Offline-Online Associated Camera-Aware Proxies for Unsupervised Person Re-identification
Menglin Wang, Jiachen Li, Baisheng Lai, Xiaojin Gong, Xian-Sheng Hua

TL;DR
This paper introduces camera-aware proxies and dual contrastive learning strategies to improve unsupervised person re-identification by better handling intra-cluster variance and dynamic associations.
Contribution
It proposes a novel clustering approach that splits clusters into camera-aware proxies and employs offline and online contrastive losses for enhanced unsupervised Re-ID.
Findings
Achieves competitive results on multiple Re-ID datasets.
Effectively reduces intra-ID variance caused by camera changes.
Demonstrates the effectiveness of combining offline and online proxy associations.
Abstract
Recently, unsupervised person re-identification (Re-ID) has received increasing research attention due to its potential for label-free applications. A promising way to address unsupervised Re-ID is clustering-based, which generates pseudo labels by clustering and uses the pseudo labels to train a Re-ID model iteratively. However, most clustering-based methods take each cluster as a pseudo identity class, neglecting the intra-cluster variance mainly caused by the change of cameras. To address this issue, we propose to split each single cluster into multiple proxies according to camera views. The camera-aware proxies explicitly capture local structures within clusters, by which the intra-ID variance and inter-ID similarity can be better tackled. Assisted with the camera-aware proxies, we design two proxy-level contrastive learning losses that are, respectively, based on offline and online…
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Taxonomy
TopicsVideo Surveillance and Tracking Methods · IoT and GPS-based Vehicle Safety Systems · Gait Recognition and Analysis
MethodsContrastive Learning
