In today's digital age, Convolutional Neural Networks (CNNs), a subset of Deep Learning
(DL), are widely used for various computer vision tasks such as image classification, object …

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

Weakly supervised object localization (WSOL) remains challenging when learning object
localization models from image category labels. Conventional methods that discriminatively …

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 …

Weakly supervised object localization (WSOL) aims to localize objects based on only image-
level labels as supervision. Recently, transformers have been introduced into WSOL …

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 …

Classification activation map (CAM), utilizing the classification structure to generate pixel-
wise localization maps, is a crucial mechanism for weakly supervised object localization …

Weakly supervised object localization (WSOL) relaxes the requirement of dense annotations
for object localization by using image-level annotation to supervise the learning process …

Scribble-based weakly-supervised semantic segmentation using sparse scribble
supervision is gaining traction as it reduces annotation costs when compared to fully …