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We present a High-Resolution Transformer (HRFormer) that learns high-resolution representations for dense prediction tasks, in contrast to the original Vision Transformer that produces low-resolution representations and has high memory and computational cost.
Microsoft coco: Common objects in context
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Simple baselines for human pose estimation and tracking
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Ocnet: Object context network for scene parsing
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Interlaced sparse self-attention for semantic segmentation
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Stand-alone self-attention in vision models
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Deep high-resolution representation learning for human pose estimation
Ke Sun, Bin Xiao, Dong Liu, and Jingdong Wang · 2019
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Deep high-resolution representation learning for visual recognition
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Object-contextual representations for semantic segmentation
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Cutmix: Regularization strategy to train strong classifiers with localizable features
Sangdoo Yun, Dongyoon Han, Seong Joon Oh, Sanghyuk Chun, Junsuk Choe, and Youngjoon Yoo · 2019
Multiscale vision transformers
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Transformer in transformer
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Token labeling: Training a 85.5% top-1 accuracy vision transformer with 56m parameters on imagenet
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Pose recognition with cascade transformers
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Openmmlab pose estimation toolbox and benchmark
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Yanjie Li, Shoukui Zhang, Zhicheng Wang, Sen Yang, Wankou Yang, Shu-Tao Xia, and Erjin Zhou · 2021
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Cvt: Introducing convolutions to vision transformers
Haiping Wu, Bin Xiao, Noel Codella, Mengchen Liu, Xiyang Dai, Lu Yuan, and Lei Zhang · 2021
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Early convolutions help transformers see better
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Incorporating convolution designs into visual transformers
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Tokens-to-token vit: Training vision transformers from scratch on imagenet
Li Yuan, Yunpeng Chen, Tao Wang, Weihao Yu, Yujun Shi, Francis EH Tay, Jiashi Feng, and Shuicheng Yan · 2021
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Multi-scale vision longformer: A new vision transformer for high-resolution image encoding
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Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers
Sixiao Zheng, Jiachen Lu, Hengshuang Zhao, Xiatian Zhu, Zekun Luo, Yabiao Wang, Yanwei Fu, Jianfeng Feng, Tao Xiang, Philip H.S. Torr, and Li Zhang · 2021
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