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

TitleOnline Domain Adaptation for Person Re-Identification with a Human in the Loop
Publication TypeConference Paper
Year of Publication2020
AuthorsDelussu, R, Putzu, L, Fumera, G, Roli, F
Conference Name25th International Conference on Pattern Recognition, {ICPR} 2020, Virtual Event / Milan, Italy, January 10-15, 2021
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.
Citation KeyDBLP:conf/icpr/DelussuPFR20
ICPR_CameraReady.pdf770.02 KB