Skip to yearly menu bar Skip to main content


Federated Online Adaptation for Deep Stereo

Matteo Poggi · Fabio Tosi

Arch 4A-E Poster #61
[ ] [ Project Page ]
Fri 21 Jun 10:30 a.m. PDT — noon PDT


We introduce a novel approach for adapting deep stereonetworks in a collaborative manner. By building over principles of federated learning, we develop a distributed framework allowing for demanding the optimization process to a number of clients deployed in different environments. This makes it possible, for a deep stereo network running on resourced-constrained devices, to capitalize on the adaptation process carried out by other instances of the same architecture, and thus improve its accuracy in challenging environments even when it cannot carry out adaptation on its own. Experimental results show how federated adaptation performs equivalently to on-device adaptation, and even better when dealing with challenging environments.

Live content is unavailable. Log in and register to view live content