Abstract Weakly Supervised Object Localization (WSOL), which aims to localize objects by
only using image-level labels, has attracted much attention because of its low annotation …

Object detection using single point supervision has received increasing attention over the
years. However, the performance gap between point supervised object detection (PSOD) …

The aim of weakly supervised object co-localization is to locate different objects of the same
superclass in a dataset. Recent methods achieve impressive co-localization performance by …

Drones are employed in a growing number of visual recognition applications. A recent
development in cell tower inspection is drone-based asset surveillance, where the …

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 …

In this paper, we focus on an under-explored issue of biased activation in prior weakly-
supervised object localization methods based on Class Activation Mapping (CAM). We …

Weakly-supervised object localization aims to indicate the category as well as the scope of
an object in an image given only the image-level labels. Most of the existing works are …

Weakly supervised object localization (WSOL) stands as a pivotal endeavor within the realm
of computer vision, entailing the location of objects utilizing merely image-level labels …

By exploring the localizable representations in deep CNN, weakly supervised object
localization (WSOL) methods could determine the position of the object in each image just …

Weakly supervised object localization (WSOL) is a challenging and promising task that aims
to localize objects solely based on the supervision of image category labels. In the absence …