Emotion recognition has recently attracted extensive interest due to its significant
applications to human-computer interaction. The expression of human emotion depends on …

Abstract Weakly Supervised Object Localization (WSOL) aims to localize objects with image-
level supervision. Existing works mainly rely on Class Activation Mapping (CAM) derived …

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 …

Weakly-supervised learning (WSL) has recently triggered substantial interest as it mitigates
the lack of pixel-wise annotations. Given global image labels, WSL methods yield pixel-level …

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 …

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 …

We propose a novel unsupervised object localization method that allows us to explain the
predictions of the model by utilizing self-supervised pre-trained models without additional …

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

Weakly supervised video object localization (WSVOL) allows locating object in videos using
only global video tags such as object classes. State-of-art methods rely on multiple …

Longitudinal studies, where a series of images from the same set of individuals are acquired
at different time-points, represent a popular technique for studying and characterizing …