Sound source localization in visual scenes aims to localize objects emitting the sound in a
given image. Recent works showing impressive localization performance typically rely on …

Learning representations with self-supervision for convolutional networks (CNN) has been
validated to be effective for vision tasks. As an alternative to CNN, vision transformers (ViT) …

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 …

Reconstruction methods based on deep learning have greatly shortened the data
acquisition time of magnetic resonance imaging (MRI). However, these methods typically …

The goal of weakly-supervised object localization (WSOL) is to train a localization model
without the location information of the object (s). Recently, most existing WSOL methods …

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 …

Unsupervised person re-identification (Re-ID) is a challenging task due to the complexity of
the camera view and the variability introduced by pedestrian pose. The irrelevant samples in …

The visual detection and tracking of surface terrain is required for spacecraft to safely land
on or navigate within close proximity to celestial objects. Current approaches rely on …

In this paper, we develop machine learning techniques to identify unknown printers in early
modern (c.~ 1500--1800) English printed books. Specifically, we focus on matching uniquely …

Recent self-supervised learning methods, where instance discrimination task is a
fundamental way of pretraining convolutional neural networks (CNN), excel in transfer …