Existing studies in weakly-supervised semantic segmentation (WSSS) using image-level
weak supervision have several limitations: sparse object coverage, inaccurate object …

Weakly supervised semantic segmentation produces pixel-level localization from class
labels; however, a classifier trained on such labels is likely to focus on a small discriminative …

Semantic segmentation is a challenging task in the absence of densely labelled data. Only
relying on class activation maps (CAM) with image-level labels provides deficient …

Few-shot segmentation has been attracting a lot of attention due to its effectiveness to
segment unseen object classes with a few annotated samples. Most existing approaches …

Abstract Weakly Supervised Semantic Segmentation (WSSS) based on image-level labels
has been greatly advanced by exploiting the outputs of Class Activation Map (CAM) to …

Weakly supervised semantic segmentation (WSSS) using image-level classification labels
usually utilizes the Class Activation Maps (CAMs) to localize objects of interest in images …

Image-level weakly supervised semantic segmentation is a challenging task. As
classification networks tend to capture notable object features and are insensitive to …

Acquiring sufficient ground-truth supervision to train deep visual models has been a
bottleneck over the years due to the data-hungry nature of deep learning. This is …

Weakly supervised semantic segmentation is receiving great attention due to its low human
annotation cost. In this paper, we aim to tackle bounding box supervised semantic …

Weakly-supervised semantic segmentation (WSSS) using image-level labels has recently
attracted much attention for reducing annotation costs. Existing WSSS methods utilize …