Weakly Supervised Semantic Segmentation (WSSS) using only image-level labels
has gained significant attention due to cost-effectiveness. Recently, Vision
Transformer (ViT) based methods without class activation map (CAM) have shown
greater capability in generating reliable pseudo labels than previous methods
using CAM. However, the current ViT-based methods utilize max pooling to select
the patch with the highest prediction score to map the patch-level
classification to the image-level one, which may affect the quality of pseudo
labels due to the inaccurate classification of the patches. In this paper, we
introduce a novel ViT-based WSSS method named top-K pooling with patch
contrastive learning (TKP-PCL), which employs a top-K pooling layer to
alleviate the limitations of previous max pooling selection. A patch
contrastive error (PCE) is also proposed to enhance the patch embeddings to
further improve the final results. The experimental results show that our
approach is very efficient and outperforms other state-of-the-art WSSS methods
on the PASCAL VOC 2012 dataset.

提出了一种名为 top-K 汇聚的 Vision Transformer 弱监督语义分割方法（TKP-PCL），通过使用 top-K 汇聚层来增强之前最大汇聚选择的限制，并提出了一种补丁对比误差（PCE）来增强补丁嵌入，从而进一步提高最终结果。实验结果表明，该方法在 PASCAL VOC 2012 数据集上非常高效，并且优于其他最新的弱监督语义分割方法。