The semantic segmentation model usually provides pixel-wise category prediction for
images. However, a massive amount of pixel-wise annotation images is required for model …

In weakly-supervised semantic segmentation (WSSS) using only image-level class labels, a
problem with CNN-based Class Activation Maps (CAM) is that they tend to activate the most …

The fully supervised semantic segmentation requires detailed annotation of each pixel,
which is time-consuming and laborious at the pixel-by-pixel level. To solve this problem, the …

Class activation maps (CAMs) are crucial in weakly-supervised semantic segmentation
(WSSS) tasks. However, challenges arise when the initial CAM quality is limited, leading to …

The existing Zero-Shot Segmentation (ZSS) and Few-Shot Segmentation (FSS) methods
utilize fully supervised pixel-labeled seen classes to segment unseen classes. Pixel-level …

Weakly supervised semantic segmentation (WSSS) using only image-level labels is a
challenging task. Most existing methods utilize class activation map (CAM) to generate pixel …

Abstract Weakly Supervised Semantic Segmentation (WSSS) techniques explore individual
regularization strategies to refine Class Activation Maps (CAMs). In this work, we first …

Weakly Supervised Semantic Segmentation (WSSS) has received extensive attention in
recent years as it achieves pixel-level segmentation by only image-level labels. However …

Weakly supervised semantic segmentation (WSSS) can obtain pseudo-semantic masks
through a weaker level of supervised labels, reducing the need for costly pixel-level …

Class Activation Map (CAM) is a common tool to address image-level weakly supervised
image segmentation (WSIS) tasks, which mainly extract the most discriminative regions as …