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We explore the plain, non-hierarchical Vision Transformer (ViT) as a backbone network for object detection.
Backpropagation applied to handwritten zip code recognition
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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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Imagenet classification with deep convolutional neural networks
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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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Spatial pyramid pooling in deep convolutional networks for visual recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 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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Fast R-CNN
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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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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2015
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Going deeper with convolutions
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich · 2015
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Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton · 2016
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Training deep nets with sublinear memory cost
Tianqi Chen, Bing Xu, Chiyuan Zhang, and Carlos Guestrin · 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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Gaussian error linear units (GeLUs)
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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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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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Accurate, large minibatch SGD: Training ImageNet in 1 hour
Priya Goyal, Piotr Dollár, Ross Girshick, Pieter Noordhuis, Lukasz Wesolowski, Aapo Kyrola, Andrew Tulloch, Yangqing Jia, and Kaiming He · 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
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Attention is all you need
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CornerNet: Detecting objects as paired keypoints
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Improving language understanding by generative pre-training
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Group normalization
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Cascade R-CNN: high quality object detection and instance segmentation
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Dynamic head: Unifying object detection heads with attentions
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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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XCiT: Cross-covariance image Transformers
Alaaeldin El-Nouby, Hugo Touvron, Mathilde Caron, Piotr Bojanowski, Matthijs Douze, Armand Joulin, Ivan Laptev, Natalia Neverova, Gabriel Synnaeve, Jakob Verbeek, and Herve Jegou · 2021
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Multiscale Vision Transformers
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WeiFu Fu, CongChong Nie, Ting Sun, Jun Liu, TianLiang Zhang, and Yong Liu · 2021
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BERT: Pre-training of deep bidirectional Transformers for language understanding
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CenterNet: Keypoint triplets for object detection
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LVIS: A dataset for large vocabulary instance segmentation
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Scale-aware trident networks for object detection
Yanghao Li, Yuntao Chen, Naiyan Wang, and Zhaoxiang Zhang · 2019
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Decoupled weight decay regularization
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Simple copy-paste is a strong data augmentation method for instance segmentation
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Masked autoencoders are scalable vision learners
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Rethinking spatial dimensions of Vision Transformers
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MViTv2: Improved multiscale Vision Transformers for classification and detection
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Benchmarking detection transfer learning with Vision Transformers
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CBNetV2: A composite backbone network architecture for object detection
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Swin Transformer V2: Scaling up capacity and resolution
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Swin Transformer: Hierarchical ision Transformer using shifted windows
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Learning transferable visual models from natural language supervision
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MLP-mixer: An all-MLP architecture for vision
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ResMLP: Feedforward networks for image classification with data-efficient training
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Pyramid Vision Transformer: A versatile backbone for dense prediction without convolutions
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Probabilistic two-stage detection
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Detecting twenty-thousand classes using image-level supervision
Xingyi Zhou, Rohit Girdhar, Armand Joulin, Phillip Krähenbühl, and Ishan Misra · 2022
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