  Weakly Supervised Semantic Segmentation (WSSS) using only image-level labels
has gained significant attention due to its cost-effectiveness. The typical
framework involves using image-level labels as training data to generate
pixel-level pseudo-labels with refinements. Recently, methods based on Vision
Transformers (ViT) have demonstrated superior capabilities in generating
reliable pseudo-labels, particularly in recognizing complete object regions,
compared to CNN methods. However, current ViT-based approaches have some
limitations in the use of patch embeddings, being prone to being dominated by
certain abnormal patches, as well as many multi-stage methods being
time-consuming and lengthy in training, thus lacking efficiency. Therefore, in
this paper, we introduce a novel ViT-based WSSS method named \textit{Adaptive
Patch Contrast} (APC) that significantly enhances patch embedding learning for
improved segmentation effectiveness. APC utilizes an Adaptive-K Pooling (AKP)
layer to address the limitations of previous max pooling selection methods.
Additionally, we propose a Patch Contrastive Learning (PCL) to enhance patch
embeddings, thereby further improving the final results. Furthermore, we
improve upon the existing multi-stage training framework without CAM by
transforming it into an end-to-end single-stage training approach, thereby
enhancing training efficiency. The experimental results show that our approach
is effective and efficient, outperforming other state-of-the-art WSSS methods
on the PASCAL VOC 2012 and MS COCO 2014 dataset within a shorter training
duration.
