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 …

Abstract Class Activation Mapping (CAM) methods have recently gained much attention for
weakly-supervised object localization (WSOL) tasks. They allow for CNN visualization and …

Most of the recent image segmentation methods have tried to achieve the utmost
segmentation results using large-scale pixel-level annotated data sets. However, obtaining …

Weakly supervised semantic segmentation task aims to learn a segmentation model with
only image-level annotations. Existing methods generally refine the initial seeds to obtain …

Existing studies on semantic segmentation using image-level weak supervision have
several limitations, including sparse object coverage, inaccurate object boundaries, and co …

Weakly supervised semantic segmentation (WSSS) aims to produce pixel-wise class
predictions with only image-level labels for training. To this end, previous methods adopt the …

The existing publicly available datasets with pixel-level labels contain limited categories,
and it is difficult to generalize to the real world containing thousands of categories. In this …

The scarcity of fully-annotated data becomes the biggest obstacle that prevents many deep
learning approaches from widely applied. Weakly-supervised visual learning which can …

Few shot semantic segmentation has been proposed to enhance the generalization ability of
traditional models with limited data. Previous works mainly focus on the supervised tasks …