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

We address the problem of weakly-supervised semantic segmentation (WSSS) using
bounding box annotations. Although object bounding boxes are good indicators to segment …

Generating precise class-aware pseudo ground-truths, aka, class activation maps (CAMs), is
essential for Weakly-Supervised Semantic Segmentation. The original CAM method usually …

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

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

Fully-supervised salient object detection (SOD) methods have made great progress, but
such methods often rely on a large number of pixel-level annotations, which are time …

Sparse labels have been attracting much attention in recent years. However, the
performance gap between weakly supervised and fully supervised salient object detection …

Recently, video object segmentation has received great attention in the computer vision
community. Most of the existing methods heavily rely on the pixel-wise human annotations …

Weakly-supervised semantic segmentation aims to reduce labeling costs by training
semantic segmentation models using weak supervision, such as image-level class labels …

Efficiently training accurate deep models for weakly supervised semantic segmentation
(WSSS) with image-level labels is challenging and important. Recently, end-to-end WSSS …