Generating accurate pseudo-labels under the supervision of image categories is a crucial
step in Weakly Supervised Semantic Segmentation (WSSS). In this work, we propose a Mat …

Weakly supervised dense object localization (WSDOL) relies generally on Class Activation
Mapping (CAM), which exploits the correlation between the class weights of the image …

Change detection (CD) in remote sensing (RS) imagery is a pivotal method for detecting
changes in the Earth's surface, finding wide applications in urban planning, disaster …

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

Weakly supervised object localization and semantic segmentation aim to localize objects
using only image-level labels. Recently, a new paradigm has emerged by generating a …

Referring image segmentation (RIS) aims to localize the object in an image referred by a
natural language expression. Most previous studies learn RIS with a large-scale dataset …

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 …

Weakly supervised object localization (WSOL) is a challenging task aiming to localize
objects with only image-level supervision. Recent works apply visual transformer to WSOL …

In the studies of Weakly Supervised Semantic Segmentation (WSSS) with image-level
labels, there is an issue of incomplete semantic information, which we summarize as …

The number, relative ratio, and appearance of peripheral blood leukocytes can assist
doctors to diagnose diseases such as lymphoma and leukemia. Therefore, segmentation of …