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End-to-end Object Detection with Transformer (DETR)proposes to perform object detection with Transformer and achieve comparable performance with two-stage object detection like Faster-RCNN.
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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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Bernardino Romera-Paredes and Philip Hilaire Sean Torr · 2016
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End-to-end people detection in crowded scenes
Russell Stewart, Mykhaylo Andriluka, and Andrew Y Ng · 2016
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Soft-nms–improving object detection with one line of code
Navaneeth Bodla, Bharat Singh, Rama Chellappa, and Larry S Davis · 2017
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Video object detection with locally-weighted deformable neighbors
Zhengkai Jiang, Peng Gao, Chaoxu Guo, Qian Zhang, Shiming Xiang, and Chunhong Pan · 2019
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Deep modular co-attention networks for visual question answering
Zhou Yu, Jun Yu, Yuhao Cui, Dacheng Tao, and Qi Tian · 2019
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Be your own teacher: Improve the performance of convolutional neural networks via self distillation
Linfeng Zhang, Jiebo Song, Anni Gao, Jingwei Chen, Chenglong Bao, and Kaisheng Ma · 2019
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Asymmetric non-local neural networks for semantic segmentation
Zhen Zhu, Mengde Xu, Song Bai, Tengteng Huang, and Xiang Bai · 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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Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 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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