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 …

We introduce a self-supervised method for learning visual correspondence from unlabeled
video. The main idea is to use cycle-consistency in time as free supervisory signal for …

Unsupervised domain adaptation algorithms aim to transfer the knowledge learned from one
domain to another (eg, synthetic to real images). The adapted representations often do not …

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

We introduce a self-supervised representation learning method based on the task of
temporal alignment between videos. The method trains a network using temporal cycle …

We present the Sketchy database, the first large-scale collection of sketch-photo pairs. We
ask crowd workers to sketch particular photographic objects sampled from 125 categories …

Image-to-image translation is a class of vision and graphics problems where the goal is to
learn the mapping between an input image and an output image using a training set of …

We present a deep learning framework for accurate visual correspondences and
demonstrate its effectiveness for both geometric and semantic matching, spanning across …

Discriminative deep learning approaches have shown impressive results for problems
where human-labeled ground truth is plentiful, but what about tasks where labels are difficult …