Papers › Twins: Revisiting the Design of Spatial Attention in Vision Transformers
Twins: Revisiting the Design of Spatial Attention in Vision Transformers
Xiangxiang Chu, Zhi Tian, Yuqing Wang, Bo Zhang, Haibing Ren, Xiaolin Wei, Huaxia Xia, Chunhua Shen
Very recently, a variety of vision transformer architectures for dense prediction tasks have been proposed and they show that the design of spatial attention is critical to their success in these tasks. In this work, we revisit the design of the spatial attention and demonstrate that a carefully-devised yet simple spatial attention mechanism performs favourably against the state-of-the-art schemes. As a result, we propose two vision transformer architectures, namely, Twins-PCPVT and Twins-SVT. Our proposed architectures are highly-efficient and easy to implement, only involving matrix multiplications that are highly optimized in modern deep learning frameworks. More importantly, the proposed architectures achieve excellent performance on a wide range of visual tasks, including image level classification as well as dense detection and segmentation. The simplicity and strong performance suggest that our proposed architectures may serve as stronger backbones for many vision tasks. Our code is released at https://github.com/Meituan-AutoML/Twins .
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Image Classification | ImageNet | Twins-SVT-L | GFLOPs | 15.1 | #401 of 1060 | Archive leaderboard | report |
| Image Classification | ImageNet | Twins-SVT-L | Number of params | 99.2M | #401 of 1060 | Archive leaderboard | report |
| Image Classification | ImageNet | Twins-SVT-L | Top 1 Accuracy | 83.7% | #401 of 1060 | Archive leaderboard | report |
| Semantic Segmentation | ADE20K | Twins-SVT-L (UperNet, ImageNet-1k pretrain) | Validation mIoU | 50.2 | #119 of 235 | Archive leaderboard | report |
| Semantic Segmentation | ADE20K val | Twins-SVT-L (UperNet, ImageNet-1k pretrain) | mIoU | 50.2 | #52 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: Global Sub-Sampled Attention, Locally-Grouped Self-Attention, Spatially Separable Self-Attention, Twins-SVT
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