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DETR is a recently proposed Transformer-based method which views object detection as a set prediction problem and achieves state-of-the-art performance but demands extra-long training time to converge.
Learning phrase representations using rnn encoder-decoder for statistical machine translation
Kyunghyun Cho, Bart Van Merriënboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio · 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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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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Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
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Fast r-cnn
Ross Girshick · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 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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Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Ssd: Single shot multibox detector
Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu, and Alexander C Berg · 2016
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You only look once: Unified, real-time object detection
Joseph Redmon, Santosh Divvala, Ross Girshick, and Ali Farhadi · 2016
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Deformable convolutional networks
Jifeng Dai, Haozhi Qi, Yuwen Xiong, Yi Li, Guodong Zhang, Han Hu, and Yichen Wei · 2017
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Mask r-cnn
Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross Girshick · 2017
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Feature pyramid networks for object detection
Tsung-Yi Lin, Piotr Dollár, Ross Girshick, Kaiming He, Bharath Hariharan, and Serge Belongie · 2017
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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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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2017
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Yolo9000: better, faster, stronger
Joseph Redmon and Ali Farhadi · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Cascade r-cnn: Delving into high quality object detection
Zhaowei Cai and Nuno Vasconcelos · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
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Relation networks for object detection
Han Hu, Jiayuan Gu, Zheng Zhang, Jifeng Dai, and Yichen Wei · 2018
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Improving object localization with fitness nms and bounded iou loss
Lachlan Tychsen-Smith and Lars Petersson · 2018
Reppoints: Point set representation for object detection
Ze Yang, Shaohui Liu, Han Hu, Liwei Wang, and Stephen Lin · 2019
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Freeanchor: Learning to match anchors for visual object detection
Xiaosong Zhang, Fang Wan, Chang Liu, Rongrong Ji, and Qixiang Ye · 2019
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Feature selective anchor-free module for single-shot object detection
Chenchen Zhu, Yihui He, and Marios Savvides · 2019
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Deformable convnets v2: More deformable, better results
Xizhou Zhu, Han Hu, Stephen Lin, and Jifeng Dai · 2019
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End-to-end object detection with transformers
Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko · 2020
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Cited alongside, same era.
Group normalization
Yuxin Wu and Kaiming He · 2018
Cited alongside, same era.
Revisiting feature alignment for one-stage object detection
Yuntao Chen, Chenxia Han, Naiyan Wang, and Zhaoxiang Zhang · 2019
Cited alongside, same era.
Generating long sequences with sparse transformers
Rewon Child, Scott Gray, Alec Radford, and Ilya Sutskever · 2019
Cited alongside, same era.
Adaptively sparse transformers
Gonçalo M Correia, Vlad Niculae, and André FT Martins · 2019
Cited alongside, same era.
Scale-aware trident networks for object detection
Yanghao Li, Yuntao Chen, Naiyan Wang, and Zhaoxiang Zhang · 2019
Cited alongside, same era.
Libra r-cnn: Towards balanced learning for object detection
Jiangmiao Pang, Kai Chen, Jianping Shi, Huajun Feng, Wanli Ouyang, and Dahua Lin · 2019
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Cheng Chi, Fangyun Wei, and Han Hu · 2020
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Up-detr: Unsupervised pre-training for object detection with transformers
Zhigang Dai, Bolun Cai, Yugeng Lin, and Junying Chen · 2020
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Convbert: Improving bert with span-based dynamic convolution
Zi-Hang Jiang, Weihao Yu, Daquan Zhou, Yunpeng Chen, Jiashi Feng, and Shuicheng Yan · 2020
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Multiple anchor learning for visual object detection
Wei Ke, Tianliang Zhang, Zeyi Huang, Qixiang Ye, Jianzhuang Liu, and Dong Huang · 2020
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Foveabox: Beyound anchor-based object detection
Tao Kong, Fuchun Sun, Huaping Liu, Yuning Jiang, Lei Li, and Jianbo Shi · 2020
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Revisiting the sibling head in object detector
Guanglu Song, Yu Liu, and Xiaogang Wang · 2020
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Big bird: Transformers for longer sequences
Manzil Zaheer, Guru Guruganesh, Kumar Avinava Dubey, Joshua Ainslie, Chris Alberti, Santiago Ontanon, Philip Pham, Anirudh Ravula, Qifan Wang, Li Yang, et al · 2020
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Dynamic r-cnn: Towards high quality object detection via dynamic training
Hongkai Zhang, Hong Chang, Bingpeng Ma, Naiyan Wang, and Xilin Chen · 2020
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Bridging the gap between anchor-based and anchor-free detection via adaptive training sample selection
Shifeng Zhang, Cheng Chi, Yongqiang Yao, Zhen Lei, and Stan Z Li · 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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