Abstract Weakly-Supervised Semantic Segmentation (WSSS) using image-level labels
typically utilizes Class Activation Map (CAM) to generate the pseudo labels. Limited by the …

The existing weakly supervised semantic segmentation (WSSS) methods pay much
attention to generating accurate and complete class activation maps (CAMs) as pseudo …

Visual segmentation seeks to partition images, video frames, or point clouds into multiple
segments or groups. This technique has numerous real-world applications, such as …

Weakly supervised semantic segmentation is typically inspired by class activation maps,
which serve as pseudo masks with class-discriminative regions highlighted. Although …

Seed area generation is usually the starting point of weakly supervised semantic
segmentation (WSSS). Computing the Class Activation Map (CAM) from a multi-label …

Weakly supervised semantic segmentation (WSSS) with image-level labels aims to achieve
segmentation tasks without dense annotations. However attributed to the frequent coupling …

This work aims to leverage pre-trained foundation models, such as contrastive language-
image pre-training (CLIP) and segment anything model (SAM), to address weakly …

This paper explores the properties of the plain Vision Transformer (ViT) for Weakly-
supervised Semantic Segmentation (WSSS). The class activation map (CAM) is of critical …

Abstract Weakly Supervised Semantic Segmentation (WSSS) aims to learn the concept of
segmentation using image-level class labels. Recent WSSS works have shown promising …

Abstract Weakly Supervised Semantic Segmentation (WSSS) relies on Class Activation
Maps (CAMs) to extract spatial information from image-level labels. With the success of …