GMM-Based Bootstrap Prototype-Aware Learning For Weakly Supervised Semantic Segmentation

Recent advancements in weakly supervised semantic segmentation (WSSS) methods have focused on incorporating contextual knowledge to enhance the completeness of class activation maps (CAMs). Inspired by prototype learning, we introduce a novel approach that capitalizes on the diverse and fine-grained features of instances, as well as the non-discriminative features of background context. However, traditional clustering methods, commonly employed in prototype-based techniques to generate feature centroids, often lack sensitivity to noisy features. To address the limitation, that CAMs predominantly emphasize discriminative features, we propose a Gaussian Mixture Model Bootstrap Prototype Aware Learning (GMM-BPA) strategy. This enriches instance comprehension by integrating instance attributes and semantic context through a Bootstrap Prototype Aware CAM (BPACAM). The core of our method lies in capturing instance knowledge through attributes, which are learned from class support banks and background support banks modeled as Gaussian distributions. Extensive experiments conducted on two standard WSSS benchmarks, PASCAL VOC and MS COCO, demonstrate that GMM-BPA outperforms existing methods and achieves state-of-the-art performance.