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 …

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

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 …

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

In this work, we propose a new transformer-based regularization to better localize objects for
Weakly supervised semantic segmentation (WSSS). In image-level WSSS, Class Activation …

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 …

Many existing weakly-supervised semantic segmentation methods focus on obtaining more
accurate pseudo-masks with weak labels. So far pseudo-masks have come close to the …

Semantic segmentation of remote sensing images (RSIs) is effective for large-scale land
cover mapping, which heavily relies on a large amount of training data with laborious pixel …