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Research data . Audiovisual . 2020 . Embargo end date: 29 Dec 2020

1088 - Online Domain Adaptation for Person Re-Identification with a Human in the Loop

25th International Conference on Pattern Recognition 2021; Delussu, Rita;
Published: 01 Jan 2020
Publisher: Underline Science Inc.

ICPR Browser Link: Abstract: Supervised deep learning methods have recently achieved remarkable performance in person re-identification. Unsupervised domain adaptation (UDA) approaches have also been proposed for application scenarios where only unlabelled data are available from target camera views. We consider a more challenging scenario when even collecting a suitable amount of representative, unlabelled target data for offline training or fine-tuning is infeasible. In this context we revisit the human-in- the-loop (HITL) approach, which exploits online the operator���s feedback on a small amount of target data. We argue that HITL is a kind of online domain adaptation specifically suited to person re-identification. We then reconsider relevance feedback methods for content-based image retrieval that are computationally much cheaper than state-of-the-art HITL methods for person re- identification, and devise a specific feedback protocol for them. Experimental results show that HITL can achieve comparable or better performance than UDA, and is therefore a valid alternative when the lack of unlabelled target data makes UDA infeasible.


Computer and Information Science, Optical Engineering

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Audiovisual . 2020
Data sources: Datacite