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DETR is a novel end-to-end transformer architecture object detector, which significantly outperforms classic detectors when scaling up.
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
Earlier work this paper cites.
Fitnets: Hints for thin deep nets
Adriana Romero, Nicolas Ballas, Samira Ebrahimi Kahou, Antoine Chassang, Carlo Gatta, and Yoshua Bengio · 2014
Earlier work this paper cites.
Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, Jeff Dean, et al · 2015
Earlier work this paper cites.
Faster r-cnn: Towards real-time object detection with region proposal networks
Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun · 2015
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
You only look once: Unified, real-time object detection
Joseph Redmon, Santosh Divvala, Ross Girshick, and Ali Farhadi · 2016
Earlier work this paper cites.
Paying more attention to attention: Improving the performance of convolutional neural networks via attention transfer
Sergey Zagoruyko and Nikos Komodakis · 2016
Earlier work this paper cites.
Learning efficient object detection models with knowledge distillation
Guobin Chen, Wongun Choi, Xiang Yu, Tony Han, and Manmohan Chandraker · 2017
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2017
Earlier work this paper cites.
Mimicking very efficient network for object detection
Quanquan Li, Shengying Jin, and Junjie Yan · 2017
Earlier work this paper cites.
Focal loss for dense object detection
Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollár · 2017
Earlier work this paper cites.
A gift from knowledge distillation: Fast optimization, network minimization and transfer learning
Junho Yim, Donggyu Joo, Jihoon Bae, and Junmo Kim · 2017
Earlier work this paper cites.
Cascade r-cnn: high quality object detection and instance segmentation
Zhaowei Cai and Nuno Vasconcelos · 2019
Cited alongside, same era.
A comprehensive overhaul of feature distillation
Byeongho Heo, Jeesoo Kim, Sangdoo Yun, Hyojin Park, Nojun Kwak, and Jin Young Choi · 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
Cited alongside, same era.
Distilling object detectors with fine-grained feature imitation
Tao Wang, Li Yuan, Xiaopeng Zhang, and Jiashi Feng · 2019
Cited alongside, same era.
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
Cited alongside, same era.
Cad-net: A context-aware detection network for objects in remote sensing imagery
Instance-conditional knowledge distillation for object detection
Zijian Kang, Peizhen Zhang, Xiangyu Zhang, Jian Sun, and Nanning Zheng · 2021
Later among the works it cites.
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 · 2021
Later among the works it cites.
Conditional detr for fast training convergence
Depu Meng, Xiaokang Chen, Zejia Fan, Gang Zeng, Houqiang Li, Yuhui Yuan, Lei Sun, and Jingdong Wang · 2021
Later among the works it cites.
Sparse detr: Efficient end-to-end object detection with learnable sparsity
Byungseok Roh, JaeWoong Shin, Wuhyun Shin, and Saehoon Kim · 2021
Later among the works it cites.
Rethinking transformer-based set prediction for object detection
Zhiqing Sun, Shengcao Cao, Yiming Yang, and Kris M Kitani · 2021
Later among the works it cites.
Pnp-detr: Towards efficient visual analysis with transformers
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Gongjie Zhang, Shijian Lu, and Wei Zhang · 2019
Cited alongside, same era.
End-to-end object detection with transformers
Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko · 2020
Cited alongside, same era.
Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection
Xiang Li, Wenhai Wang, Lijun Wu, Shuo Chen, Xiaolin Hu, Jun Li, Jinhui Tang, and Jian Yang · 2020
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 · 2020
Cited alongside, same era.
Fast convergence of detr with spatially modulated co-attention
Peng Gao, Minghang Zheng, Xiaogang Wang, Jifeng Dai, and Hongsheng Li · 2021
Cited alongside, same era.
Distilling object detectors via decoupled features
Jianyuan Guo, Kai Han, Yunhe Wang, Han Wu, Xinghao Chen, Chunjing Xu, and Chang Xu · 2021
Cited alongside, same era.
Dynamic detr: End-to-end object detection with dynamic attention
Xiyang Dai, Yinpeng Chen, Jianwei Yang, Pengchuan Zhang, Lu Yuan, and Lei Zhang
Cited in the paper.
Tao Wang, Li Yuan, Yunpeng Chen, Jiashi Feng, and Shuicheng Yan · 2021
Later among the works it cites.
Efficient detr: improving end-to-end object detector with dense prior
Zhuyu Yao, Jiangbo Ai, Boxun Li, and Chi Zhang · 2021
Later among the works it cites.
Dn-detr: Accelerate detr training by introducing query denoising
Feng Li, Hao Zhang, Shilong Liu, Jian Guo, Lionel M Ni, and Lei Zhang · 2022
Closest in time.
Anchor detr: Query design for transformer-based detector
Yingming Wang, Xiangyu Zhang, Tong Yang, and Jian Sun · 2022
Closest in time.
Decoupled knowledge distillation
Borui Zhao, Quan Cui, Renjie Song, Yiyu Qiu, and Jiajun Liang · 2022
Closest in time.
Detrdistill: A universal knowledge distillation framework for detr-families
Jiahao Chang, Shuo Wang, Hai-Ming Xu, Zehui Chen, Chenhongyi Yang, and Feng Zhao · 2023
Closest in time.