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.

翻译：本文探讨了普通视觉Transformer（ViT）在弱监督语义分割（WSSS）中的特性。类激活图（CAM）对于理解分类网络和启动WSSS至关重要。我们观察到ViT的不同注意力头关注不同的图像区域，因此提出了一种新颖的基于权重的方法，用于端到端地估计各注意力头的重要性，同时自适应地融合自注意力图以生成具有更完整对象的高质量CAM结果。此外，我们设计了一个基于ViT的梯度裁剪解码器，用于利用CAM结果进行在线再训练以完成WSSS任务。我们将这一基于普通Transformer的弱监督学习框架命名为WeakTr。该方法在标准基准上达到了最先进的WSSS性能，即在PASCAL VOC 2012验证集上mIoU为78.4%，在COCO 2014验证集上mIoU为50.3%。代码已开源在https://github.com/hustvl/WeakTr。