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DETR-like models have significantly boosted the performance of detectors and even outperformed classical convolutional models.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Microsoft COCO: common objects in context
Tsung-Yi Lin, Michael Maire, Serge J. Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C. Lawrence Zitnick · 2014
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 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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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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Focal loss for dense object detection
Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollár · 2020
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Dynamic detr: End-to-end object detection with dynamic attention
Xiyang Dai, Yinpeng Chen, Jianwei Yang, Pengchuan Zhang, Lu Yuan, and Lei Zhang · 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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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, Jakob Uszkoreit, and Neil Houlsby · 2021
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You only look at one sequence: Rethinking transformer in vision through object detection
Yuxin Fang, Bencheng Liao, Xinggang Wang, Jiemin Fang, Jiyang Qi, Rui Wu, Jianwei Niu, and Wenyu Liu · 2021
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Fast convergence of detr with spatially modulated co-attention
Peng Gao, Minghang Zheng, Xiaogang Wang, Jifeng Dai, and Hongsheng Li · 2021
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Spvit: Enabling faster vision transformers via soft token pruning
Zhenglun Kong, Peiyan Dong, Xiaolong Ma, Xin Meng, Wei Niu, Mengshu Sun, Bin Ren, Minghai Qin, Hao Tang, and Yanzhi Wang · 2021
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Swin transformer: Hierarchical vision transformer using shifted windows
Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo · 2021
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Conditional detr for fast training convergence
Depu Meng, Xiaokang Chen, Zejia Fan, Gang Zeng, Houqiang Li, Yuhui Yuan, Lei Sun, and Jingdong Wang · 2021
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Dynamicvit: Efficient vision transformers with dynamic token sparsification
Yongming Rao, Wenliang Zhao, Benlin Liu, Jiwen Lu, Jie Zhou, and Cho-Jui Hsieh · 2021
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Rethinking transformer-based set prediction for object detection
Zhiqing Sun, Shengcao Cao, Yiming Yang, and Kris Kitani · 2021
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Exploring plain vision transformer backbones for object detection
Yanghao Li, Hanzi Mao, Ross Girshick, and Kaiming He · 2022
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DAB-DETR: Dynamic anchor boxes are better queries for DETR
Shilong Liu, Feng Li, Hao Zhang, Xiao Yang, Xianbiao Qi, Hang Su, Jun Zhu, and Lei Zhang · 2022
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Adaptive sparse vit: Towards learnable adaptive token pruning by fully exploiting self-attention
Xiangcheng Liu, Tianyi Wu, and Guodong Guo · 2022
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Sparse DETR: Efficient end-to-end object detection with learnable sparsity
Byungseok Roh, JaeWoong Shin, Wuhyun Shin, and Saehoon Kim · 2022
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Patch slimming for efficient vision transformers
Yehui Tang, Kai Han, Yunhe Wang, Chang Xu, Jianyuan Guo, Chao Xu, and Dacheng Tao · 2022
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Pnp-detr: Towards efficient visual analysis with transformers
Tao Wang, Li Yuan, Yunpeng Chen, Jiashi Feng, and Shuicheng Yan · 2021
Cited alongside, same era.
Efficient DETR: improving end-to-end object detector with dense prior
Zhuyu Yao, Jiangbo Ai, Boxun Li, and Chi Zhang · 2021
Cited alongside, same era.
Deformable detr: Deformable transformers for end-to-end object detection
Xizhou Zhu, Weijie Su, Lewei Lu, Bin Li, Xiaogang Wang, and Jifeng Dai · 2021
Cited alongside, same era.
Group DETR: fast training convergence with decoupled one-to-many label assignment
Qiang Chen, Xiaokang Chen, Gang Zeng, and Jingdong Wang · 2022
Cited alongside, same era.
Conditional DETR V2: efficient detection transformer with box queries
Xiaokang Chen, Fangyun Wei, Gang Zeng, and Jingdong Wang · 2022
Cited alongside, same era.
Learned token pruning for transformers
Sehoon Kim, Sheng Shen, David Thorsley, Amir Gholami, Woosuk Kwon, Joseph Hassoun, and Kurt Keutzer · 2022
Cited alongside, same era.
Dn-detr: Accelerate detr training by introducing query denoising
Feng Li, Hao Zhang, Shilong Liu, Jian Guo, Lionel M Ni, and Lei Zhang · 2022
Cited alongside, same era.
Anchor detr: Query design for transformer-based detector
Yingming Wang, Xiangyu Zhang, Tong Yang, and Jian Sun · 2022
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Evo-vit: Slow-fast token evolution for dynamic vision transformer
Yifan Xu, Zhijie Zhang, Mengdan Zhang, Kekai Sheng, Ke Li, Weiming Dong, Liqing Zhang, Changsheng Xu, and Xing Sun · 2022
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A-vit: Adaptive tokens for efficient vision transformer
Hongxu Yin, Arash Vahdat, Jose M. Alvarez, Arun Mallya, Jan Kautz, and Pavlo Molchanov · 2022
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Accelerating detr convergence via semantic-aligned matching
Gongjie Zhang, Zhipeng Luo, Yingchen Yu, Kaiwen Cui, and Shijian Lu · 2022
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Dino: Detr with improved denoising anchor boxes for end-to-end object detection, 2022
Hao Zhang, Feng Li, Shilong Liu, Lei Zhang, Hang Su, Jun Zhu, Lionel M. Ni, and Heung-Yeung Shum · 2022
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detrex: Benchmarking detection transformers, 2023
Tianhe Ren, Shilong Liu, Feng Li, Hao Zhang, Ailing Zeng, Jie Yang, Xingyu Liao, Ding Jia, Hongyang Li, He Cao, Jianan Wang, Zhaoyang Zeng, Xianbiao Qi, Yuhui Yuan, Jianwei Yang, and Lei Zhang · 2023
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Towards efficient use of multi-scale features in transformer-based object detectors
Gongjie Zhang, Zhipeng Luo, Zichen Tian, Jingyi Zhang, Xiaoqin Zhang, and Shijian Lu · 2023
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