Papers › WeakTr: Exploring Plain Vision Transformer for Weakly-supervised Semantic Segmentation
WeakTr: Exploring Plain Vision Transformer for Weakly-supervised Semantic Segmentation
Lianghui Zhu, Yingyue Li, Jiemin Fang, Yan Liu, Hao Xin, Wenyu Liu, Xinggang Wang
This paper explores the properties of the plain Vision Transformer (ViT) for Weakly-supervised Semantic Segmentation (WSSS). The class activation map (CAM) is of critical importance for understanding a classification network and launching WSSS. We observe that different attention heads of ViT focus on different image areas. Thus a novel weight-based method is proposed to end-to-end estimate the importance of attention heads, while the self-attention maps are adaptively fused for high-quality CAM results that tend to have more complete objects. Besides, we propose a ViT-based gradient clipping decoder for online retraining with the CAM results to complete the WSSS task. We name this plain Transformer-based Weakly-supervised learning framework WeakTr. It achieves the state-of-the-art WSSS performance on standard benchmarks, i.e., 78.4% mIoU on the val set of PASCAL VOC 2012 and 50.3% mIoU on the val set of COCO 2014. Code is available at https://github.com/hustvl/WeakTr.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Weakly-Supervised Semantic Segmentation | COCO 2014 val | WeakTr (ViT-S, multi-stage) | mIoU | 50.3 | #7 of 39 | Archive leaderboard | report |
| Weakly-Supervised Semantic Segmentation | COCO 2014 val | WeakTr (DeiT-S, multi-stage) | mIoU | 46.9 | #9 of 39 | Archive leaderboard | report |
| Weakly-Supervised Semantic Segmentation | PASCAL VOC 2012 test | WeakTr (ViT-S, multi-stage) | Mean IoU | 79.0 | #4 of 60 | Archive leaderboard | report |
| Weakly-Supervised Semantic Segmentation | PASCAL VOC 2012 test | WeakTr (DeiT-S, multi-stage) | Mean IoU | 74.1 | #12 of 60 | Archive leaderboard | report |
| Weakly-Supervised Semantic Segmentation | PASCAL VOC 2012 train | WeakTr (DeiT-S, single-stage) | Mean IoU | 76.5 | #2 of 4 | Archive leaderboard | report |
| Weakly-Supervised Semantic Segmentation | PASCAL VOC 2012 val | WeakTr (ViT-S, multi-stage) | Mean IoU | 78.4 | #5 of 73 | Archive leaderboard | report |
| Weakly-Supervised Semantic Segmentation | PASCAL VOC 2012 val | WeakTr (DeiT-S, multi-stage) | Mean IoU | 74.0 | #13 of 73 | 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
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