Adaptive Patch Contrast for Weakly Supervised Semantic Segmentation

TL;DR

This paper introduces Adaptive Patch Contrast, a ViT-based method for weakly supervised semantic segmentation that improves patch embedding learning and training efficiency, outperforming state-of-the-art methods on standard datasets.

Contribution

The paper proposes a novel ViT-based WSSS method with adaptive pooling and contrastive learning, transforming multi-stage training into an efficient single-stage framework.

Findings

Outperforms state-of-the-art on PASCAL VOC 2012

Achieves better segmentation accuracy with less training time

Enhances patch embedding quality through contrastive learning

Abstract

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.