2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)最新文献_第6页

Select, Supplement and Focus for RGB-D Saliency Detection RGB-D显著性检测的选择、补充和聚焦

2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2020-06-01 DOI: 10.1109/CVPR42600.2020.00353

Miao Zhang, Weisong Ren, Yongri Piao, Zhengkun Rong, Huchuan Lu

引用次数: 147

Which Is Plagiarism: Fashion Image Retrieval Based on Regional Representation for Design Protection 哪是抄袭:基于地域表征的时尚图像检索设计保护

2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2020-06-01 DOI: 10.1109/CVPR42600.2020.00267

Yining Lang, Yuan He, Fan Yang, Jianfeng Dong, Hui Xue

{"title":"Which Is Plagiarism: Fashion Image Retrieval Based on Regional Representation for Design Protection","authors":"Yining Lang, Yuan He, Fan Yang, Jianfeng Dong, Hui Xue","doi":"10.1109/CVPR42600.2020.00267","DOIUrl":"https://doi.org/10.1109/CVPR42600.2020.00267","url":null,"abstract":"With the rapid growth of e-commerce and the popularity of online shopping, fashion retrieval has received considerable attention in the computer vision community. Different from the existing works that mainly focus on identical or similar fashion item retrieval, in this paper, we aim to study the plagiarized clothes retrieval which is somewhat ignored in the academic community while itself has great application value. One of the key challenges is that plagiarized clothes are usually modified in a certain region on the original design to escape the supervision by traditional retrieval methods. To relieve it, we propose a novel network named Plagiarized-Search-Net (PS-Net) based on regional representation, where we utilize the landmarks to guide the learning of regional representations and compare fashion items region by region. Besides, we propose a new dataset named Plagiarized Fashion for plagiarized clothes retrieval, which provides a meaningful complement to the existing fashion retrieval field. Experiments on Plagiarized Fashion dataset verify that our approach is superior to other instance-level counterparts for plagiarized clothes retrieval, showing a promising result for original design protection. Moreover, our PS-Net can also be adapted to traditional fashion retrieval and landmark estimation tasks and achieves the state-of-the-art performance on the DeepFashion and DeepFashion2 datasets.","PeriodicalId":6715,"journal":{"name":"2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","volume":"91 1","pages":"2592-2601"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80529364","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: 21

HAMBox: Delving Into Mining High-Quality Anchors on Face Detection HAMBox:基于人脸检测的高质量锚点挖掘

2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2020-06-01 DOI: 10.1109/cvpr42600.2020.01306

Yang Liu, Xu Tang, Junyu Han, Jingtuo Liu, Dinger Rui, Xiang Wu

引用次数: 35

Towards Global Explanations of Convolutional Neural Networks With Concept Attribution 基于概念归因的卷积神经网络的全局解释

2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2020-06-01 DOI: 10.1109/CVPR42600.2020.00868

Weibin Wu, Yuxin Su, Xixian Chen, Shenglin Zhao, Irwin King, M. Lyu, Yu-Wing Tai

{"title":"Towards Global Explanations of Convolutional Neural Networks With Concept Attribution","authors":"Weibin Wu, Yuxin Su, Xixian Chen, Shenglin Zhao, Irwin King, M. Lyu, Yu-Wing Tai","doi":"10.1109/CVPR42600.2020.00868","DOIUrl":"https://doi.org/10.1109/CVPR42600.2020.00868","url":null,"abstract":"With the growing prevalence of convolutional neural networks (CNNs), there is an urgent demand to explain their behaviors. Global explanations contribute to understanding model predictions on a whole category of samples, and thus have attracted increasing interest recently. However, existing methods overwhelmingly conduct separate input attribution or rely on local approximations of models, making them fail to offer faithful global explanations of CNNs. To overcome such drawbacks, we propose a novel two-stage framework, Attacking for Interpretability (AfI), which explains model decisions in terms of the importance of user-defined concepts. AfI first conducts a feature occlusion analysis, which resembles a process of attacking models to derive the category-wide importance of different features. We then map the feature importance to concept importance through ad-hoc semantic tasks. Experimental results confirm the effectiveness of AfI and its superiority in providing more accurate estimations of concept importance than existing proposals.","PeriodicalId":6715,"journal":{"name":"2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","volume":"86 1","pages":"8649-8658"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80790460","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: 38

Minimal Solvers for 3D Scan Alignment With Pairs of Intersecting Lines 具有对相交线的三维扫描对齐的最小解

2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2020-06-01 DOI: 10.1109/cvpr42600.2020.00726

André Mateus, S. Ramalingam, Pedro Miraldo

{"title":"Minimal Solvers for 3D Scan Alignment With Pairs of Intersecting Lines","authors":"André Mateus, S. Ramalingam, Pedro Miraldo","doi":"10.1109/cvpr42600.2020.00726","DOIUrl":"https://doi.org/10.1109/cvpr42600.2020.00726","url":null,"abstract":"We explore the possibility of using line intersection constraints for 3D scan registration. Typical 3D registration algorithms exploit point and plane correspondences, while line intersection constraints have not been used in the context of 3D scan registration before. Constraints from a match of pairs of intersecting lines in two 3D scans can be seen as two 3D line intersections, a plane correspondence, and a point correspondence. In this paper, we present minimal solvers that combine these different type of constraints: 1) three line intersections and one point match; 2) one line intersection and two point matches; 3) three line intersections and one plane match; 4) one line intersection and two plane matches; and 5) one line intersection, one point match, and one plane match. To use all the available solvers, we present a hybrid RANSAC loop. We propose a non-linear refinement technique using all the inliers obtained from the RANSAC. Vast experiments with simulated data and two real-data data-sets show that the use of these features and the combined solvers improve the accuracy. The code is available.","PeriodicalId":6715,"journal":{"name":"2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","volume":"90 1","pages":"7232-7242"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80321975","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: 8

Private-kNN: Practical Differential Privacy for Computer Vision Private-kNN:计算机视觉的实用差分隐私

2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2020-06-01 DOI: 10.1109/CVPR42600.2020.01187

Yuqing Zhu, Xiang Yu, Manmohan Chandraker, Yu-Xiang Wang

{"title":"Private-kNN: Practical Differential Privacy for Computer Vision","authors":"Yuqing Zhu, Xiang Yu, Manmohan Chandraker, Yu-Xiang Wang","doi":"10.1109/CVPR42600.2020.01187","DOIUrl":"https://doi.org/10.1109/CVPR42600.2020.01187","url":null,"abstract":"With increasing ethical and legal concerns on privacy for deep models in visual recognition, differential privacy has emerged as a mechanism to disguise membership of sensitive data in training datasets. Recent methods like Private Aggregation of Teacher Ensembles (PATE) leverage a large ensemble of teacher models trained on disjoint subsets of private data, to transfer knowledge to a student model with privacy guarantees. However, labeled vision data is often expensive and datasets, when split into many disjoint training sets, lead to signiï¬cantly sub-optimal accuracy and thus hardly sustain good privacy bounds. We propose a practically data-efï¬cient scheme based on private release of k-nearest neighbor (kNN) queries, which altogether avoids splitting the training dataset. Our approach allows the use of privacy-ampliï¬cation by subsampling and iterative reï¬nement of the kNN feature embedding. We rigorously analyze the theoretical properties of our method and demonstrate strong experimental performance on practical computer vision datasets for face attribute recognition and person reidentiï¬cation. In particular, we achieve comparable or better accuracy than PATE while reducing more than 90% of the privacy loss, thereby providing the “most practical method to-date” for private deep learning in computer vision.","PeriodicalId":6715,"journal":{"name":"2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","volume":"88 1","pages":"11851-11859"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75866451","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: 55

DLWL: Improving Detection for Lowshot Classes With Weakly Labelled Data DLWL:改进对带有弱标记数据的低像素类的检测

2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2020-06-01 DOI: 10.1109/cvpr42600.2020.00936

Vignesh Ramanathan, Rui Wang, D. Mahajan

{"title":"DLWL: Improving Detection for Lowshot Classes With Weakly Labelled Data","authors":"Vignesh Ramanathan, Rui Wang, D. Mahajan","doi":"10.1109/cvpr42600.2020.00936","DOIUrl":"https://doi.org/10.1109/cvpr42600.2020.00936","url":null,"abstract":"Large detection datasets have a long tail of lowshot classes with very few bounding box annotations. We wish to improve detection for lowshot classes with weakly labelled web-scale datasets only having image-level labels. This requires a detection framework that can be jointly trained with limited number of bounding box annotated images and large number of weakly labelled images. Towards this end, we propose a modification to the FRCNN model to automatically infer label assignment for objects proposals from weakly labelled images during training. We pose this label assignment as a Linear Program with constraints on the number and overlap of object instances in an image. We show that this can be solved efficiently during training for weakly labelled images. Compared to just training with few annotated examples, augmenting with weakly labelled examples in our framework provides significant gains. We demonstrate this on the LVIS dataset 3.5 gain in AP as well as different lowshot variants of the COCO dataset. We provide a thorough analysis of the effect of amount of weakly labelled and fully labelled data required to train the detection model. Our DLWL framework can also outperform self-supervised baselines like omni-supervision for lowshot classes.","PeriodicalId":6715,"journal":{"name":"2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","volume":"4 1","pages":"9339-9349"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81371611","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: 20

Spherical Space Domain Adaptation With Robust Pseudo-Label Loss 具有鲁棒伪标签损失的球面空间域自适应

2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2020-06-01 DOI: 10.1109/cvpr42600.2020.00912

Xiang Gu, Jian Sun, Zongben Xu

引用次数: 97

Instance Guided Proposal Network for Person Search 基于实例的人物搜索建议网络

2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2020-06-01 DOI: 10.1109/CVPR42600.2020.00266

Wenkai Dong, Zhaoxiang Zhang, Chunfeng Song, T. Tan

{"title":"Instance Guided Proposal Network for Person Search","authors":"Wenkai Dong, Zhaoxiang Zhang, Chunfeng Song, T. Tan","doi":"10.1109/CVPR42600.2020.00266","DOIUrl":"https://doi.org/10.1109/CVPR42600.2020.00266","url":null,"abstract":"Person detection networks have been widely used in person search. These detectors discriminate persons from the background and generate proposals of all the persons from a gallery of scene images for each query. However, such a large number of proposals have a negative influence on the following identity matching process because many distractors are involved. In this paper, we propose a new detection network for person search, named Instance Guided Proposal Network (IGPN), which can learn the similarity between query persons and proposals. Thus, we can decrease proposals according to the similarity scores. To incorporate information of the query into the detection network, we introduce the Siamese region proposal network to Faster-RCNN and we propose improved cross-correlation layers to alleviate the imbalance of parameters distribution. Furthermore, we design a local relation block and a global relation branch to leverage the proposal-proposal relations and query-scene relations, respectively. Extensive experiments show that our method improves the person search performance through decreasing proposals and achieves competitive performance on two large person search benchmark datasets, CUHK-SYSU and PRW.","PeriodicalId":6715,"journal":{"name":"2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","volume":"487 1","pages":"2582-2591"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78829340","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: 66

Advancing High Fidelity Identity Swapping for Forgery Detection 推进高保真身份交换伪造检测

2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Pub Date : 2020-06-01 DOI: 10.1109/cvpr42600.2020.00512

Lingzhi Li, Jianmin Bao, Hao Yang, Dong Chen, Fang Wen

引用次数: 133