Web: http://arxiv.org/abs/2209.07806

Sept. 19, 2022, 1:14 a.m. | Lei Li, Souhaib Attaiki, Maks Ovsjanikov

cs.CV updates on arXiv.org arxiv.org

In this work, we present a novel learning-based framework that combines the
local accuracy of contrastive learning with the global consistency of geometric
approaches, for robust non-rigid matching. We first observe that while
contrastive learning can lead to powerful point-wise features, the learned
correspondences commonly lack smoothness and consistency, owing to the purely
combinatorial nature of the standard contrastive losses. To overcome this
limitation we propose to boost contrastive feature learning with two types of
smoothness regularization that inject geometric …

arxiv consistent

More from arxiv.org / cs.CV updates on arXiv.org

Research Scientists

@ ODU Research Foundation | Norfolk, Virginia

Embedded Systems Engineer (Robotics)

@ Neo Cybernetica | Bedford, New Hampshire

2023 Luis J. Alvarez and Admiral Grace M. Hopper Postdoc Fellowship in Computing Sciences

@ Lawrence Berkeley National Lab | San Francisco, CA

Senior Manager Data Scientist

@ NAV | Remote, US

Senior AI Research Scientist

@ Earth Species Project | Remote anywhere

Research Fellow- Center for Security and Emerging Technology (Multiple Opportunities)

@ University of California Davis | Washington, DC

Staff Fellow - Data Scientist

@ U.S. FDA/Center for Devices and Radiological Health | Silver Spring, Maryland

Staff Fellow - Senior Data Engineer

@ U.S. FDA/Center for Devices and Radiological Health | Silver Spring, Maryland

Senior Research Engineer, Applied Language

@ DeepMind | Mountain View, California, US

Machine Learning Engineer

@ Bluevine | Austin, TX

Lead Manager - Analytics & Data Science

@ Tide | India(Remote)

Machine Learning Engineer

@ Gtmhub | Indore, Madhya Pradesh, India