本文爲極市平臺公衆號編輯整理，未經授權，不得二次轉載。
原文鏈接：CVPR2020 檢測類論文最全彙總：136 篇論文方向細分 / 代碼 / 論文解讀 / 打包下載
本週三，CVPR官方正式開放下載，極市第一時間將所有論文（共1467篇）進行了下載打包，詳情見**此處。爲了方便大家進一步的學習，我們對這1467篇論文進行了整理，本次分享的是所有檢測類論文，並將它們細分爲3D目標檢測、人臉檢測、動作檢測、視頻目標檢測、文本檢測、行人檢測**等方向，同時附上了相關解讀和已開源論文的代碼，共計149篇，並將其打包，獲取方式見文末。
3D目標檢測
【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
代碼：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
代碼：https://github.com/tomztyang/3DSSD
本文主要從point-based的研究入手，考慮如何解決掉以前的point-based的方法的瓶頸，即時間和內存佔有遠遠大於voxel-based的方法，從而作者設計了新的SA模塊和丟棄了FP模塊到達時間上可達25FPS，此外本文采用一個anchor free Head，進一步減少時間和GPU顯存，提出了3D center-ness label的表示，進一步提高了精度。
【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
代碼：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
代碼：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
代碼：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
代碼：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 Cao
3D目標檢測是當前自動駕駛感知模塊重要的一個環節，如何平衡3D物體檢測的精度以及速度更是非常重要的一個研究話題。本文提出了一種新的基於點雲的三維物體檢測的統一網絡：混合體素網絡(HVNet)，通過在點級別上混合尺度體素特徵編碼器(VFE)得到更好的體素特徵編碼方法，從而在速度和精度上得到提升。與多種方法相比，HVNet在檢測速度上有明顯的提高。在KITTI 數據集自行車檢測的中等難度級別（moderate）中，HVNet 的準確率比PointPillars方法高出了8.44%。
【22】Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud
作者：Weijing Shi, Raj Rajkumar
代碼：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
代碼：https://github.com/mileyan/pseudo-LiDAR_e2e
人物（交互）檢測
【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
代碼：https://github.com/YueLiao/PPDM
【30】（人物檢測）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】（人體檢測）VSGNet: Spatial Attention Network for Detecting Human Object Interactions Using Graph Convolutions
作者：Oytun Ulutan, A S M Iftekhar, B. S. Manjunath
動作檢測
【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
活體檢測
【35】ZSTAD: Zero-Shot Temporal Activity Detection
作者：Lingling Zhang, Xiaojun Chang, Jun Liu, Minnan Luo, Sen Wang, Zongyuan Ge, Alexander Hauptmann
顯著性檢測
【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
代碼：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
代碼：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
僞裝/僞造檢測
【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
人臉檢測
【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】（數據集）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
小樣本/零樣本
【59】Few-Shot Object Detection With Attention-RPN and Multi-Relation Detector
作者：Qi Fan, Wei Zhuo, Chi-Keung Tang, Yu-Wing Tai
本文提出了新的少樣本目標檢測算法，創新點包括Attention-RPN、多關係檢測器以及對比訓練策略，另外還構建了包含1000類的少樣本檢測數據集FSOD，在FSOD上訓練得到的論文模型能夠直接遷移到新類別的檢測中，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
異常檢測
【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
半監督/弱監督/無監督
【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
代碼：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
密集檢測
【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
文本檢測
【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
視頻目標檢測
【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
代碼：https://github.com/yinjunbo/3DVID
【78】Combining Detection and Tracking for Human Pose Estimation in Videos
作者：Manchen Wang, Joseph Tighe, Davide Modolo
行人檢測
【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
移動目標檢測
【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
通用目標檢測/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
代碼：https://github.com/sfzhang15/ATSS
本文指出one-stage anchor-based和center-based anchor-free檢測算法間的差異主要來自於正負樣本的選擇，基於此提出ATSS(Adaptive Training Sample Selection)方法，該方法能夠自動根據GT的相關統計特徵選擇合適的anchor box作爲正樣本，在不帶來額外計算量和參數的情況下，能夠大幅提升模型的性能。
【85】（大規模/不均衡目標檢測）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】（特徵檢測）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】（陰影檢測）Instance Shadow Detection
作者：Tianyu Wang, Xiaowei Hu, Qiong Wang, Pheng-Ann Heng, Chi-Wing Fu
【94】（陰影檢測）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】（玻璃檢測）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 代碼：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
代碼：https://github.com/KiveeDong/CentripetalNet
本文提出一種使用向心偏移來對同一實例中的角點進行配對的CentripetalNet向心網絡。向心網絡可以預測角點的位置和向心偏移，並匹配移動結果對齊的角。結合位置信息，這種方法比傳統的嵌入方法更準確地匹配角點。角池將邊界框內的信息提取到邊界上。爲了使這些信息在角落裏更容易被察覺，作者又設計了一個交叉星可變形卷積網絡來適應特徵。除了檢測，通過爲作者的CentripetalNet安置一個mask預測模塊來探索anchor-free檢測器上的實例分割。
【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
代碼：https://github.com/google/automl/tree/master/efficientdet
本文系統性地研究了多種檢測器架構設計，試圖解決該問題。基於單階段檢測器範式，研究者查看了主幹網絡、特徵融合和邊界框/類別預測網絡的設計選擇，發現了兩大主要挑戰：高效的多尺度特徵融合和模型縮放。針對這兩項挑戰，研究者提出了應對方法：高效的多尺度特徵融合和模型縮放。
【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
代碼：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
代碼：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
代碼：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
代碼：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】（跨域目標檢測）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】（跨域目標檢測）Cross-domain Object Detection through Coarse-to-Fine Feature Adaptation
作者：Yangtao Zheng, Di Huang, Songtao Liu, Yunhong Wang
近年來，在基於深度學習的目標檢測中見證了巨大的進步。但是，由於domain shift問題，將現成的檢測器應用於未知的域會導致性能顯著下降。爲了解決這個問題，本文提出了一種新穎的從粗到精的特徵自適應方法來進行跨域目標檢測。由於這種從粗到細的特徵自適應，前景區域中的領域知識可以有效地傳遞。在各種跨域檢測方案中進行了廣泛的實驗，結果證明了所提出方法的廣泛適用性和有效性。
【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
代碼：https://github.com/jshilong/SEPC
爲了更好的解決物體檢測中的尺度問題，本文重新設計了經典的單階段檢測器的FPN以及HEAD結構，通過構造更具等變性的特徵金子塔，以提高檢測器應對尺度變化的魯棒性，可以使單階段檢測器在coco上提升～4mAP。
【123】（邊界檢測）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
代碼：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
代碼：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
代碼：https://github.com/Sense-X/TSD
目前很多研究表明目標檢測中的分類分支和定位分支存在較大的偏差，本文從sibling head改造入手，跳出常規的優化方向，提出TSD方法解決混合任務帶來的內在衝突，從主幹的proposal中學習不同的task-aware proposal，同時結合PC來保證TSD的性能，在COCO上達到了51.2mAP。
【139】Detecting Adversarial Samples Using Influence Functions and Nearest Neighbors
作者：Gilad Cohen, Guillermo Sapiro, Raja Giryes
【140】（特徵檢測）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】（陰影檢測）Instance Shadow Detection
作者：Tianyu Wang, Xiaowei Hu, Qiong Wang, Pheng-Ann Heng, Chi-Wing Fu
【146】（陰影檢測）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】（玻璃檢測）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
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