Visual Place Recognition (VPR) aims to estimate the location of an image by treating it as a retrieval problem. VPR uses a database of geo-tagged images and leverages deep neural networks to extract a global representation, called descriptor, from each image. While the training data for VPR models often originates from diverse, geographically scattered sources (geo-tagged images), the training process itself is typically assumed to be centralized. This research revisits the task of VPR through the lens of Federated Learning (FL), addressing several key challenges associated with this adaptation. VPR data inherently lacks well-defined classes, and models are typically trained using contrastive learning, which necessitates a data mining step on a centralized database. Additionally, client devices in federated systems can be highly heterogeneous in terms of their processing capabilities. The proposed FedVPR framework not only presents a novel approach for VPR but also introduces a new, challenging, and realistic task for FL research. This has the potential to spur the application of FL to other image retrieval tasks.

Collaborative Visual Place Recognition through Federated Learning / Dutto, Mattia; Berton, Gabriele; Caldarola, Debora; Fani, Eros; Trivigno, Gabriele; Masone, Carlo. - (2024), pp. 4215-4225. (Intervento presentato al convegno IEEE / CVF Computer Vision and Pattern Recognition Conference Workshop tenutosi a Seattle (USA) nel 17-18 June 2024) [10.1109/CVPRW63382.2024.00425].

Collaborative Visual Place Recognition through Federated Learning

Dutto, Mattia;Berton, Gabriele;Caldarola, Debora;Fani, Eros;Trivigno, Gabriele;Masone, Carlo
2024

Abstract

Visual Place Recognition (VPR) aims to estimate the location of an image by treating it as a retrieval problem. VPR uses a database of geo-tagged images and leverages deep neural networks to extract a global representation, called descriptor, from each image. While the training data for VPR models often originates from diverse, geographically scattered sources (geo-tagged images), the training process itself is typically assumed to be centralized. This research revisits the task of VPR through the lens of Federated Learning (FL), addressing several key challenges associated with this adaptation. VPR data inherently lacks well-defined classes, and models are typically trained using contrastive learning, which necessitates a data mining step on a centralized database. Additionally, client devices in federated systems can be highly heterogeneous in terms of their processing capabilities. The proposed FedVPR framework not only presents a novel approach for VPR but also introduces a new, challenging, and realistic task for FL research. This has the potential to spur the application of FL to other image retrieval tasks.
2024
979-8-3503-6547-4
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2987725