本篇文章给大家谈谈rena christian church，以及renachris的知识点，希望对各位有所帮助，不要忘了收藏本站喔。
文章详情介绍：
英语“老顾客”说成Old customer？老外：你才old，全家都old
小编有个朋友是外贸业务员，
刚入行时，
朋友一口蹩脚的英语吃了不少亏：
别人总能接到单，
而他电脑对面的顾客
总是聊着聊着就不见了。
后来慢慢才知道，
原来很多都是当年中式英语挖的坑…
这哥们分享过一个好笑的故事：
有一次，
他约了个下过单的美国客户去参观工厂，
却在介绍的时候把对方气得差点发飙。
发生了什么？
原来他给主管介绍的时候把客户叫成了
Old customer！
客户当场就问：
I’m sorry. Do I look old?
That hurts！
尴尬的空气，
瞬间包围了正在交谈的三个人。。
最终客户回去下了一个小单，
并顺带在邮件里告诉他“老主顾”的正确说法，
想想是真的很介意了。
所以old这个字眼，真的杀伤力巨大！
西方人往往比亚洲人更显成熟，
但说实在的不管哪个国家，什么年纪的人，
都不喜欢被人家说老..
一个采访歪果仁的视频中，
一对外国情侣让采访人员猜她们的年龄，
听到答案后，
小姐姐瞬间失去了笑容
。。。。
顺便跟大家说一下，在美国，
最好也不要随意询问女士的年龄，
这个可是被认为非常讨嫌的行为。
1
老顾客用英语怎么说？
老顾客嘛，
这个老不是说人家老，
而是说经常来光顾，
千万不要说old customer。
old 表示上了年纪的，
所以如果你叫对方old customer，
就是说对方是上了年纪的顾客，
或者说对方长得老气，
就不能怪人家下一分钟亮刀了。
那种心情你们懂的，
就跟我们这些永远十八岁的小仙女，
在路上被带小孩子的家长指着叫阿姨一样。
心情久久难以平复
那常常关顾的顾客
“老主顾”应该怎么说呢？
来！记好了！
Regular customer
“老顾客，老主顾”
（regular/ˈreɡ.jə.lɚ/频繁的，常来的）
eg.
I’m a regular customer here.
我是这儿的老主顾了。
同理，表示经常参加某种活动的人
用regular+称谓来表示
老读者：regular reader
老用户：regular user
常去礼拜的人：regular churchgoer
eg.
Hewas a regular attender at the opera.
他是剧院的常客。
2
老员工用英语怎么说？
老员工是经验丰富的员工，
同样不能用old。
员工：employee/staff
老员工可以用这3个：
Senior staff
experienced staff
veteran staff
Experienced staff
英[ɪkˈspɪəriənst]
experienced：
表示有丰富阅历的，有经验的，老练的
Veteran staff
美 [ˈvetərən]
veteran 表示老手，或者富有经验的人
Senior staff
英[ˈsiːniə(r) stɑːf]
Senior表示高级的，资深的，
很多高级职位前面常带用Senior。
Junior 则表示相对资历比较浅的
级别比较低的，初级的。
Senior staff可以表示资深员工，
也就是老员工。
老员工 = Senior Staff
新员工 = Junior Staff
eg.
William is asking senior staff to
bring juniors to client meetings.
William要求资深职员带上
新员工参加客户会议。
2
老司机用英语怎么说？
都知道不能用old了，
ok。
“老司机”
这里指的是车龄长，车技棒，
路面经验丰富的司机，
所以咱们管这种老司机叫做：
Veteran driver
Experienced driver
如果是情场上的老司机们，
翻译最好可以体现这类人的特点，
比如或者懂异性的心思，
在某些方面富有经验，
多个异性朋友间相处游刃有余等。
1. old hand
这个就是老手最直白的翻译了，
指在某方面富有经验的人。
eg.
Frank is an old hand at
dealing with girls.
弗兰克在处理女孩子关系方面是老手了。
2. old timer
这个词是老资格，老前辈的意思，
也即经验丰富的人，老司机。
eg.
I believe that every old timer
can solve this problem easily.
我相信任何一个老司机
都可以轻易地解决这个问题。
-END-
@今日作业
# Rena是这家公司的老员工了#
用来Senior Staff 翻译下这个句子
CVPR2020 检测类论文最全：136篇论文细分_代码_论文解读_下载
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）进行了下载打包，详情见此处。为了方便大家进一步的学习，我们对这1467篇论文进行了整理，本次分享的是所有检测类论文，并将它们细分为3D目标检测、人脸检测、动作检测、视频目标检测、文本检测、行人检测等方向，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目标检测
【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，考虑如何解决掉以前的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模块和丢弃了FP模块到达时间上可达25FPS，此外本文采用一个anchor free Head，进一步减少时间和GPU显存，提出了3D center-ness label的表示，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 Cao
3D目标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非常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：混合体素网络(HVNet)，通过在点级别上混合尺度体素特征编码器(VFE)得到更好的体素特征编码方法，从而在速度和精度上得到提升。与多种方法相比，HVNet在检测速度上有明显的提高。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
动作检测
【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标检测算法，创新点包括Attention-RPN、多关系检测器以及对比训练策略，另外还构建了包含1000类的少样本检测数据集FSOD，在FSOD上训练得到的论文模型能够直接迁移到新类别的检测中，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
半监督/弱监督/无监督
【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
密集检测
【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
视频目标检测
【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
移动目标检测
【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
通用目标检测/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，该方法能够自动根据GT的相关统计特征选择合适的anchor box作为正样本，在不带来额外计算量和参数的情况下，能够大幅提升模型的性能。
【85】（大规模/不均衡目标检测）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进行配对的CentripetalNet向心网络。向心网络可以预测角点的位置和向心偏移，并匹配移动结果对齐的角。结合位置信息，这种方法比传统的嵌入方法更准确地匹配角点。角池将边界框内的信息提取到边界上。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叉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。基于单阶段检测器范式，研究者查看了主干网络、特征融合和边界框/类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】（跨域目标检测）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】（跨域目标检测）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标检测中见证了巨大的进步。但是，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。为了解决这个问题，本文提出了一种新颖的从粗到精的特征自适应方法来进行跨域目标检测。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传递。在各种跨域检测方案中进行了广泛的实验，结果证明了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，通过构造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】（边界检测）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
目前很多研究表明目标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改造入手，跳出常规的优化方向，提出TSD方法解决混合任务带来的内在冲突，从主干的proposal中学习不同的task-aware proposal，同时结合PC来保证TSD的性能，在COCO上达到了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
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