2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)最新文献_第3页

Imitative Non-Autoregressive Modeling for Trajectory Forecasting and Imputation 基于非自回归模型的轨迹预测与估算

2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2020-06-01 DOI: 10.1109/CVPR42600.2020.01275

Mengshi Qi, Jie Qin, Yu Wu, Yi Yang

{"title":"Imitative Non-Autoregressive Modeling for Trajectory Forecasting and Imputation","authors":"Mengshi Qi, Jie Qin, Yu Wu, Yi Yang","doi":"10.1109/CVPR42600.2020.01275","DOIUrl":"https://doi.org/10.1109/CVPR42600.2020.01275","url":null,"abstract":"Trajectory forecasting and imputation are pivotal steps towards understanding the movement of human and objects, which are quite challenging since the future trajectories and missing values in a temporal sequence are full of uncertainties, and the spatial-temporally contextual correlation is hard to model. Yet, the relevance between sequence prediction and imputation is disregarded by existing approaches. To this end, we propose a novel imitative non-autoregressive modeling method to simultaneously handle the trajectory prediction task and the missing value imputation task. Specifically, our framework adopts an imitation learning paradigm, which contains a recurrent conditional variational autoencoder (RC-VAE) as a demonstrator, and a non-autoregressive transformation model (NART) as a learner. By jointly optimizing the two models, RC-VAE can predict the future trajectory and capture the temporal relationship in the sequence to supervise the NART learner. As a result, NART learns from the demonstrator and imputes the missing value in a non autoregressive strategy. We conduct extensive experiments on three popular datasets, and the results show that our model achieves state-of-the-art performance across all the datasets.","PeriodicalId":6715,"journal":{"name":"2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","volume":"48 1","pages":"12733-12742"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82720180","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 28

WCP: Worst-Case Perturbations for Semi-Supervised Deep Learning 半监督深度学习的最坏情况摄动

2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2020-06-01 DOI: 10.1109/CVPR42600.2020.00397

Liheng Zhang, Guo-Jun Qi

引用次数: 33

Deep Kinematics Analysis for Monocular 3D Human Pose Estimation 单目三维人体姿态估计的深度运动学分析

2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2020-06-01 DOI: 10.1109/cvpr42600.2020.00098

Jingwei Xu, Zhenbo Yu, Bingbing Ni, Jiancheng Yang, Xiaokang Yang, Wenjun Zhang

{"title":"Deep Kinematics Analysis for Monocular 3D Human Pose Estimation","authors":"Jingwei Xu, Zhenbo Yu, Bingbing Ni, Jiancheng Yang, Xiaokang Yang, Wenjun Zhang","doi":"10.1109/cvpr42600.2020.00098","DOIUrl":"https://doi.org/10.1109/cvpr42600.2020.00098","url":null,"abstract":"For monocular 3D pose estimation conditioned on 2D detection, noisy/unreliable input is a key obstacle in this task. Simple structure constraints attempting to tackle this problem, e.g., symmetry loss and joint angle limit, could only provide marginal improvements and are commonly treated as auxiliary losses in previous researches. Thus it still remains challenging about how to effectively utilize the power of human prior knowledge for this task. In this paper, we propose to address above issue in a systematic view. Firstly, we show that optimizing the kinematics structure of noisy 2D inputs is critical to obtain accurate 3D estimations. Secondly, based on corrected 2D joints, we further explicitly decompose articulated motion with human topology, which leads to more compact 3D static structure easier for estimation. Finally, temporal refinement emphasizing the validity of 3D dynamic structure is naturally developed to pursue more accurate result. Above three steps are seamlessly integrated into deep neural models, which form a deep kinematics analysis pipeline concurrently considering the static/dynamic structure of 2D inputs and 3D outputs. Extensive experiments show that proposed framework achieves state-of-the-art performance on two widely used 3D human action datasets. Meanwhile, targeted ablation study shows that each former step is critical for the latter one to obtain promising results.","PeriodicalId":6715,"journal":{"name":"2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","volume":"15 1","pages":"896-905"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89031789","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 120

Structure Boundary Preserving Segmentation for Medical Image With Ambiguous Boundary 边界模糊医学图像的结构边界保持分割

2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2020-06-01 DOI: 10.1109/CVPR42600.2020.00487

Hong Joo Lee, Jung Uk Kim, Sangmin Lee, Hak Gu Kim, Yong Man Ro

{"title":"Structure Boundary Preserving Segmentation for Medical Image With Ambiguous Boundary","authors":"Hong Joo Lee, Jung Uk Kim, Sangmin Lee, Hak Gu Kim, Yong Man Ro","doi":"10.1109/CVPR42600.2020.00487","DOIUrl":"https://doi.org/10.1109/CVPR42600.2020.00487","url":null,"abstract":"In this paper, we propose a novel image segmentation method to tackle two critical problems of medical image, which are (i) ambiguity of structure boundary in the medical image domain and (ii) uncertainty of the segmented region without specialized domain knowledge. To solve those two problems in automatic medical segmentation, we propose a novel structure boundary preserving segmentation framework. To this end, the boundary key point selection algorithm is proposed. In the proposed algorithm, the key points on the structural boundary of the target object are estimated. Then, a boundary preserving block (BPB) with the boundary key point map is applied for predicting the structure boundary of the target object. Further, for embedding experts’ knowledge in the fully automatic segmentation, we propose a novel shape boundary-aware evaluator (SBE) with the ground-truth structure information indicated by experts. The proposed SBE could give feedback to the segmentation network based on the structure boundary key point. The proposed method is general and flexible enough to be built on top of any deep learning-based segmentation network. We demonstrate that the proposed method could surpass the state-of-the-art segmentation network and improve the accuracy of three different segmentation network models on different types of medical image datasets.","PeriodicalId":6715,"journal":{"name":"2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","volume":"157 5 1","pages":"4816-4825"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89123192","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 65

A Shared Multi-Attention Framework for Multi-Label Zero-Shot Learning 多标签零学习的共享多注意框架

2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2020-06-01 DOI: 10.1109/cvpr42600.2020.00880

Dat T. Huynh, Ehsan Elhamifar

{"title":"A Shared Multi-Attention Framework for Multi-Label Zero-Shot Learning","authors":"Dat T. Huynh, Ehsan Elhamifar","doi":"10.1109/cvpr42600.2020.00880","DOIUrl":"https://doi.org/10.1109/cvpr42600.2020.00880","url":null,"abstract":"In this work, we develop a shared multi-attention model for multi-label zero-shot learning. We argue that designing attention mechanism for recognizing multiple seen and unseen labels in an image is a non-trivial task as there is no training signal to localize unseen labels and an image only contains a few present labels that need attentions out of thousands of possible labels. Therefore, instead of generating attentions for unseen labels which have unknown behaviors and could focus on irrelevant regions due to the lack of any training sample, we let the unseen labels select among a set of shared attentions which are trained to be label-agnostic and to focus on only relevant/foreground regions through our novel loss. Finally, we learn a compatibility function to distinguish labels based on the selected attention. We further propose a novel loss function that consists of three components guiding the attention to focus on diverse and relevant image regions while utilizing all attention features. By extensive experiments, we show that our method improves the state of the art by 2.9% and 1.4% F1 score on the NUS-WIDE and the large scale Open Images datasets, respectively.","PeriodicalId":6715,"journal":{"name":"2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","volume":"1 1","pages":"8773-8783"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90211722","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 64

A Multi-Task Mean Teacher for Semi-Supervised Shadow Detection 半监督阴影检测的多任务均值教师

2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2020-06-01 DOI: 10.1109/CVPR42600.2020.00565

Zhihao Chen, Lei Zhu, Liang Wan, Song Wang, Wei Feng, P. Heng

{"title":"A Multi-Task Mean Teacher for Semi-Supervised Shadow Detection","authors":"Zhihao Chen, Lei Zhu, Liang Wan, Song Wang, Wei Feng, P. Heng","doi":"10.1109/CVPR42600.2020.00565","DOIUrl":"https://doi.org/10.1109/CVPR42600.2020.00565","url":null,"abstract":"Existing shadow detection methods suffer from an intrinsic limitation in relying on limited labeled datasets, and they may produce poor results in some complicated situations. To boost the shadow detection performance, this paper presents a multi-task mean teacher model for semi-supervised shadow detection by leveraging unlabeled data and exploring the learning of multiple information of shadows simultaneously. To be specific, we first build a multi-task baseline model to simultaneously detect shadow regions, shadow edges, and shadow count by leveraging their complementary information and assign this baseline model to the student and teacher network. After that, we encourage the predictions of the three tasks from the student and teacher networks to be consistent for computing a consistency loss on unlabeled data, which is then added to the supervised loss on the labeled data from the predictions of the multi-task baseline model. Experimental results on three widely-used benchmark datasets show that our method consistently outperforms all the compared state-of- the-art methods, which verifies that the proposed network can effectively leverage additional unlabeled data to boost the shadow detection performance.","PeriodicalId":6715,"journal":{"name":"2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","volume":"70 1","pages":"5610-5619"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83793347","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 83

Attack to Explain Deep Representation 攻击解释深度表示

2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2020-06-01 DOI: 10.1109/cvpr42600.2020.00956

M. Jalwana, Naveed Akhtar, Bennamoun, A. Mian

{"title":"Attack to Explain Deep Representation","authors":"M. Jalwana, Naveed Akhtar, Bennamoun, A. Mian","doi":"10.1109/cvpr42600.2020.00956","DOIUrl":"https://doi.org/10.1109/cvpr42600.2020.00956","url":null,"abstract":"Deep visual models are susceptible to extremely low magnitude perturbations to input images. Though carefully crafted, the perturbation patterns generally appear noisy, yet they are able to perform controlled manipulation of model predictions. This observation is used to argue that deep representation is misaligned with human perception. This paper counter-argues and proposes the first attack on deep learning that aims at explaining the learned representation instead of fooling it. By extending the input domain of the manipulative signal and employing a model faithful channelling, we iteratively accumulate adversarial perturbations for a deep model. The accumulated signal gradually manifests itself as a collection of visually salient features of the target label (in model fooling), casting adversarial perturbations as primitive features of the target label. Our attack provides the first demonstration of systematically computing perturbations for adversarially non-robust classifiers that comprise salient visual features of objects. We leverage the model explaining character of our algorithm to perform image generation, inpainting and interactive image manipulation by attacking adversarially robust classifiers. The visually appealing results across these applications demonstrate the utility of our attack (and perturbations in general) beyond model fooling.","PeriodicalId":6715,"journal":{"name":"2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","volume":"22 1","pages":"9540-9549"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83480243","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 12

ActBERT: Learning Global-Local Video-Text Representations 学习全局-局部视频-文本表示

2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2020-06-01 DOI: 10.1109/cvpr42600.2020.00877

Linchao Zhu, Yi Yang

引用次数: 337

Learning Integral Objects With Intra-Class Discriminator for Weakly-Supervised Semantic Segmentation 基于类内判别器的弱监督语义分割学习积分对象

2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2020-06-01 DOI: 10.1109/cvpr42600.2020.00434

Junsong Fan, Zhaoxiang Zhang, Chunfeng Song, T. Tan

{"title":"Learning Integral Objects With Intra-Class Discriminator for Weakly-Supervised Semantic Segmentation","authors":"Junsong Fan, Zhaoxiang Zhang, Chunfeng Song, T. Tan","doi":"10.1109/cvpr42600.2020.00434","DOIUrl":"https://doi.org/10.1109/cvpr42600.2020.00434","url":null,"abstract":"Image-level weakly-supervised semantic segmentation (WSSS) aims at learning semantic segmentation by adopting only image class labels. Existing approaches generally rely on class activation maps (CAM) to generate pseudo-masks and then train segmentation models. The main difficulty is that the CAM estimate only covers partial foreground objects. In this paper, we argue that the critical factor preventing to obtain the full object mask is the classification boundary mismatch problem in applying the CAM to WSSS. Because the CAM is optimized by the classification task, it focuses on the discrimination across different image-level classes. However, the WSSS requires to distinguish pixels sharing the same image-level class to separate them into the foreground and the background. To alleviate this contradiction, we propose an efficient end-to-end Intra-Class Discriminator (ICD) framework, which learns intra-class boundaries to help separate the foreground and the background within each image-level class. Without bells and whistles, our approach achieves the state-of-the-art performance of image label based WSSS, with mIoU 68.0% on the VOC 2012 semantic segmentation benchmark, demonstrating the effectiveness of the proposed approach.","PeriodicalId":6715,"journal":{"name":"2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","volume":"120 1","pages":"4282-4291"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83516192","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 145

Hyperbolic Visual Embedding Learning for Zero-Shot Recognition 零射击识别的双曲视觉嵌入学习

2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2020-06-01 DOI: 10.1109/cvpr42600.2020.00929

Shaoteng Liu, Jingjing Chen, Liangming Pan, C. Ngo, Tat-seng Chua, Yu-Gang Jiang

引用次数: 93