CVPR2020 检测类论文最全:136篇论文细分/代码/论文解读/下载

企业团队 / 2022-09-13 07:56

本文摘要:本周三,CVPR官方正式开放下载,极市第一时间将所有论文(共1467篇)举行了下载打包,详情见此处。为了利便大家进一步的学习,我们对这1467篇论文举行了整理,本次分享的是所有检测类论文,并将它们细分为3D目的检测、人脸检测、行动检测、视频目的检测、文本检测、行人检测等偏向,同时附上了相关解读和已开源论文的代码,共计149篇,并将其打包,获取方式见文末。

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本周三,CVPR官方正式开放下载,极市第一时间将所有论文(共1467篇)举行了下载打包,详情见此处。为了利便大家进一步的学习,我们对这1467篇论文举行了整理,本次分享的是所有检测类论文,并将它们细分为3D目的检测、人脸检测、行动检测、视频目的检测、文本检测、行人检测等偏向,同时附上了相关解读和已开源论文的代码,共计149篇,并将其打包,获取方式见文末。3D目的检测【1】Learning Deep Network for Detecting 3D Object Keypoints and 6D Poses作者:Wanqing Zhao, Shaobo Zhang, Ziyu Guan, Wei Zhao, Jinye Peng, Jianping Fan【2】DOPS: Learning to Detect 3D Objects and Predict Their 3D Shapes作者:Mahyar Najibi, Guangda Lai, Abhijit Kundu, Zhichao Lu, Vivek Rathod, Thomas Funkhouser, Caroline Pantofaru, David Ross, Larry S. Davis, Alireza Fathi【3】Train in Germany, Test in the USA: Making 3D Object Detectors Generalize作者:Yan Wang, Xiangyu Chen, Yurong You, Li Erran Li, Bharath Hariharan, Mark Campbell, Kilian Q. Weinberger, Wei-Lun Chao代码:https://github.com/cxy1997/3D_adapt_auto_driving【4】3DSSD: Point-Based 3D Single Stage Object Detector作者:Zetong Yang, Yanan Sun, Shu Liu, Jiaya Jia代码:https://github.com/tomztyang/3DSSD本文主要从point-based的研究入手,思量如何解决掉以前的point-based的方法的瓶颈,即时间和内存占有远远大于voxel-based的方法,从而作者设计了新的SA模块和抛弃了FP模块到达时间上可达25FPS,此外本文接纳一个anchor free Head,进一步淘汰时间和GPU显存,提出了3D center-ness label的表现,进一步提高了精度。

【5】FroDO: From Detections to 3D Objects作者:Martin Runz, Kejie Li, Meng Tang, Lingni Ma, Chen Kong, Tanner Schmidt, Ian Reid, Lourdes Agapito, Julian Straub, Steven Lovegrove, Richard Newcombe【6】Associate-3Ddet: Perceptual-to-Conceptual Association for 3D Point Cloud Object Detection作者:Liang Du, Xiaoqing Ye, Xiao Tan, Jianfeng Feng, Zhenbo Xu, Errui Ding, Shilei Wen【7】IDA-3D: Instance-Depth-Aware 3D Object Detection From Stereo Vision for Autonomous Driving作者:Wanli Peng, Hao Pan, He Liu, Yi Sun【8】DSGN: Deep Stereo Geometry Network for 3D Object Detection作者:Yilun Chen, Shu Liu, Xiaoyong Shen, Jiaya Jia代码:https://github.com/chenyilun95/DSGN【9】DR Loss: Improving Object Detection by Distributional Ranking作者:Qi Qian, Lei Chen, Hao Li, Rong Jin【10】MonoPair: Monocular 3D Object Detection Using Pairwise Spatial Relationships作者:Yongjian Chen, Lei Tai, Kai Sun, Mingyang Li【11】Structure Aware Single-Stage 3D Object Detection From Point Cloud作者:Chenhang He, Hui Zeng, Jianqiang Huang, Xian-Sheng Hua, Lei Zhang【12】Learning Depth-Guided Convolutions for Monocular 3D Object Detection作者:Mingyu Ding, Yuqi Huo, Hongwei Yi, Zhe Wang, Jianping Shi, Zhiwu Lu, Ping Luo【13】LiDAR-Based Online 3D Video Object Detection With Graph-Based Message Passing and Spatiotemporal Transformer Attention作者:Junbo Yin, Jianbing Shen, Chenye Guan, Dingfu Zhou, Ruigang Yang【14】SESS: Self-Ensembling Semi-Supervised 3D Object Detection作者:Na Zhao, Tat-Seng Chua, Gim Hee Lee【15】What You See is What You Get: Exploiting Visibility for 3D Object Detection作者:Peiyun Hu, Jason Ziglar, David Held, Deva Ramanan【16】Density-Based Clustering for 3D Object Detection in Point Clouds作者:Syeda Mariam Ahmed, Chee Meng Chew【17】Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation作者:Jiaming Sun, Linghao Chen, Yiming Xie, Siyu Zhang, Qinhong Jiang, Xiaowei Zhou, Hujun Bao代码:https://github.com/zju3dv/disprcn【18】PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection作者:Shaoshuai Shi, Chaoxu Guo, Li Jiang, Zhe Wang, Jianping Shi, Xiaogang Wang, Hongsheng Li代码:https://github.com/sshaoshuai/PV-RCNN【19】MLCVNet: Multi-Level Context VoteNet for 3D Object Detection作者:Qian Xie, Yu-Kun Lai, Jing Wu, Zhoutao Wang, Yiming Zhang, Kai Xu, Jun Wang代码:https://github.com/NUAAXQ/MLCVNet【20】A Hierarchical Graph Network for 3D Object Detection on Point Clouds作者:Jintai Chen, Biwen Lei, Qingyu Song, Haochao Ying, Danny Z. Chen, Jian Wu【21】HVNet: Hybrid Voxel Network for LiDAR Based 3D Object Detection作者:Maosheng Ye, Shuangjie Xu, Tongyi Cao3D目的检测是当前自动驾驶感知模块重要的一个环节,如何平衡3D物体检测的精度以及速度更是很是重要的一个研究话题。本文提出了一种新的基于点云的三维物体检测的统一网络:混淆体素网络(HVNet),通过在点级别上混淆尺度体素特征编码器(VFE)获得更好的体素特征编码方法,从而在速度和精度上获得提升。

与多种方法相比,HVNet在检测速度上有显着的提高。在KITTI 数据集自行车检测的中等难度级别(moderate)中,HVNet 的准确率比PointPillars方法横跨了8.44%。

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【22】Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud作者:Weijing Shi, Raj Rajkumar代码:https://github.com/WeijingShi/Point-GNN【23】Joint 3D Instance Segmentation and Object Detection for Autonomous Driving作者:Dingfu Zhou, Jin Fang, Xibin Song, Liu Liu, Junbo Yin, Yuchao Dai, Hongdong Li, Ruigang Yang【24】FocalMix: Semi-Supervised Learning for 3D Medical Image Detection作者:Dong Wang, Yuan Zhang, Kexin Zhang, Liwei Wang【25】ImVoteNet: Boosting 3D Object Detection in Point Clouds With Image Votes作者:Charles R. Qi, Xinlei Chen, Or Litany, Leonidas J. Guibas【26】PointPainting: Sequential Fusion for 3D Object Detection作者:Sourabh Vora, Alex H. Lang, Bassam Helou, Oscar Beijbom【27】End-to-End Pseudo-LiDAR for Image-Based 3D Object Detection作者:Rui Qian, Divyansh Garg, Yan Wang, Yurong You, Serge Belongie, Bharath Hariharan, Mark Campbell, Kilian Q. Weinberger, Wei-Lun Chao代码:https://github.com/mileyan/pseudo-LiDAR_e2e人物(交互)检测【28】Learning Human-Object Interaction Detection Using Interaction Points作者:Tiancai Wang, Tong Yang, Martin Danelljan, Fahad Shahbaz Khan, Xiangyu Zhang, Jian Sun【29】PPDM: Parallel Point Detection and Matching for Real-Time Human-Object Interaction Detection作者:Yue Liao, Si Liu, Fei Wang, Yanjie Chen, Chen Qian, Jiashi Feng代码:https://github.com/YueLiao/PPDM【30】(人物检测)Learning to Detect Important People in Unlabelled Images for Semi-Supervised Important People Detection作者:Fa-Ting Hong, Wei-Hong Li, Wei-Shi Zheng【31】(人体检测)VSGNet: Spatial Attention Network for Detecting Human Object Interactions Using Graph Convolutions作者:Oytun Ulutan, A S M Iftekhar, B. S. Manjunath行动检测【32】Combining Detection and Tracking for Human Pose Estimation in Videos作者:Manchen Wang, Joseph Tighe, Davide Modolo【33】G-TAD: Sub-Graph Localization for Temporal Action Detection作者:Mengmeng Xu, Chen Zhao, David S. Rojas, Ali Thabet, Bernard Ghanem【34】Learning to Discriminate Information for Online Action Detection作者:Hyunjun Eun, Jinyoung Moon, Jongyoul Park, Chanho Jung, Changick Kim活体检测【35】ZSTAD: Zero-Shot Temporal Activity Detection作者:Lingling Zhang, Xiaojun Chang, Jun Liu, Minnan Luo, Sen Wang, Zongyuan Ge, Alexander Hauptmann显著性检测【36】Learning Selective Self-Mutual Attention for RGB-D Saliency Detection作者:Nian Liu, Ni Zhang, Junwei Han【37】Label Decoupling Framework for Salient Object Detection作者:Jun Wei, Shuhui Wang, Zhe Wu, Chi Su, Qingming Huang, Qi Tian【38】Weakly-Supervised Salient Object Detection via Scribble Annotations作者:Jing Zhang, Xin Yu, Aixuan Li, Peipei Song, Bowen Liu, Yuchao Dai【39】UC-Net: Uncertainty Inspired RGB-D Saliency Detection via Conditional Variational Autoencoders作者:Jing Zhang, Deng-Ping Fan, Yuchao Dai, Saeed Anwar, Fatemeh Sadat Saleh, Tong Zhang, Nick Barnes代码:https://github.com/JingZhang617/UCNet【40】Adaptive Graph Convolutional Network With Attention Graph Clustering for Co-Saliency Detection作者:Kaihua Zhang, Tengpeng Li, Shiwen Shen, Bo Liu, Jin Chen, Qingshan Liu【41】A2dele: Adaptive and Attentive Depth Distiller for Efficient RGB-D Salient Object Detection作者:Yongri Piao, Zhengkun Rong, Miao Zhang, Weisong Ren, Huchuan Lu【42】Interactive Two-Stream Decoder for Accurate and Fast Saliency Detection作者:Huajun Zhou, Xiaohua Xie, Jian-Huang Lai, Zixuan Chen, Lingxiao Yang【43】Multi-Scale Interactive Network for Salient Object Detection作者:Youwei Pang, Xiaoqi Zhao, Lihe Zhang, Huchuan Lu【44】Taking a Deeper Look at Co-Salient Object Detection作者:Deng-Ping Fan, Zheng Lin, Ge-Peng Ji, Dingwen Zhang, Huazhu Fu, Ming-Ming Cheng【45】JL-DCF: Joint Learning and Densely-Cooperative Fusion Framework for RGB-D Salient Object Detection作者:Keren Fu, Deng-Ping Fan, Ge-Peng Ji, Qijun Zhao代码:https://github.com/kerenfu/JLDCF/【46】Select, Supplement and Focus for RGB-D Saliency Detection作者:Miao Zhang, Weisong Ren, Yongri Piao, Zhengkun Rong, Huchuan Lu伪装/伪造检测【47】Camouflaged Object Detection作者:Deng-Ping Fan, Ge-Peng Ji, Guolei Sun, Ming-Ming Cheng, Jianbing Shen, Ling Shao【48】DOA-GAN: Dual-Order Attentive Generative Adversarial Network for Image Copy-Move Forgery Detection and Localization作者:Ashraful Islam, Chengjiang Long, Arslan Basharat, Anthony Hoogs【49】Advancing High Fidelity Identity Swapping for Forgery Detection作者:Lingzhi Li, Jianmin Bao, Hao Yang, Dong Chen, Fang Wen【50】Advancing High Fidelity Identity Swapping for Forgery Detection作者:Lingzhi Li, Jianmin Bao, Hao Yang, Dong Chen, Fang Wen人脸检测【51】Cross-Domain Face Presentation Attack Detection via Multi-Domain Disentangled Representation Learning作者:Guoqing Wang, Hu Han, Shiguang Shan, Xilin Chen【52】HAMBox: Delving Into Mining High-Quality Anchors on Face Detection作者:Yang Liu, Xu Tang, Junyu Han, Jingtuo Liu, Dinger Rui, Xiang Wu【53】BFBox: Searching Face-Appropriate Backbone and Feature Pyramid Network for Face Detector作者:Yang Liu, Xu Tang【54】Global Texture Enhancement for Fake Face Detection in the Wild作者:Zhengzhe Liu, Xiaojuan Qi, Philip H.S. Torr【55】(数据集)DeeperForensics-1.0: A Large-Scale Dataset for Real-World Face Forgery Detection作者:Liming Jiang, Ren Li, Wayne Wu, Chen Qian, Chen Change Loy【56】Face X-Ray for More General Face Forgery Detection作者:Lingzhi Li, Jianmin Bao, Ting Zhang, Hao Yang, Dong Chen, Fang Wen, Baining Guo【57】On the Detection of Digital Face Manipulation作者:Hao Dang, Feng Liu, Joel Stehouwer, Xiaoming Liu, Anil K. Jain【58】Attention-Driven Cropping for Very High Resolution Facial Landmark Detection作者:Prashanth Chandran, Derek Bradley, Markus Gross, Thabo Beeler小样本/零样本【59】Few-Shot Object Detection With Attention-RPN and Multi-Relation Detector作者:Qi Fan, Wei Zhuo, Chi-Keung Tang, Yu-Wing Tai本文提出了新的少样本目的检测算法,创新点包罗Attention-RPN、多关系检测器以及对比训练计谋,另外还构建了包罗1000类的少样本检测数据集FSOD,在FSOD上训练获得的论文模型能够直接迁移到新种别的检测中,不需要fine-tune。【60】Incremental Few-Shot Object Detection作者:Juan-Manuel Perez-Rua, Xiatian Zhu, Timothy M. Hospedales, Tao Xiang【61】Don't Even Look Once: Synthesizing Features for Zero-Shot Detection作者:Pengkai Zhu, Hanxiao Wang, Venkatesh Saligrama异常检测【62】Uninformed Students: Student-Teacher Anomaly Detection With Discriminative Latent Embeddings作者:Paul Bergmann, Michael Fauser, David Sattlegger, Carsten Steger【63】Graph Embedded Pose Clustering for Anomaly Detection作者:Amir Markovitz, Gilad Sharir, Itamar Friedman, Lihi Zelnik-Manor, Shai Avidan【64】Self-Trained Deep Ordinal Regression for End-to-End Video Anomaly Detection作者:Guansong Pang, Cheng Yan, Chunhua Shen, Anton van den Hengel, Xiao Bai【65】Learning Memory-Guided Normality for Anomaly Detection作者:Hyunjong Park, Jongyoun Noh, Bumsub Ham半监视/弱监视/无监视【66】DUNIT: Detection-Based Unsupervised Image-to-Image Translation作者:Deblina Bhattacharjee, Seungryong Kim, Guillaume Vizier, Mathieu Salzmann【67】A Multi-Task Mean Teacher for Semi-Supervised Shadow Detection作者:Zhihao Chen, Lei Zhu, Liang Wan, Song Wang, Wei Feng, Pheng-Ann Heng【68】Instance-Aware, Context-Focused, and Memory-Efficient Weakly Supervised Object Detection作者:Zhongzheng Ren, Zhiding Yu, Xiaodong Yang, Ming-Yu Liu, Yong Jae Lee, Alexander G. Schwing, Jan Kautz代码:https://github.com/NVlabs/wetectron【69】SLV: Spatial Likelihood Voting for Weakly Supervised Object Detection作者:Ze Chen, Zhihang Fu, Rongxin Jiang, Yaowu Chen, Xian-Sheng Hua麋集检测【70】D3Feat: Joint Learning of Dense Detection and Description of 3D Local Features作者:Xuyang Bai, Zixin Luo, Lei Zhou, Hongbo Fu, Long Quan, Chiew-Lan Tai【71】Real-Time Panoptic Segmentation From Dense Detections作者:Rui Hou, Jie Li, Arjun Bhargava, Allan Raventos, Vitor Guizilini, Chao Fang, Jerome Lynch, Adrien Gaidon文本检测【72】Deep Relational Reasoning Graph Network for Arbitrary Shape Text Detection作者:Shi-Xue Zhang, Xiaobin Zhu, Jie-Bo Hou, Chang Liu, Chun Yang, Hongfa Wang, Xu-Cheng Yin【73】ContourNet: Taking a Further Step Toward Accurate Arbitrary-Shaped Scene Text Detection作者:Yuxin Wang, Hongtao Xie, Zheng-Jun Zha, Mengting Xing, Zilong Fu, Yongdong Zhang视频目的检测【74】Memory Enhanced Global-Local Aggregation for Video Object Detection作者:Yihong Chen, Yue Cao, Han Hu, Liwei Wang【75】Beyond Short-Term Snippet: Video Relation Detection With Spatio-Temporal Global Context作者:Chenchen Liu, Yang Jin, Kehan Xu, Guoqiang Gong, Yadong Mu【76】Detecting Attended Visual Targets in Video作者:Eunji Chong, Yongxin Wang, Nataniel Ruiz, James M. Rehg【77】LiDAR-Based Online 3D Video Object Detection With Graph-Based Message Passing and Spatiotemporal Transformer Attention作者:Junbo Yin, Jianbing Shen, Chenye Guan, Dingfu Zhou, Ruigang Yang代码:https://github.com/yinjunbo/3DVID【78】Combining Detection and Tracking for Human Pose Estimation in Videos作者:Manchen Wang, Joseph Tighe, Davide Modolo行人检测【79】STINet: Spatio-Temporal-Interactive Network for Pedestrian Detection and Trajectory Prediction作者:Zhishuai Zhang, Jiyang Gao, Junhua Mao, Yukai Liu, Dragomir Anguelov, Congcong Li【80】Temporal-Context Enhanced Detection of Heavily Occluded Pedestrians作者:Jialian Wu, Chunluan Zhou, Ming Yang, Qian Zhang, Yuan Li, Junsong Yuan【81】Where, What, Whether: Multi-Modal Learning Meets Pedestrian Detection作者:Yan Luo, Chongyang Zhang, Muming Zhao, Hao Zhou, Jun Sun【82】NMS by Representative Region: Towards Crowded Pedestrian Detection by Proposal Pairing作者:Xin Huang, Zheng Ge, Zequn Jie, Osamu Yoshie移动目的检测【83】MnasFPN: Learning Latency-Aware Pyramid Architecture for Object Detection on Mobile Devices作者:Bo Chen, Golnaz Ghiasi, Hanxiao Liu, Tsung-Yi Lin, Dmitry Kalenichenko, Hartwig Adam, Quoc V. Le通用目的检测/其他【84】(anchor-free)Bridging the Gap Between Anchor-Based and Anchor-Free Detection via Adaptive Training Sample Selection作者:Shifeng Zhang, Cheng Chi, Yongqiang Yao, Zhen Lei, Stan Z. Li代码:https://github.com/sfzhang15/ATSS本文指出one-stage anchor-based和center-based anchor-free检测算法间的差异主要来自于正负样本的选择,基于此提出ATSS(Adaptive Training Sample Selection)方法,该方法能够自动凭据GT的相关统计特征选择合适的anchor box作为正样本,在不带来分外盘算量和参数的情况下,能够大幅提升模型的性能。【85】(大规模/不平衡目的检测)Large-Scale Object Detection in the Wild From Imbalanced Multi-Labels作者:Junran Peng, Xingyuan Bu, Ming Sun, Zhaoxiang Zhang, Tieniu Tan, Junjie Yan【86】DLWL: Improving Detection for Lowshot Classes With Weakly Labelled Data作者:Vignesh Ramanathan, Rui Wang, Dhruv Mahajan【87】Correlation-Guided Attention for Corner Detection Based Visual Tracking作者:Fei Du, Peng Liu, Wei Zhao, Xianglong Tang【88】(特征检测)Reinforced Feature Points: Optimizing Feature Detection and Description for a High-Level Task作者:Aritra Bhowmik, Stefan Gumhold, Carsten Rother, Eric Brachmann【89】Seeing Around Street Corners: Non-Line-of-Sight Detection and Tracking In-the-Wild Using Doppler Radar作者:Nicolas Scheiner, Florian Kraus, Fangyin Wei, Buu Phan, Fahim Mannan, Nils Appenrodt, Werner Ritter, Jurgen Dickmann, Klaus Dietmayer, Bernhard Sick, Felix Heide【90】Learning to Observe: Approximating Human Perceptual Thresholds for Detection of Suprathreshold Image Transformations作者:Alan Dolhasz, Carlo Harvey, Ian Williams【91】Siam R-CNN: Visual Tracking by Re-Detection作者:Paul Voigtlaender, Jonathon Luiten, Philip H.S. Torr, Bastian Leibe【92】Progressive Mirror Detection作者:Jiaying Lin, Guodong Wang, Rynson W.H. Lau【93】(阴影检测)Instance Shadow Detection作者:Tianyu Wang, Xiaowei Hu, Qiong Wang, Pheng-Ann Heng, Chi-Wing Fu【94】(阴影检测)A Multi-Task Mean Teacher for Semi-Supervised Shadow Detection作者:Zhihao Chen, Lei Zhu, Liang Wan, Song Wang, Wei Feng, Pheng-Ann Heng【95】(玻璃检测)Don't Hit Me! Glass Detection in Real-World Scenes作者:Haiyang Mei, Xin Yang, Yang Wang, Yuanyuan Liu, Shengfeng He, Qiang Zhang, Xiaopeng Wei, Rynson W.H. Lau【96】Rethinking Classification and Localization for Object Detection作者:Yue Wu, Yinpeng Chen, Lu Yuan, Zicheng Liu, Lijuan Wang, Hongzhi Li, Yun Fu【97】(多anchor)Multiple Anchor Learning for Visual Object Detection作者:Wei Ke, Tianliang Zhang, Zeyi Huang, Qixiang Ye, Jianzhuang Liu, Dong Huang【98】Memory Enhanced Global-Local Aggregation for Video Object Detection作者:Yihong Chen, Yue Cao, Han Hu, Liwei Wang 代码:https://github.com/Scalsol/mega.pytorch【99】CentripetalNet: Pursuing High-Quality Keypoint Pairs for Object Detection作者:Zhiwei Dong, Guoxuan Li, Yue Liao, Fei Wang, Pengju Ren, Chen Qian代码:https://github.com/KiveeDong/CentripetalNet本文提出一种使用向心偏移来对同一实例中的角点举行配对的CentripetalNet向心网络。向心网络可以预测角点的位置和向心偏移,并匹配移动效果对齐的角。

联合位置信息,这种方法比传统的嵌入方法更准确地匹配角点。角池将界限框内的信息提取到界限上。为了使这些信息在角落里更容易被察觉,作者又设计了一个交织星可变形卷积网络来适应特征。

除了检测,通过为作者的CentripetalNet安置一个mask预测模块来探索anchor-free检测器上的实例支解。【100】(one-stage)Learning From Noisy Anchors for One-Stage Object Detection作者:Hengduo Li, Zuxuan Wu, Chen Zhu, Caiming Xiong, Richard Socher, Larry S. Davis【101】EfficientDet: Scalable and Efficient Object Detection作者:Mingxing Tan, Ruoming Pang, Quoc V. Le代码:https://github.com/google/automl/tree/master/efficientdet本文系统性地研究了多种检测器架构设计,试图解决该问题。

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基于单阶段检测器范式,研究者检察了主干网络、特征融合和界限框/种别预测网络的设计选择,发现了两大主要挑战:高效的多尺度特征融合和模型缩放。针对这两项挑战,研究者提出了应对方法:高效的多尺度特征融合和模型缩放。【102】Overcoming Classifier Imbalance for Long-Tail Object Detection With Balanced Group Softmax作者:Yu Li, Tao Wang, Bingyi Kang, Sheng Tang, Chunfeng Wang, Jintao Li, Jiashi Feng【103】Dynamic Refinement Network for Oriented and Densely Packed Object Detection作者:Xingjia Pan, Yuqiang Ren, Kekai Sheng, Weiming Dong, Haolei Yuan, Xiaowei Guo, Chongyang Ma, Changsheng Xu代码:https://github.com/Anymake/DRN_CVPR2020【104】Noise-Aware Fully Webly Supervised Object Detection作者:Yunhang Shen, Rongrong Ji, Zhiwei Chen, Xiaopeng Hong, Feng Zheng, Jianzhuang Liu, Mingliang Xu, Qi Tian【105】Hit-Detector: Hierarchical Trinity Architecture Search for Object Detection作者:Jianyuan Guo, Kai Han, Yunhe Wang, Chao Zhang, Zhaohui Yang, Han Wu, Xinghao Chen, Chang Xu代码:https://github.com/ggjy/HitDet.pytorch【106】D2Det: Towards High Quality Object Detection and Instance Segmentation作者:Jiale Cao, Hisham Cholakkal, Rao Muhammad Anwer, Fahad Shahbaz Khan, Yanwei Pang, Ling Shao代码:https://github.com/JialeCao001/D2Det【107】Prime Sample Attention in Object Detection作者:Yuhang Cao, Kai Chen, Chen Change Loy, Dahua Lin【108】Exploring Categorical Regularization for Domain Adaptive Object Detection作者:Chang-Dong Xu, Xing-Ran Zhao, Xin Jin, Xiu-Shen Wei【109】SP-NAS: Serial-to-Parallel Backbone Search for Object Detection作者:Chenhan Jiang, Hang Xu, Wei Zhang, Xiaodan Liang, Zhenguo Li【110】NAS-FCOS: Fast Neural Architecture Search for Object Detection作者:Ning Wang, Yang Gao, Hao Chen, Peng Wang, Zhi Tian, Chunhua Shen, Yanning Zhang【111】Detection in Crowded Scenes: One Proposal, Multiple Predictions作者:Xuangeng Chu, Anlin Zheng, Xiangyu Zhang, Jian Sun代码:https://github.com/megvii-model/CrowdDetection【112】Cross-Domain Detection via Graph-Induced Prototype Alignment作者:Minghao Xu, Hang Wang, Bingbing Ni, Qi Tian, Wenjun Zhang【113】AugFPN: Improving Multi-Scale Feature Learning for Object Detection作者:Chaoxu Guo, Bin Fan, Qian Zhang, Shiming Xiang, Chunhong Pan【114】Robust Object Detection Under Occlusion With Context-Aware CompositionalNets作者:Angtian Wang, Yihong Sun, Adam Kortylewski, Alan L. Yuille【115】(跨域目的检测)Cross-Domain Document Object Detection: Benchmark Suite and Method作者:Kai Li, Curtis Wigington, Chris Tensmeyer, Handong Zhao, Nikolaos Barmpalios, Vlad I. Morariu, Varun Manjunatha, Tong Sun, Yun Fu【116】(跨域目的检测)Cross-domain Object Detection through Coarse-to-Fine Feature Adaptation作者:Yangtao Zheng, Di Huang, Songtao Liu, Yunhong Wang近年来,在基于深度学习的目的检测中见证了庞大的进步。

可是,由于domain shift问题,将现成的检测器应用于未知的域会导致性能显著下降。为相识决这个问题,本文提出了一种新颖的从粗到精的特征自适应方法来举行跨域目的检测。由于这种从粗到细的特征自适应,前景区域中的领域知识可以有效地通报。

在种种跨域检测方案中举行了广泛的实验,效果证明晰所提出方法的广泛适用性和有效性。【117】Exploring Bottom-Up and Top-Down Cues With Attentive Learning for Webly Supervised Object Detection作者:Zhonghua Wu, Qingyi Tao, Guosheng Lin, Jianfei Cai【118】Context R-CNN: Long Term Temporal Context for Per-Camera Object Detection作者:Sara Beery, Guanhang Wu, Vivek Rathod, Ronny Votel, Jonathan Huang【119】Mixture Dense Regression for Object Detection and Human Pose Estimation作者:Ali Varamesh, Tinne Tuytelaars【120】Offset Bin Classification Network for Accurate Object Detection作者:Heqian Qiu, Hongliang Li, Qingbo Wu, Hengcan Shi【121】(Single Shot)NETNet: Neighbor Erasing and Transferring Network for Better Single Shot Object Detection作者:Yazhao Li, Yanwei Pang, Jianbing Shen, Jiale Cao, Ling Shao【122】Scale-Equalizing Pyramid Convolution for Object Detection作者:Xinjiang Wang, Shilong Zhang, Zhuoran Yu, Litong Feng, Wayne Zhang代码:https://github.com/jshilong/SEPC为了更好的解决物体检测中的尺度问题,本文重新设计了经典的单阶段检测器的FPN以及HEAD结构,通过结构更具等变性的特征金子塔,以提高检测器应对尺度变化的鲁棒性,可以使单阶段检测器在coco上提升~4mAP。【123】(界限检测)Joint Semantic Segmentation and Boundary Detection Using Iterative Pyramid Contexts作者:Mingmin Zhen, Jinglu Wang, Lei Zhou, Shiwei Li, Tianwei Shen, Jiaxiang Shang, Tian Fang, Long Quan【124】Physically Realizable Adversarial Examples for LiDAR Object Detection作者:James Tu, Mengye Ren, Sivabalan Manivasagam, Ming Liang, Bin Yang, Richard Du, Frank Cheng, Raquel Urtasun【125】Hierarchical Graph Attention Network for Visual Relationship Detection作者:Li Mi, Zhenzhong Chen【126】Training a Steerable CNN for Guidewire Detection作者:Donghang Li, Adrian Barbu【127】Deep Residual Flow for Out of Distribution Detection作者:Ev Zisselman, Aviv Tamar【128】Cylindrical Convolutional Networks for Joint Object Detection and Viewpoint Estimation作者:Sunghun Joung, Seungryong Kim, Hanjae Kim, Minsu Kim, Ig-Jae Kim, Junghyun Cho, Kwanghoon Sohn【129】Learning a Unified Sample Weighting Network for Object Detection作者:Qi Cai, Yingwei Pan, Yu Wang, Jingen Liu, Ting Yao, Tao Mei【130】Seeing without Looking: Contextual Rescoring of Object Detections for AP Maximization作者:Lourenco V. Pato, Renato Negrinho, Pedro M. Q. Aguiar【131】(single stage)RetinaTrack: Online Single Stage Joint Detection and Tracking作者:Zhichao Lu, Vivek Rathod, Ronny Votel, Jonathan Huang【132】Universal Physical Camouflage Attacks on Object Detectors作者:Lifeng Huang, Chengying Gao, Yuyin Zhou, Cihang Xie, Alan L. Yuille, Changqing Zou, Ning Liu【133】BiDet: An Efficient Binarized Object Detector作者:Ziwei Wang, Ziyi Wu, Jiwen Lu, Jie Zhou代码:https://github.com/ZiweiWangTHU/BiDet【134】Harmonizing Transferability and Discriminability for Adapting Object Detectors作者:Chaoqi Chen, Zebiao Zheng, Xinghao Ding, Yue Huang, Qi Dou代码:https://github.com/chaoqichen/HTCN【135】SaccadeNet: A Fast and Accurate Object Detector作者:Shiyi Lan, Zhou Ren, Yi Wu, Larry S. Davis, Gang Hua【136】Generalized ODIN: Detecting Out-of-Distribution Image Without Learning From Out-of-Distribution Data作者:Yen-Chang Hsu, Yilin Shen, Hongxia Jin, Zsolt Kira【137】A Programmatic and Semantic Approach to Explaining and Debugging Neural Network Based Object Detectors作者:Edward Kim, Divya Gopinath, Corina Pasareanu, Sanjit A. Seshia【138】Revisiting the Sibling Head in Object Detector作者:Guanglu Song, Yu Liu, Xiaogang Wang代码:https://github.com/Sense-X/TSD现在许多研究讲明目的检测中的分类分支和定位分支存在较大的偏差,本文从sibling head革新入手,跳出通例的优化偏向,提出TSD方法解决混淆任务带来的内在冲突,从主干的proposal中学习差别的task-aware proposal,同时联合PC来保证TSD的性能,在COCO上到达了51.2mAP。

【139】Detecting Adversarial Samples Using Influence Functions and Nearest Neighbors作者:Gilad Cohen, Guillermo Sapiro, Raja Giryes【140】(特征检测)Reinforced Feature Points: Optimizing Feature Detection and Description for a High-Level Task作者:Aritra Bhowmik, Stefan Gumhold, Carsten Rother, Eric Brachmann【141】Seeing Around Street Corners: Non-Line-of-Sight Detection and Tracking In-the-Wild Using Doppler Radar作者:Nicolas Scheiner, Florian Kraus, Fangyin Wei, Buu Phan, Fahim Mannan, Nils Appenrodt, Werner Ritter, Jurgen Dickmann, Klaus Dietmayer, Bernhard Sick, Felix Heide【142】Learning to Observe: Approximating Human Perceptual Thresholds for Detection of Suprathreshold Image Transformations作者:Alan Dolhasz, Carlo Harvey, Ian Williams【143】Siam R-CNN: Visual Tracking by Re-Detection作者:Paul Voigtlaender, Jonathon Luiten, Philip H.S. Torr, Bastian Leibe【144】Progressive Mirror Detection作者:Jiaying Lin, Guodong Wang, Rynson W.H. Lau【145】(阴影检测)Instance Shadow Detection作者:Tianyu Wang, Xiaowei Hu, Qiong Wang, Pheng-Ann Heng, Chi-Wing Fu【146】(阴影检测)A Multi-Task Mean Teacher for Semi-Supervised Shadow Detection作者:Zhihao Chen, Lei Zhu, Liang Wan, Song Wang, Wei Feng, Pheng-Ann Heng【147】(玻璃检测)Don't Hit Me! Glass Detection in Real-World Scenes作者:Haiyang Mei, Xin Yang, Yang Wang, Yuanyuan Liu, Shengfeng He, Qiang Zhang, Xiaopeng Wei, Rynson W.H. Lau【148】Rethinking Classification and Localization for Object Detection作者:Yue Wu, Yinpeng Chen, Lu Yuan, Zicheng Liu, Lijuan Wang, Hongzhi Li, Yun Fu【149】(多anchor)Multiple Anchor Learning for Visual Object Detection作者:Wei Ke, Tianliang Zhang, Zeyi Huang, Qixiang Ye, Jianzhuang Liu, Dong Huang后台回复 CVPR检测,即可获取136篇论文打包下载链接。


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