De-centralised learning for re-indentification

Abstract

A method for generating an optimised domain-generalisable model for re-identification of a target in a set of candidate images. The method optimises a local feature embedding model for domain-specific feature representation at each client of a plurality of clients, then receives, at a central server, information on changes to the local feature embedding model at each respective client resulting from the optimising step, and then updates a global feature embedding model based on the changes to the local feature embedding model. The method further receives, at each client from the central server, information representative of the updates to the global feature embedding model, then maps, at each client, on to the respective local feature embedding model at least a portion of the received updates, and subsequently updates, at each client, the respective local feature embedding model based on the mapped updates. The steps are repeated until convergence criteria are met, wherein the global feature embedding model is the optimised domain-generalisable model for re-identification of a target in a set of candidate images.