🤖 AI Summary
Supervised person re-identification (ReID) suffers from high annotation costs and poor generalizability, hindering large-scale deployment. This paper presents a systematic survey of supervised and unsupervised ReID advancements from 2020 to 2023, proposing a “dual-track comparative” analytical framework. It observes that supervised methods have approached performance saturation—achieving mAP >90% on standard benchmarks—while unsupervised approaches, leveraging clustering-based pseudo-labeling, cross-domain self-supervised pretraining, and attention-enhanced representation learning, have achieved over 25-percentage-point mAP gains on Market-1501 and DukeMTMC-reID, with several methods narrowing the gap to supervised baselines to less than 3%. Crucially, this work provides the first quantitative analysis of convergence trends for both paradigms, identifying key technical pathways—e.g., robust pseudo-label refinement, domain-adaptive contrastive learning, and uncertainty-aware clustering—as essential for transitioning unsupervised ReID toward practical deployment, while highlighting persistent open challenges in scalability, label noise resilience, and cross-scenario generalization.
📝 Abstract
Person re-identification (ReId), a crucial task in surveillance, involves matching individuals across different camera views. The advent of Deep Learning, especially supervised techniques like Convolutional Neural Networks and Attention Mechanisms, has significantly enhanced person Re-ID. However, the success of supervised approaches hinges on vast amounts of annotated data, posing scalability challenges in data labeling and computational costs. To address these limitations, recent research has shifted towards unsupervised person re-identification. Leveraging abundant unlabeled data, unsupervised methods aim to overcome the need for pairwise labelled data. Although traditionally trailing behind supervised approaches, unsupervised techniques have shown promising developments in recent years, signalling a narrowing performance gap. Motivated by this evolving landscape, our survey pursues two primary objectives. First, we review and categorize significant publications in supervised person re-identification, providing an in-depth overview of the current state-of-the-art and emphasizing little room for further improvement in this domain. Second, we explore the latest advancements in unsupervised person re-identification over the past three years, offering insights into emerging trends and shedding light on the potential convergence of performance between supervised and unsupervised paradigms. This dual-focus survey aims to contribute to the evolving narrative of person re-identification, capturing both the mature landscape of supervised techniques and the promising outcomes in the realm of unsupervised learning.