Semantic segmentation has been continuously investigated in the last ten years, and
majority of the established technologies are based on supervised models. In recent years …

Image-level weakly supervised semantic segmentation has received increasing attention
due to its low annotation cost. Existing methods mainly rely on Class Activation Mapping …

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

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

In this paper we focus on the task of weakly-supervised semantic segmentation supervised
with image-level labels. Since the pixel-level annotation is not available in the training …

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

End-to-end weakly supervised semantic segmentation aims at optimizing a segmentation
model in a single-stage training process based on only image annotations. Existing methods …

In this paper, we aim to tackle semi-and-weakly supervised semantic segmentation
(SWSSS), where many image-level classification labels and a few pixel-level annotations …

Abstract For Weakly-Supervised Semantic Segmentation (WSSS) with image-level
annotation, mostly relies on the classification network to generate initial segmentation …

Great progress has been witnessed for weakly supervised semantic segmentation, which
aims to segment objects without dense pixel annotations. Most approaches concentrate on …