes | Yifan Wang, Andrew Liu, Richard Tucker, Jiajun Wu, Brian L. Curless, Steven M. Seitz, Noah Snavely | We present a framework for automatically reconfiguring images of street scenes by populating, depopulating, or repopulating them with objects such as pedestrians or vehicles. Applications of this method include anonymizing images to enhance privacy, generating data augmentations for perception tasks like autonomous driving, and composing scenes to achieve a certain ambiance, such as empty streets in the early morning. At a technical level, our work has three primary contributions: (1) a method for clearing images of objects,(2) a method for estimating sun direction from a single image, and (3) a way to compose objects in scenes that respects scene geometry and illumination. Each component is learned from data with minimal ground truth annotations, by making creative use of large-numbers of short image bursts of street scenes. We demonstrate convincing results on a range of street scenes and illustrate potential applications. | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_Repopulating_Street_Scenes_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.16183 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Repopulating_Street_Scenes_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Repopulating_Street_Scenes_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wang_Repopulating_Street_Scenes_CVPR_2021_supplemental.pdf | null |
ARVo: Learning All-Range Volumetric Correspondence for Video Deblurring | Dongxu Li, Chenchen Xu, Kaihao Zhang, Xin Yu, Yiran Zhong, Wenqi Ren, Hanna Suominen, Hongdong Li | Video deblurring models exploit consecutive frames to remove blurs from camera shakes and object motions. In order to utilize neighboring sharp patches, typical methods rely mainly on homography or optical flows to spatially align neighboring blurry frames. However, such explicit approaches are less effective in the presence of fast motions with large pixel displacements. In this work, we propose a novel implicit method to learn spatial correspondence among blurry frames in the feature space. To construct distant pixel correspondences, our model builds a correlation volume pyramid among all the pixel-pairs between neighboring frames. To enhance the features of the reference frame, we design a correlative aggregation module that maximizes the pixel-pair correlations with its neighbors based on the volume pyramid. Finally, we feed the aggregated features into a reconstruction module to obtain the restored frame. We design a generative adversarial paradigm to optimize the model progressively. Our proposed method is evaluated on the widely-adopted DVD dataset, along with a newly collected High-Frame-Rate (1000 fps) Dataset for Video Deblurring (HFR-DVD). Quantitative and qualitative experiments show that our model performs favorably on both datasets against previous state-of-the-art methods, confirming the benefit of modeling all-range spatial correspondence for video deblurring. | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_ARVo_Learning_All-Range_Volumetric_Correspondence_for_Video_Deblurring_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.04260 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Li_ARVo_Learning_All-Range_Volumetric_Correspondence_for_Video_Deblurring_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Li_ARVo_Learning_All-Range_Volumetric_Correspondence_for_Video_Deblurring_CVPR_2021_paper.html | CVPR 2021 | null | null |
Unsupervised Object Detection With LIDAR Clues | Hao Tian, Yuntao Chen, Jifeng Dai, Zhaoxiang Zhang, Xizhou Zhu | Despite the importance of unsupervised object detection, to the best of our knowledge, there is no previous work addressing this problem. One main issue, widely known to the community, is that object boundaries derived only from 2D image appearance are ambiguous and unreliable. To address this, we exploit LiDAR clues to aid unsupervised object detection. By exploiting the 3D scene structure, the issue of localization can be considerably mitigated. We further identify another major issue, seldom noticed by the community, that the long-tailed and open-ended (sub-)category distribution should be accommodated. In this paper, we present the first practical method for unsupervised object detection with the aid of LiDAR clues. In our approach, candidate object segments based on 3D point clouds are firstly generated. Then, an iterative segment labeling process is conducted to assign segment labels and to train a segment labeling network, which is based on features from both 2D images and 3D point clouds. The labeling process is carefully designed so as to mitigate the issue of long-tailed and open-ended distribution. The final segment labels are set as pseudo annotations for object detection network training. Extensive experiments on the large-scale Waymo Open dataset suggest that the derived unsupervised object detection method achieves reasonable accuracy compared with that of strong supervision within the LiDAR visible range. | https://openaccess.thecvf.com/content/CVPR2021/papers/Tian_Unsupervised_Object_Detection_With_LIDAR_Clues_CVPR_2021_paper.pdf | http://arxiv.org/abs/2011.12953 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Tian_Unsupervised_Object_Detection_With_LIDAR_Clues_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Tian_Unsupervised_Object_Detection_With_LIDAR_Clues_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Tian_Unsupervised_Object_Detection_CVPR_2021_supplemental.pdf | null |
TesseTrack: End-to-End Learnable Multi-Person Articulated 3D Pose Tracking | N Dinesh Reddy, Laurent Guigues, Leonid Pishchulin, Jayan Eledath, Srinivasa G. Narasimhan | We consider the task of 3D pose estimation and trackingof multiple people seen in an arbitrary number of camerafeeds. We propose TesseTrack, a novel top-down approachthat simultaneously reasons about multiple individuals' 3Dbody joint reconstructions and associations in space andtime in a single end-to-end learnable framework. At the core of our approach is a novel spatio-temporal formulation that operates in a common voxelized feature space aggregated from single- or multiple-camera views. After a person detection step, a 4D CNN produces short-term person-specific representations which are then linked across time by a differentiable matcher. The linked descriptions are then merged and deconvolved into 3D poses. This joint spatio-temporal formulation contrasts with previous piece-wise strategies that treat 2D pose estimation, 2D-to-3D lifting, and 3D pose tracking as independent sub-problems that are error-prone when solved in isolation. Furthermore, unlike previous methods, TesseTrack is robust to changes in the number of camera views and achieves very good results even if a single view is available at inference time. Quantitative evaluation of 3D pose reconstruction accuracy on standard benchmarks shows significant improvements over the state of the art. Evaluation of multi-person articulated 3D pose tracking in our novel evaluation framework demonstrates the superiority of TesseTrack over strong baselines. | https://openaccess.thecvf.com/content/CVPR2021/papers/Reddy_TesseTrack_End-to-End_Learnable_Multi-Person_Articulated_3D_Pose_Tracking_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Reddy_TesseTrack_End-to-End_Learnable_Multi-Person_Articulated_3D_Pose_Tracking_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Reddy_TesseTrack_End-to-End_Learnable_Multi-Person_Articulated_3D_Pose_Tracking_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Reddy_TesseTrack_End-to-End_Learnable_CVPR_2021_supplemental.zip | null |
HVPR: Hybrid Voxel-Point Representation for Single-Stage 3D Object Detection | Jongyoun Noh, Sanghoon Lee, Bumsub Ham | We address the problem of 3D object detection, that is, estimating 3D object bounding boxes from point clouds. 3D object detection methods exploit either voxel-based or point-based features to represent 3D objects in a scene. Voxel-based features are efficient to extract, while they fail to preserve fine-grained 3D structures of objects. Point-based features, on the other hand, represent the 3D structures more accurately, but extracting these features is computationally expensive. We introduce in this paper a novel single-stage 3D detection method having the merit of both voxel-based and point-based features. To this end, we propose a new convolutional neural network (CNN) architecture, dubbed HVPR, that integrates both features into a single 3D representation effectively and efficiently. Specifically, we augment the point-based features with a memory module to reduce the computational cost. We then aggregate the features in the memory, semantically similar to each voxel-based one, to obtain a hybrid 3D representation in a form of a pseudo image, allowing to localize 3D objects in a single stage efficiently. We also propose an Attentive Multi-scale Feature Module (AMFM) that extracts scale-aware features considering the sparse and irregular patterns of point clouds. Experimental results on the KITTI dataset demonstrate the effectiveness and efficiency of our approach, achieving a better compromise in terms of speed and accuracy. | https://openaccess.thecvf.com/content/CVPR2021/papers/Noh_HVPR_Hybrid_Voxel-Point_Representation_for_Single-Stage_3D_Object_Detection_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.00902 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Noh_HVPR_Hybrid_Voxel-Point_Representation_for_Single-Stage_3D_Object_Detection_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Noh_HVPR_Hybrid_Voxel-Point_Representation_for_Single-Stage_3D_Object_Detection_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Noh_HVPR_Hybrid_Voxel-Point_CVPR_2021_supplemental.pdf | null |
SOE-Net: A Self-Attention and Orientation Encoding Network for Point Cloud Based Place Recognition | Yan Xia, Yusheng Xu, Shuang Li, Rui Wang, Juan Du, Daniel Cremers, Uwe Stilla | We tackle the problem of place recognition from point cloud data and introduce a self-attention and orientation encoding network (SOE-Net) that fully explores the relationship between points and incorporates long-range context into point-wise local descriptors. Local information of each point from eight orientations is captured in a PointOE module, whereas long-range feature dependencies among local descriptors are captured with a self-attention unit. Moreover, we propose a novel loss function called Hard Positive Hard Negative quadruplet loss (HPHN quadruplet), that achieves better performance than the commonly used metric learning loss. Experiments on various benchmark datasets demonstrate superior performance of the proposed network over the current state-of-the-art approaches. Our code is released publicly at https://github.com/Yan-Xia/SOE-Net. | https://openaccess.thecvf.com/content/CVPR2021/papers/Xia_SOE-Net_A_Self-Attention_and_Orientation_Encoding_Network_for_Point_Cloud_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Xia_SOE-Net_A_Self-Attention_and_Orientation_Encoding_Network_for_Point_Cloud_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Xia_SOE-Net_A_Self-Attention_and_Orientation_Encoding_Network_for_Point_Cloud_CVPR_2021_paper.html | CVPR 2021 | null | null |
Controlling the Rain: From Removal to Rendering | Siqi Ni, Xueyun Cao, Tao Yue, Xuemei Hu | Existing rain image editing methods focus on either removing rain from rain images or rendering rain on rain-free images. This paper proposes to realize continuous control of rain intensity bidirectionally, from clear rain-free to downpour image with a single rain image as input, without changing the scene-specific characteristics, e.g. the direction, appearance and distribution of rain. Specifically, we introduce a Rain Intensity Controlling Network (RICNet) that contains three sub-networks of background extraction network, high-frequency rain-streak elimination network and main controlling network, which allows to control rain image of different intensities continuously by interpolation in the deep feature space. The HOG loss and autocorrelation loss are proposed to enhance consistency in orientation and suppress repetitive rain streaks. Furthermore, a decremental learning strategy that trains the network from downpour to drizzle images sequentially is proposed to further improve the performance and speedup the convergence. Extensive experiments on both rain dataset and real rain images demonstrate the effectiveness of the proposed method. | https://openaccess.thecvf.com/content/CVPR2021/papers/Ni_Controlling_the_Rain_From_Removal_to_Rendering_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Ni_Controlling_the_Rain_From_Removal_to_Rendering_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Ni_Controlling_the_Rain_From_Removal_to_Rendering_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Ni_Controlling_the_Rain_CVPR_2021_supplemental.pdf | null |
KeypointDeformer: Unsupervised 3D Keypoint Discovery for Shape Control | Tomas Jakab, Richard Tucker, Ameesh Makadia, Jiajun Wu, Noah Snavely, Angjoo Kanazawa | We introduce KeypointDeformer, a novel unsupervised method for shape control through automatically discovered 3D keypoints. We cast this as the problem of aligning a source 3D object to a target 3D object from the same object category. Our method analyzes the difference between the shapes of the two objects by comparing their latent representations. This latent representation is in the form of 3D keypoints that are learned in an unsupervised way. The difference between the 3D keypoints of the source and the target objects then informs the shape deformation algorithm that deforms the source object into the target object. The whole model is learned end-to-end and simultaneously discovers 3D keypoints while learning to use them for deforming object shapes. Our approach produces intuitive and semantically consistent control of shape deformations. Moreover, our discovered 3D keypoints are consistent across object category instances despite large shape variations. As our method is unsupervised, it can be readily deployed to new object categories without requiring annotations for 3D keypoints and deformations. Project page: http://tomasjakab.github.io/KeypointDeformer | https://openaccess.thecvf.com/content/CVPR2021/papers/Jakab_KeypointDeformer_Unsupervised_3D_Keypoint_Discovery_for_Shape_Control_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.11224 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Jakab_KeypointDeformer_Unsupervised_3D_Keypoint_Discovery_for_Shape_Control_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Jakab_KeypointDeformer_Unsupervised_3D_Keypoint_Discovery_for_Shape_Control_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Jakab_KeypointDeformer_Unsupervised_3D_CVPR_2021_supplemental.pdf | null |
A2-FPN: Attention Aggregation Based Feature Pyramid Network for Instance Segmentation | Miao Hu, Yali Li, Lu Fang, Shengjin Wang | Learning pyramidal feature representations is crucial for recognizing object instances at different scales. Feature Pyramid Network (FPN) is the classic architecture to build a feature pyramid with high-level semantics throughout. However, intrinsic defects in feature extraction and fusion inhibit FPN from further aggregating more discriminative features. In this work, we propose Attention Aggregation based Feature Pyramid Network (A^2-FPN), to improve multi-scale feature learning through attention-guided feature aggregation. In feature extraction, it extracts discriminative features by collecting-distributing multi-level global context features, and mitigates the semantic information loss due to drastically reduced channels. In feature fusion, it aggregates complementary information from adjacent features to generate location-wise reassembly kernels for content-aware sampling, and employs channel-wise reweighting to enhance the semantic consistency before element-wise addition. A^2-FPN shows consistent gains on different instance segmentation frameworks. By replacing FPN with A^2-FPN in Mask R-CNN, our model boosts the performance by 2.1% and 1.6% mask AP when using ResNet-50 and ResNet-101 as backbone, respectively. Moreover, A^2-FPN achieves an improvement of 2.0% and 1.4% mask AP when integrated into the strong baselines such as Cascade Mask R-CNN and Hybrid Task Cascade. | https://openaccess.thecvf.com/content/CVPR2021/papers/Hu_A2-FPN_Attention_Aggregation_Based_Feature_Pyramid_Network_for_Instance_Segmentation_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Hu_A2-FPN_Attention_Aggregation_Based_Feature_Pyramid_Network_for_Instance_Segmentation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Hu_A2-FPN_Attention_Aggregation_Based_Feature_Pyramid_Network_for_Instance_Segmentation_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Hu_A2-FPN_Attention_Aggregation_CVPR_2021_supplemental.pdf | null |
Quasi-Dense Similarity Learning for Multiple Object Tracking | Jiangmiao Pang, Linlu Qiu, Xia Li, Haofeng Chen, Qi Li, Trevor Darrell, Fisher Yu | Similarity learning has been recognized as a crucial step for object tracking. However, existing multiple object tracking methods only use sparse ground truth matching as the training objective, while ignoring the majority of the informative regions on the images. In this paper, we present Quasi-Dense Similarity Learning, which densely samples hundreds of region proposals on a pair of images for contrastive learning. We can directly combine this similarity learning with existing detection methods to build Quasi-Dense Tracking (QDTrack) without turning to displacement regression or motion priors. We also find that the resulting distinctive feature space admits a simple nearest neighbor search at the inference time. Despite its simplicity, QDTrack outperforms all existing methods on MOT, BDD100K, Waymo, and TAO tracking benchmarks. It achieves 68.7 MOTA at 20.3 FPS on MOT17 without using external training data. Compared to methods with similar detectors, it boosts almost 10 points of MOTA and significantly decreases the number of ID switches on BDD100K and Waymo datasets. Our code and trained models are available at https://github.com/SysCV/qdtrack. | https://openaccess.thecvf.com/content/CVPR2021/papers/Pang_Quasi-Dense_Similarity_Learning_for_Multiple_Object_Tracking_CVPR_2021_paper.pdf | http://arxiv.org/abs/2006.06664 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Pang_Quasi-Dense_Similarity_Learning_for_Multiple_Object_Tracking_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Pang_Quasi-Dense_Similarity_Learning_for_Multiple_Object_Tracking_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Pang_Quasi-Dense_Similarity_Learning_CVPR_2021_supplemental.pdf | null |
Simultaneously Localize, Segment and Rank the Camouflaged Objects | Yunqiu Lv, Jing Zhang, Yuchao Dai, Aixuan Li, Bowen Liu, Nick Barnes, Deng-Ping Fan | Camouflage is a key defence mechanism across species that is critical to survival. Common camouflage include background matching, imitating the color and pattern of the environment, and disruptive coloration, disguising body outlines. Camouflaged object detection (COD) aims to segment camouflaged objects hiding in their surroundings. Existing COD models are built upon binary ground truth to segment the camouflaged objects without illustrating the level of camouflage. In this paper, we revisit this task and argue that explicitly modeling the conspicuousness of camouflaged objects against their particular backgrounds can not only lead to a better understanding about camouflage and evolution of animals, but also provide guidance to design more sophisticated camouflage techniques. Furthermore, we observe that it is some specific parts of the camouflaged objects that make them detectable by predators. With the above understanding about camouflaged objects, we present the first ranking based COD network to simultaneously localize, segment and rank camouflaged objects. The localization model is proposed to find the discriminative regions that make the camouflaged object obvious. The segmentation model segments the full scope of the camouflaged objects. And, the ranking model infers the detectability of different camouflaged objects. Moreover, we contribute a large COD testing set to evaluate the generalization ability of COD models. Experimental results show that our model achieves new state-of-the-art, leading to a more interpretable COD network. | https://openaccess.thecvf.com/content/CVPR2021/papers/Lv_Simultaneously_Localize_Segment_and_Rank_the_Camouflaged_Objects_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.04011 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Lv_Simultaneously_Localize_Segment_and_Rank_the_Camouflaged_Objects_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Lv_Simultaneously_Localize_Segment_and_Rank_the_Camouflaged_Objects_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Lv_Simultaneously_Localize_Segment_CVPR_2021_supplemental.zip | null |
Hybrid Message Passing With Performance-Driven Structures for Facial Action Unit Detection | Tengfei Song, Zijun Cui, Wenming Zheng, Qiang Ji | Message passing neural network has been an effective method to represent dependencies among nodes by propagating messages. However, most of message passing algorithms focus on one structure and the messages are estimated by one single approach. For the real-world data, like facial action units (AUs), the dependencies may vary in terms of different expressions and individuals. In this paper, we propose a novel hybrid message passing neural network with performance-driven structures (HMP-PS), which combines complementary message passing methods and captures more possible structures in a Bayesian manner. Particularly, a performance-driven Monte Carlo Markov Chain sampling method is proposed for generating high performance graph structures. Besides, the hybrid message passing is proposed to combine different types of messages, which provide the complementary information. The contribution of each type of message is adaptively adjusted along with different inputs. The experiments on two widely used benchmark datasets, i.e., BP4D and DISFA, validate that our proposed method can achieve the state-of-the-art performance. | https://openaccess.thecvf.com/content/CVPR2021/papers/Song_Hybrid_Message_Passing_With_Performance-Driven_Structures_for_Facial_Action_Unit_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Song_Hybrid_Message_Passing_With_Performance-Driven_Structures_for_Facial_Action_Unit_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Song_Hybrid_Message_Passing_With_Performance-Driven_Structures_for_Facial_Action_Unit_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Song_Hybrid_Message_Passing_CVPR_2021_supplemental.pdf | null |
Distilling Object Detectors via Decoupled Features | Jianyuan Guo, Kai Han, Yunhe Wang, Han Wu, Xinghao Chen, Chunjing Xu, Chang Xu | Knowledge distillation is a widely used paradigm for inheriting information from a complicated teacher network to a compact student network and maintaining the strong performance. Different from image classification, object detectors are much more sophisticated with multiple loss functions in which features that semantic information rely on are tangled. In this paper, we point out that the information of features derived from regions excluding objects are also essential for distilling the student detector, which is usually ignored in existing approaches. In addition, we elucidate that features from different regions should be assigned with different importance during distillation. To this end, we present a novel distillation algorithm via decoupled features (DeFeat) for learning a better student detector. Specifically, two levels of decoupled features will be processed for embedding useful information into the student, i.e., decoupled features from neck and decoupled proposals from classification head. Extensive experiments on various detectors with different backbones show that the proposed DeFeat is able to surpass the state-of-the-art distillation methods for object detection. For example, DeFeat improves ResNet50 based Faster R-CNN from 37.4% to 40.9% mAP, and improves ResNet50 based RetinaNet from 36.5% to 39.7% mAP on COCO benchmark. Code will be released. | https://openaccess.thecvf.com/content/CVPR2021/papers/Guo_Distilling_Object_Detectors_via_Decoupled_Features_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.14475 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Guo_Distilling_Object_Detectors_via_Decoupled_Features_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Guo_Distilling_Object_Detectors_via_Decoupled_Features_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Guo_Distilling_Object_Detectors_CVPR_2021_supplemental.pdf | null |
Roof-GAN: Learning To Generate Roof Geometry and Relations for Residential Houses | Yiming Qian, Hao Zhang, Yasutaka Furukawa | This paper presents Roof-GAN, a novel generative adversarial network that generates structured geometry of residential roof structures as a set of roof primitives and their relationships. Given the number of primitives, the generator produces a structured roof model as a graph, which consists of 1) primitive geometry as raster images at each node, encoding facet segmentation and angles; 2) inter-primitive colinear/coplanar relationships at each edge; and 3) primitive geometry in a vector format at each node, generated by a novel differentiable vectorizer while enforcing the relationships. The discriminator is trained to assess the primitive raster geometry, the primitive relationships, and the primitive vector geometry in a fully end-to-end architecture. Qualitative and quantitative evaluations demonstrate the effectiveness of our approach in generating diverse and realistic roof models over the competing methods with a novel metric proposed in this paper for the task of structured geometry generation. Code and data are available at https://github.com/yi-ming-qian/roofgan. | https://openaccess.thecvf.com/content/CVPR2021/papers/Qian_Roof-GAN_Learning_To_Generate_Roof_Geometry_and_Relations_for_Residential_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Qian_Roof-GAN_Learning_To_Generate_Roof_Geometry_and_Relations_for_Residential_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Qian_Roof-GAN_Learning_To_Generate_Roof_Geometry_and_Relations_for_Residential_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Qian_Roof-GAN_Learning_To_CVPR_2021_supplemental.pdf | null |
No Shadow Left Behind: Removing Objects and Their Shadows Using Approximate Lighting and Geometry | Edward Zhang, Ricardo Martin-Brualla, Janne Kontkanen, Brian L. Curless | Removing objects from images is a challenging technical problem that is important for many applications, including mixed reality. For believable results, the shadows that the object casts should also be removed. Current inpainting-based methods only remove the object itself, leaving shadows behind, or at best require specifying shadow regions to inpaint. We introduce a deep learning pipeline for removing a shadow along with its caster. We leverage rough scene models in order to remove a wide variety of shadows (hard or soft, dark or subtle, large or thin) from surfaces with a wide variety of textures. We train our pipeline on synthetically rendered data, and show qualitative and quantitative results on both synthetic and real scenes. | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_No_Shadow_Left_Behind_Removing_Objects_and_Their_Shadows_Using_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_No_Shadow_Left_Behind_Removing_Objects_and_Their_Shadows_Using_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_No_Shadow_Left_Behind_Removing_Objects_and_Their_Shadows_Using_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhang_No_Shadow_Left_CVPR_2021_supplemental.pdf | null |
NetAdaptV2: Efficient Neural Architecture Search With Fast Super-Network Training and Architecture Optimization | Tien-Ju Yang, Yi-Lun Liao, Vivienne Sze | Neural architecture search (NAS) typically consists of three main steps: training a super-network, training and evaluating sampled deep neural networks (DNNs), and training the discovered DNN. Most of the existing efforts speed up some steps at the cost of a significant slowdown of other steps or sacrificing the support of non-differentiable search metrics. The unbalanced reduction in the time spent per step limits the total search time reduction, and the inability to support non-differentiable search metrics limits the performance of discovered DNNs. In this paper, we present NetAdaptV2 with three innovations to better balance the time spent for each step while supporting non-differentiable search metrics. First, we propose channel-level bypass connections that merge network depth and layer width into a single search dimension to reduce the time for training and evaluating sampled DNNs. Second, ordered dropout is proposed to train multiple DNNs in a single forward-backward pass to decrease the time for training a super-network. Third, we propose the multi-layer coordinate descent optimizer that considers the interplay of multiple layers in each iteration of optimization to improve the performance of discovered DNNs while supporting non-differentiable search metrics. With these innovations, NetAdaptV2 reduces the total search time by up to 5.8x on ImageNet and 2.4x on NYU Depth V2, respectively, and discovers DNNs with better accuracy-latency/accuracy-MAC trade-offs than state-of-the-art NAS works. Moreover, the discovered DNN outperforms NAS-discovered MobileNetV3 by 1.8% higher top-1 accuracy with the same latency. | https://openaccess.thecvf.com/content/CVPR2021/papers/Yang_NetAdaptV2_Efficient_Neural_Architecture_Search_With_Fast_Super-Network_Training_and_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.00031 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Yang_NetAdaptV2_Efficient_Neural_Architecture_Search_With_Fast_Super-Network_Training_and_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Yang_NetAdaptV2_Efficient_Neural_Architecture_Search_With_Fast_Super-Network_Training_and_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yang_NetAdaptV2_Efficient_Neural_CVPR_2021_supplemental.pdf | null |
Subsets and Splits