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") …

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 …

The rapid development of deep learning has driven significant progress in the field of image
semantic segmentation-a fundamental task in computer vision. Semantic segmentation …

Weakly supervised semantic segmentation has witnessed great achievements with image-
level labels. Several recent approaches use the CLIP model to generate pseudo labels for …

Abstract Weakly Supervised Semantic Segmentation (WSSS) aims to learn the concept of
segmentation using image-level class labels. Recent WSSS works have shown promising …

Abstract Weakly Supervised Semantic Segmentation (WSSS) relies on Class Activation
Maps (CAMs) to extract spatial information from image-level labels. With the success of …

Recent weakly supervised semantic segmentation (WSSS) methods strive to incorporate
contextual knowledge to improve the completeness of class activation maps (CAM). In this …

Recently, CLIP-based approaches have exhibited remarkable performance on
generalization and few-shot learning tasks, fueled by the power of contrastive language …

End-to-end weakly supervised semantic segmentation (E2E-WSSS) aims at optimizing a
segmentation model in a single-stage training process based on only image annotations …