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 rapid development of deep learning has driven significant progress in image semantic
segmentation—a fundamental task in computer vision. Semantic segmentation algorithms …

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

Extracting class activation maps (CAM) from a classification model often results in poor
coverage on foreground objects, ie, only the discriminative region (eg, the" head" of" sheep") …

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

Weakly supervised dense object localization (WSDOL) relies generally on Class Activation
Map** (CAM), which exploits the correlation between the class weights of the image …

Weakly supervised semantic segmentation (WSSS) models relying on class activation maps
(CAMs) have achieved desirable performance comparing to the non-CAMs-based …

This paper proposes a novel transformer-based framework to generate accurate class-
specific object localization maps for weakly supervised semantic segmentation (WSSS) …

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

Weakly-supervised semantic segmentation aims to train a semantic segmentation network
using weak labels. Among weak labels, image-level label has been the most popular choice …