The rapid development of deep learning has made a great progress in image segmentation,
one of the fundamental tasks of computer vision. However, the current segmentation …

Weakly supervised semantic segmentation (WSSS) with image-level labels is a challenging
task. Mainstream approaches follow a multi-stage framework and suffer from high training …

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 …

Weakly-supervised semantic segmentation aims to reduce labeling costs by training
semantic segmentation models using weak supervision, such as image-level class labels …

Efficiently training accurate deep models for weakly supervised semantic segmentation
(WSSS) with image-level labels is challenging and important. Recently, end-to-end WSSS …

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

Weakly Supervised Semantic Segmentation (WSSS) with only image-level supervision has
garnered increasing attention due to its low annotation cost compared to pixel-level …

Abstract In Weakly Supervised Semantic Segmentation (WSSS), Class Activation Maps
(CAMs) usually 1) do not cover the whole object and 2) be activated on irrelevant regions …

Many weakly supervised semantic segmentation (WSSS) methods employ the class
activation map (CAM) to generate the initial segmentation results. However, CAM often fails …