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Recent self-supervised pretraining methods for object detection largely focus on pretraining the backbone of the object detector, neglecting key parts of detection architecture.
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Bogdan Alexe, Thomas Deselaers, and Vittorio Ferrari · 2010
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The pascal visual object classes (voc) challenge
Mark Everingham, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman · 2010
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Rectified linear units improve restricted boltzmann machines
Vinod Nair and Geoffrey E Hinton · 2010
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Cpmc: Automatic object segmentation using constrained parametric min-cuts
Joao Carreira and Cristian Sminchisescu · 2011
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Category-independent object proposals with diverse ranking
Ian Endres and Derek Hoiem · 2013
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Deep inside convolutional networks: Visualising image classification models and saliency maps
Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman · 2013
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Selective search for object recognition
Jasper RR Uijlings, Koen EA Van De Sande, Theo Gevers, and Arnold WM Smeulders · 2013
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Multiscale combinatorial grouping
Pablo Arbeláez, Jordi Pont-Tuset, Jonathan T Barron, Ferran Marques, and Jitendra Malik · 2014
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Ming-Ming Cheng, Niloy J Mitra, Xiaolei Huang, Philip HS Torr, and Shi-Min Hu · 2014
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Bing: Binarized normed gradients for objectness estimation at 300fps
Ming-Ming Cheng, Ziming Zhang, Wen-Yan Lin, and Philip Torr · 2014
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Rich feature hierarchies for accurate object detection and semantic segmentation
Ross Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik · 2014
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How good are detection proposals, really?
Jan Hosang, Rodrigo Benenson, and Bernt Schiele · 2014
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Geodesic object proposals
Philipp Krähenbühl and Vladlen Koltun · 2014
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Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick · 2014
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Edge boxes: Locating object proposals from edges
C Lawrence Zitnick and Piotr Dollár · 2014
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Fast r-cnn
Ross Girshick · 2015
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What makes for effective detection proposals?
Jan Hosang, Rodrigo Benenson, Piotr Dollár, and Bernt Schiele · 2015
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Faster r-cnn: Towards real-time object detection with region proposal networks
Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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iprivacy: image privacy protection by identifying sensitive objects via deep multi-task learning
Jun Yu, Baopeng Zhang, Zhengzhong Kuang, Dan Lin, and Jianping Fan · 2016
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Focal loss for dense object detection
Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollár · 2017
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Yolo9000: better, faster, stronger
Joseph Redmon and Ali Farhadi · 2017
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Lstd: A low-shot transfer detector for object detection
Hao Chen, Yali Wang, Guoyou Wang, and Yu Qiao · 2018
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Robert Geirhos, Patricia Rubisch, Claudio Michaelis, Matthias Bethge, Felix A Wichmann, and Wieland Brendel · 2018
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Unsupervised feature learning via non-parametric instance discrimination
Zhirong Wu, Yuanjun Xiong, Stella X Yu, and Dahua Lin · 2018
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Scaling and benchmarking self-supervised visual representation learning
Priya Goyal, Dhruv Mahajan, Abhinav Gupta, and Ishan Misra · 2019
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Rethinking imagenet pre-training
A simple semi-supervised learning framework for object detection
Kihyuk Sohn, Zizhao Zhang, Chun-Liang Li, Han Zhang, Chen-Yu Lee, and Tomas Pfister · 2020
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Frustratingly simple few-shot object detection
Xin Wang, Thomas E Huang, Trevor Darrell, Joseph E Gonzalez, and Fisher Yu · 2020
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Dense contrastive learning for self-supervised visual pre-training
Xinlong Wang, Rufeng Zhang, Chunhua Shen, Tao Kong, and Lei Li · 2020
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Multi-scale positive sample refinement for few-shot object detection
Jiaxi Wu, Songtao Liu, Di Huang, and Yunhong Wang · 2020
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Meta-rcnn: Meta learning for few-shot object detection
Xiongwei Wu, Doyen Sahoo, and Steven Hoi · 2020
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Kaiming He, Ross Girshick, and Piotr Dollár · 2019
Cited alongside, same era.
Consistency-based semi-supervised learning for object detection
Jisoo Jeong, Seungeui Lee, Jeesoo Kim, and Nojun Kwak · 2019
Cited alongside, same era.
Few-shot object detection via feature reweighting
Bingyi Kang, Zhuang Liu, Xin Wang, Fisher Yu, Jiashi Feng, and Trevor Darrell · 2019
Cited alongside, same era.
Repmet: Representative-based metric learning for classification and few-shot object detection
Leonid Karlinsky, Joseph Shtok, Sivan Harary, Eli Schwartz, Amit Aides, Rogerio Feris, Raja Giryes, and Alex M Bronstein · 2019
Cited alongside, same era.
Generalized intersection over union: A metric and a loss for bounding box regression
Hamid Rezatofighi, Nathan Tsoi, JunYoung Gwak, Amir Sadeghian, Ian Reid, and Silvio Savarese · 2019
Cited alongside, same era.
Meta-learning to detect rare objects
Yu-Xiong Wang, Deva Ramanan, and Martial Hebert · 2019
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Meta r-cnn: Towards general solver for instance-level low-shot learning
Xiaopeng Yan, Ziliang Chen, Anni Xu, Xiaoxi Wang, Xiaodan Liang, and Liang Lin · 2019
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Few-shot object detection and viewpoint estimation for objects in the wild
Yang Xiao and Renaud Marlet · 2020
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Cheaper pre-training lunch: An efficient paradigm for object detection
Dongzhan Zhou, Xinchi Zhou, Hongwen Zhang, Shuai Yi, and Wanli Ouyang · 2020
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Deformable DETR: Deformable transformers for end-to-end object detection
Xizhou Zhu, Weijie Su, Lewei Lu, Bin Li, Xiaogang Wang, and Jifeng Dai · 2020
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https://www.kaggle.com/c/airbus-ship-detection
Airbus. airbus ship detection challenge · 2021
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Emerging properties in self-supervised vision transformers
Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin · 2021
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Should i look at the head or the tail? dual-awareness attention for few-shot object detection
Tung-I Chen, Yueh-Cheng Liu, Hung-Ting Su, Yu-Cheng Chang, Yu-Hsiang Lin, Jia-Fong Yeh, and Winston H Hsu · 2021
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UP-DETR: Unsupervised pre-training for object detection with transformers
Zhigang Dai, Bolun Cai, Yugeng Lin, and Junying Chen · 2021
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Generalized few-shot object detection without forgetting
Zhibo Fan, Yuchen Ma, Zeming Li, and Jian Sun · 2021
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Efficient visual pretraining with contrastive detection
Olivier J Hénaff, Skanda Koppula, Jean-Baptiste Alayrac, Aaron van den Oord, Oriol Vinyals, and João Carreira · 2021
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Transformation invariant few-shot object detection
Aoxue Li and Zhenguo Li · 2021
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Unbiased teacher for semi-supervised object detection
Yen-Cheng Liu, Chih-Yao Ma, Zijian He, Chia-Wen Kuo, Kan Chen, Peizhao Zhang, Bichen Wu, Zsolt Kira, and Peter Vajda · 2021
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Addressing the exorbitant cost of labeling medical images with active learning
Saba Rahimi, Ozan Oktay, Javier Alvarez-Valle, and Sujeeth Bharadwaj · 2021
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Self-supervised pretraining improves self-supervised pretraining
Colorado J Reed, Xiangyu Yue, Ani Nrusimha, Sayna Ebrahimi, Vivek Vijaykumar, Richard Mao, Bo Li, Shanghang Zhang, Devin Guillory, Sean Metzger, et al · 2021
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Spatially consistent representation learning
Byungseok Roh, Wuhyun Shin, Ildoo Kim, and Sungwoong Kim · 2021
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Region similarity representation learning
Tete Xiao, Colorado J Reed, Xiaolong Wang, Kurt Keutzer, and Trevor Darrell · 2021
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Detco: Unsupervised contrastive learning for object detection
Enze Xie, Jian Ding, Wenhai Wang, Xiaohang Zhan, Hang Xu, Zhenguo Li, and Ping Luo · 2021
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End-to-end semi-supervised object detection with soft teacher
Mengde Xu, Zheng Zhang, Han Hu, Jianfeng Wang, Lijuan Wang, Fangyun Wei, Xiang Bai, and Zicheng Liu · 2021
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Instance localization for self-supervised detection pretraining
Ceyuan Yang, Zhirong Wu, Bolei Zhou, and Stephen Lin · 2021
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Meta-detr: Few-shot object detection via unified image-level meta-learning
Gongjie Zhang, Zhipeng Luo, Kaiwen Cui, and Shijian Lu · 2021
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