Papers › SegViT: Semantic Segmentation with Plain Vision Transformers
SegViT: Semantic Segmentation with Plain Vision Transformers
BoWen Zhang, Zhi Tian, Quan Tang, Xiangxiang Chu, Xiaolin Wei, Chunhua Shen, Yifan Liu
We explore the capability of plain Vision Transformers (ViTs) for semantic segmentation and propose the SegVit. Previous ViT-based segmentation networks usually learn a pixel-level representation from the output of the ViT. Differently, we make use of the fundamental component -- attention mechanism, to generate masks for semantic segmentation. Specifically, we propose the Attention-to-Mask (ATM) module, in which the similarity maps between a set of learnable class tokens and the spatial feature maps are transferred to the segmentation masks. Experiments show that our proposed SegVit using the ATM module outperforms its counterparts using the plain ViT backbone on the ADE20K dataset and achieves new state-of-the-art performance on COCO-Stuff-10K and PASCAL-Context datasets. Furthermore, to reduce the computational cost of the ViT backbone, we propose query-based down-sampling (QD) and query-based up-sampling (QU) to build a Shrunk structure. With the proposed Shrunk structure, the model can save up to 40% computations while maintaining competitive performance.
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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 val | SegViT ViT-Large | mIoU | 55.2 | #31 of 95 | Archive leaderboard | report |
| Semantic Segmentation | COCO-Stuff test | SegViT (ours) | mIoU | 50.3% | #6 of 21 | Archive leaderboard | report |
| Semantic Segmentation | PASCAL Context | SegViT (ours) | mIoU | 65.3 | #8 of 66 | Archive leaderboard | report |
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