Equilibrium and integrity correction class activation map in weakly supervised semantic segmentation

Semantic segmentation is a fundamental problem in computer vision, aiming to assign predefined class labels to each pixel in an image. In order to reduce the annotation cost of semantic segmentation tasks, weakly supervised semantic segmentation has attracted increasing attention. Weakly Supervised Semantic Segmentation approaches commonly leverage class activation maps derived from classification networks. However, these classification networks primarily focus on regions that contribute the most to the classification task, resulting in imbalanced activation degrees and incomplete activation regions in the generated maps. To address these limitations, this paper proposes the Equilibrium and Integrity Correction Class Activation Map (EIC-CAM) algorithm. It introduces a Gaussian suppression strategy to enhance under-activated regions while suppressing excessively activated regions, thus overall enhancing the activation levels of target objects. Moreover, a category mining strategy based on pixel semantic correlation is employed to exploit the inter-pixel relationships and uncover more complete target regions. By implementing these two strategies, stronger activation maps with more complete regions are generated, leading to higher mIoU scores. Extensive experiments conducted on the Pascal VOC 2012 segmentation dataset demonstrate that, compared to several state-of-the-art methods, our proposed approach achieves a significant improvement in accuracy when utilizing only image-level annotations for training.