Weakly supervised semantic segmentation (WSSS) aims to bypass the need for laborious
pixel-level annotation by using only image-level annotation. Most existing methods rely on …

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

Weakly supervised semantic segmentation (WSSS) has employed Class Activation Maps
(CAMs) to localize the objects. However, the CAMs typically do not fit along the object …

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 (WSSS) models relying on class activation maps
(CAMs) have achieved desirable performance comparing to the non-CAMs-based …

Accurate change detection of built-up areas (BAs) fosters a comprehensive understanding of
urban development. The post-classification comparison (PCC) is a widely-used change …

This paper proposes a novel transformer-based framework to generate accurate class-
specific object localization maps for weakly supervised semantic segmentation (WSSS) …

Weakly supervised semantic segmentation is a challenging task that only takes image-level
labels as supervision but produces pixel-level predictions for testing. To address such a …

Weakly supervised semantic segmentation (WSSS) using only image-level labels can
greatly reduce the annotation cost and therefore has attracted considerable research …

A critical obstacle to achieve semantic segmentation of remote sensing images by the deep
convolutional neural network is the requirement of huge pixel-level labels. Taking building …