US 12,333,439 B2
De-centralised learning for re-indentification
Shaogang Gong, London (GB); and Guile Wu, London (GB)
Assigned to VERITONE, INC., Denver, CO (US)
Appl. No. 17/795,959
Filed by Veritone, Inc., Denver, CO (US)
PCT Filed Jan. 29, 2021, PCT No. PCT/GB2021/050216
§ 371(c)(1), (2) Date Jul. 28, 2022,
PCT Pub. No. WO2021/152329, PCT Pub. Date Aug. 5, 2021.
Claims priority of application No. 2001313 (GB), filed on Jan. 30, 2020.
Prior Publication US 2023/0087863 A1, Mar. 23, 2023
Int. Cl. G06V 10/774 (2022.01); G06N 3/082 (2023.01); G06V 10/74 (2022.01); G06V 10/77 (2022.01); G06V 10/778 (2022.01); G06V 10/82 (2022.01); G06V 10/94 (2022.01); G06V 20/52 (2022.01)
CPC G06N 3/082 (2013.01) [G06V 10/761 (2022.01); G06V 10/7715 (2022.01); G06V 10/774 (2022.01); G06V 10/778 (2022.01); G06V 10/82 (2022.01); G06V 10/95 (2022.01); G06V 20/52 (2022.01)] 18 Claims
OG exemplary drawing
 
1. A method for generating an optimised domain-generalizable model for re-identification of a target in a set of candidate images, comprising:
optimising a local feature embedding model for domain-specific feature representation at each client of a plurality of clients, the local feature embedding model at each client of a plurality of clients optimised for a respective data set associated with a domain of each client of the plurality of clients;
receiving, at a central server from each client of the plurality of clients, information on changes to the local feature embedding model at each respective client resulting from the optimising step;
updating, at the central server, a global feature embedding model for domain-generalizable feature representation based on the changes to the local feature embedding model at each respective client of at least a subset of the plurality of clients;
receiving, at each client of the plurality of clients from the central server, information representative of the updates to the global feature embedding model;
mapping, at each client of the plurality of clients, on to the respective local feature embedding model, at least a portion of the received updates to the global feature embedding model;
updating, at each client of the plurality of clients, the respective local feature embedding model based on the mapped updates; and
repeating each of the previous steps until convergence criteria are met for the optimisation of the local feature embedding model at each of the plurality of local clients, wherein the global feature embedding model is the optimised domain-generalizable model for re-identification of a target image in a set of candidate images.