Federated Online Adaptation for Deep Stereo

Fuente: arXiv
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Auteurs principaux: Poggi, Matteo, Tosi, Fabio
Format: Preprint
Publié: 2024
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author Poggi, Matteo
Tosi, Fabio
author_facet Poggi, Matteo
Tosi, Fabio
contents We introduce a novel approach for adapting deep stereo networks 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.
format Preprint
id arxiv_https___arxiv_org_abs_2405_14873
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Federated Online Adaptation for Deep Stereo
Poggi, Matteo
Tosi, Fabio
Computer Vision and Pattern Recognition
We introduce a novel approach for adapting deep stereo networks 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.
title Federated Online Adaptation for Deep Stereo
topic Computer Vision and Pattern Recognition
url https://arxiv.org/abs/2405.14873