Abstract Self-Supervised Learning (SSL) surmises that inputs and pairwise positive
relationships are enough to learn meaningful representations. Although SSL has recently …

The goal of self-supervised learning from images is to construct image representations that
are semantically meaningful via pretext tasks that do not require semantic annotations. Many …

We address the problem of visible-infrared person re-identification (VI-reID), that is,
retrieving a set of person images, captured by visible or infrared cameras, in a cross-modal …

Contrastive self-supervised learning has emerged as a promising approach to unsupervised
visual representation learning. In general, these methods learn global (image-level) …

We present an image-based VIirtual Try-On Network (VITON) without using 3D information
in any form, which seamlessly transfers a desired clothing item onto the corresponding …

We introduce a data-driven approach for unsupervised video retargeting that translates
content from one domain to another while preserving the style native to a domain, ie, if …

We address the problem of finding realistic geometric corrections to a foreground object
such that it appears natural when composited into a background image. To achieve this, we …

We address the problem of determining correspondences between two images in
agreement with a geometric model such as an affine or thin-plate spline transformation, and …

Most activity localization methods in the literature suffer from the burden of frame-wise
annotation requirement. Learning from weak labels may be a potential solution towards …

Abstract 3D object reconstruction from a single image is a highly under-determined problem,
requiring strong prior knowledge of plausible 3D shapes. This introduces challenge for …