Visual and audio signals often coexist in natural environments, forming audio-visual events
(AVEs). Given a video, we aim to localize video segments containing an AVE and identify its …

Weakly supervised object localization aims to find a target object region in a given image
with only weak supervision, such as image-level labels. Most existing methods use a class …

Weakly supervised object localization (WSOL) focuses on localizing objects only with the
supervision of image-level classification masks. Most previous WSOL methods follow the …

Using deep learning models to diagnose cancer from histology data presents several
challenges. Cancer grading and localization of regions of interest (ROIs) in these images …

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

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

An increasing number of applications in computer vision, specially, in medical imaging and
remote sensing, become challenging when the goal is to classify very large images with tiny …

Self-supervised vision transformers (SSTs) have shown great potential to yield rich
localization maps that highlight different objects in an image. However, these maps remain …

Obtaining accurate pixel-level localization from class labels is a crucial process in weakly
supervised semantic segmentation and object localization. Attribution maps from a trained …

Weakly supervised object localization (WSOL) aims to learn representations that encode
object location using only image-level category labels. However, many objects can be …