A Weakly Supervised Semantic Segmentation Method Based on Local Superpixel Transformation

Weakly supervised semantic segmentation (WSSS) can obtain pseudo-semantic masks through a weaker level of supervised labels, reducing the need for costly pixel-level annotations. However, the general class activation map (CAM)-based pseudo-mask acquisition method suffers from sparse coverage, leading to false positive and false negative regions that reduce accuracy. We propose a WSSS method based on local superpixel transformation that combines superpixel theory and image local information. Our method uses a superpixel local consistency weighted cross-entropy loss to correct erroneous regions and a post-processing method based on the adjacent superpixel affinity matrix (ASAM) to expand false negatives, suppress false positives, and optimize semantic boundaries. Our method achieves 73.5% mIoU on the PASCAL VOC 2012 validation set, which is 2.5% higher than our baseline EPS and 73.9% on the test set, and the ASAM post-processing method is validated on several state-of-the-art methods. If our paper is accepted, our code will be published at https://github.com/JimmyMa99/SPL.