Graph matching, which refers to a class of computational problems of finding an optimal
correspondence between the vertices of graphs to minimize (maximize) their node and edge …

As a fundamental and critical task in various visual applications, image matching can identify
then correspond the same or similar structure/content from two or more images. Over the …

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

Abstract Low Rank Regularization (LRR), in essence, involves introducing a low rank or
approximately low rank assumption to target we aim to learn, which has achieved great …

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 …

This paper presents KeypointNet, an end-to-end geometric reasoning framework to learn an
optimal set of category-specific keypoints, along with their detectors to predict 3D keypoints …

This paper addresses the problem of 3D pose estimation for multiple people in a few
calibrated camera views. The main challenge of this problem is to find the cross-view …

This paper addresses the problem of reconstructing 3D poses of multiple people from a few
calibrated camera views. The main challenge of this problem is to find the cross-view …