Weakly Supervised Semantic Segmentation (WSSS) research has explored many
directions to improve the typical pipeline CNN plus class activation maps (CAM)
plus refinements, given the image-class label as the only supervision. Though
the gap with the fully supervised methods is reduced, further abating the
spread seems unlikely within this framework. On the other hand, WSSS methods
based on Vision Transformers (ViT) have not yet explored valid alternatives to
CAM. ViT features have been shown to retain a scene layout, and object
boundaries in self-supervised learning. To confirm these findings, we prove
that the advantages of transformers in self-supervised methods are further
strengthened by Global Max Pooling (GMP), which can leverage patch features to
negotiate pixel-label probability with class probability. This work proposes a
new WSSS method dubbed ViT-PCM (ViT Patch-Class Mapping), not based on CAM. The
end-to-end presented network learns with a single optimization process, refined
shape and proper localization for segmentation masks. Our model outperforms the
state-of-the-art on baseline pseudo-masks (BPM), where we achieve $69.3\%$ mIoU
on PascalVOC 2012 $val$ set. We show that our approach has the least set of
parameters, though obtaining higher accuracy than all other approaches. In a
sentence, quantitative and qualitative results of our method reveal that
ViT-PCM is an excellent alternative to CNN-CAM based architectures.

该论文提出了一种新的基于 Vision Transformers (ViT) 的弱监督语义分割方法，命名为 ViT-PCM，该方法不使用传统的 CNN-CAM。在数据集 PascalVOC2012 上实验结果表明，该方法在参数数量最少的情况下具有最好的性能和精度。

基于视觉 Transformer 的最大池化方法调和弱监督下的语义分割中的类别和形状

Max Pooling with Vision Transformers reconciles class and shape in  weakly supervised semantic segmentation

We consider the problem of distance metric learning (DML), where the task is
to learn an effective similarity measure between images. We revisit ProxyNCA
and incorporate several enhancements. We find that low temperature scaling is a
performance-critical component and explain why it works. Besides, we also
discover that Global Max Pooling works better in general when compared to
Global Average Pooling. Additionally, our proposed fast moving proxies also
addresses small gradient issue of proxies, and this component synergizes well
with low temperature scaling and Global Max Pooling. Our enhanced model, called
ProxyNCA++, achieves a 22.9 percentage point average improvement of Recall@1
across four different zero-shot retrieval datasets compared to the original
ProxyNCA algorithm. Furthermore, we achieve state-of-the-art results on the
CUB200, Cars196, Sop, and InShop datasets, achieving Recall@1 scores of 72.2,
90.1, 81.4, and 90.9, respectively.

通过对 ProxyNCA 进行改进和优化，我们提出了 ProxyNCA++ 这一性能更卓越的模型，并在零样本检索数据集上实现了 22.9% 的平均召回率提升，并在 CUB200、Cars196、Sop 和 InShop 数据集上取得了最佳结果。