Mapping woody vegetation from aerial images is an important task bluein environment
monitoring and management. A few studies have shown that semantic segmentation …

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

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 …

In this paper, we consider the problem of open-vocabulary semantic segmentation (OVS),
which aims to segment objects of arbitrary classes instead of pre-defined, closed-set …

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

Generating accurate pseudo-labels under the supervision of image categories is a crucial
step in Weakly Supervised Semantic Segmentation (WSSS). In this work, we propose a Mat …

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

Abstract Weakly Supervised Semantic Segmentation (WSSS) research has explored many
directions to improve the typical pipeline CNN plus class activation maps (CAM) plus …