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The recently proposed Detection Transformer (DETR) model successfully applies Transformer to objects detection and achieves comparable performance with two-stage object detection frameworks, such as Faster-RCNN.
Long short-term memory
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Microsoft coco: Common objects in context
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Fast r-cnn
Ross Girshick · 2015
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Region-based convolutional networks for accurate object detection and segmentation
Ross Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik · 2015
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Draw: A recurrent neural network for image generation
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Show, attend and tell: Neural image caption generation with visual attention
Kelvin Xu, Jimmy Ba, Ryan Kiros, Kyunghyun Cho, Aaron Courville, Ruslan Salakhudinov, Rich Zemel, and Yoshua Bengio · 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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Dynamic filter networks
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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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Faster r-cnn: Towards real-time object detection with region proposal networks
Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun · 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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Mask r-cnn
Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross Girshick · 2017
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 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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End-to-end instance segmentation with recurrent attention
Mengye Ren and Richard S Zemel · 2017
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Recurrent neural networks for semantic instance segmentation
Amaia Salvador, Miriam Bellver, Victor Campos, Manel Baradad, Ferran Marques, Jordi Torres, and Xavier Giro-i Nieto · 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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Bert: Pre-training of deep bidirectional transformers for language understanding
Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever · 2019
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Fcos: Fully convolutional one-stage object detection
Zhi Tian, Chunhua Shen, Hao Chen, and Tong He · 2019
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Pay less attention with lightweight and dynamic convolutions
Felix Wu, Angela Fan, Alexei Baevski, Yann N Dauphin, and Michael Auli · 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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Longformer: The long-document transformer
Iz Beltagy, Matthew E Peters, and Arman Cohan · 2020
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Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
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Question-guided hybrid convolution for visual question answering
Peng Gao, Hongsheng Li, Shuang Li, Pan Lu, Yikang Li, Steven CH Hoi, and Xiaogang Wang · 2018
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Squeeze-and-excitation networks
Jie Hu, Li Shen, and Gang Sun · 2018
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Parallel feature pyramid network for object detection
Seung-Wook Kim, Hyong-Keun Kook, Jee-Young Sun, Mun-Cheon Kang, and Sung-Jea Ko · 2018
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Fixing weight decay regularization in adam
Ilya Loshchilov and Frank Hutter · 2018
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Improving language understanding by generative pre-training, 2018
Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever · 2018
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Dynamic fusion with intra-and inter-modality attention flow for visual question answering
Peng Gao, Zhengkai Jiang, Haoxuan You, Pan Lu, Steven CH Hoi, Xiaogang Wang, and Hongsheng Li · 2019
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Tom B Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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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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Up-detr: Unsupervised pre-training for object detection with transformers
Zhigang Dai, Bolun Cai, Yugeng Lin, and Junying Chen · 2020
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2020
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Transformers are rnns: Fast autoregressive transformers with linear attention
Angelos Katharopoulos, Apoorv Vyas, Nikolaos Pappas, and François Fleuret · 2020
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Reformer: The efficient transformer
Nikita Kitaev, Łukasz Kaiser, and Anselm Levskaya · 2020
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Rethinking transformer-based set prediction for object detection
Zhiqing Sun, Shengcao Cao, Yiming Yang, and Kris Kitani · 2020
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Linformer: Self-attention with linear complexity
Sinong Wang, Belinda Li, Madian Khabsa, Han Fang, and Hao Ma · 2020
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End-to-end object detection with adaptive clustering transformer
Minghang Zheng, Peng Gao, Xiaogang Wang, Hongsheng Li, and Hao Dong · 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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