Papers › Shuffle Transformer: Rethinking Spatial Shuffle for Vision Transformer
Shuffle Transformer: Rethinking Spatial Shuffle for Vision Transformer
Zilong Huang, Youcheng Ben, Guozhong Luo, Pei Cheng, Gang Yu, Bin Fu
Very recently, Window-based Transformers, which computed self-attention within non-overlapping local windows, demonstrated promising results on image classification, semantic segmentation, and object detection. However, less study has been devoted to the cross-window connection which is the key element to improve the representation ability. In this work, we revisit the spatial shuffle as an efficient way to build connections among windows. As a result, we propose a new vision transformer, named Shuffle Transformer, which is highly efficient and easy to implement by modifying two lines of code. Furthermore, the depth-wise convolution is introduced to complement the spatial shuffle for enhancing neighbor-window connections. The proposed architectures achieve excellent performance on a wide range of visual tasks including image-level classification, object detection, and semantic segmentation. Code will be released for reproduction.
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Code
Syntology Ran 5 of 12 code samples harvested from 1 repository linked to this paper; 7 have no recorded run. Of those that ran: 1 ran · honoured contract; 1 ran · our draft was wrong; 1 ran · fixture could not drive it; 2 ran with no contract checked.
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Code Syntology ran Syntology
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Semantic Segmentation | ADE20K | UperNet Shuffle-B | Validation mIoU | 50.5 | #111 of 235 | Archive leaderboard | report |
| Semantic Segmentation | ADE20K | UperNet Shuffle-T | Validation mIoU | 47.6 | #161 of 235 | Archive leaderboard | report |
| Semantic Segmentation | ADE20K val | UperNet Shuffle-B | mIoU | 50.5 | #49 of 95 | Archive leaderboard | report |
| Semantic Segmentation | ADE20K val | UperNet Shuffle-S | mIoU | 49.6 | #57 of 95 | Archive leaderboard | report |
| Semantic Segmentation | ADE20K val | UperNet Shuffle-T | mIoU | 47.6 | #66 of 95 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
Introduced by this paper: Shuffle-T
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