行人重识别近期技术综述
A review of Recent Techniques for Person Re-Identification
AI总结：
本综述聚焦行人重识别，回顾监督技术现状并总结近三年无监督方法进展，指出性能差距缩小，为领域发展提供双重视角洞察。
摘要：
行人重识别（ReId）是监控中的一项关键任务，涉及在不同摄像机视角下匹配个体。深度学习的出现，尤其是卷积神经网络和注意力机制等监督技术，显著提升了行人重识别的性能。然而，监督方法的成功依赖于大量标注数据，这在数据标注和计算成本方面带来了可扩展性挑战。为解决这些局限，近期研究已转向无监督行人重识别。利用丰富的未标注数据，无监督方法旨在克服对成对标注数据的需求。尽管传统上落后于监督方法，但无监督技术在近年来展现出有前景的发展，表明性能差距正在缩小。受这一不断演变的格局驱动，我们的综述追求两个主要目标。首先，我们回顾并分类监督行人重识别中的重要出版物，深入概述当前最先进水平，并强调该领域进一步改进的空间已很小。其次，我们探讨过去三年中无监督行人重识别的最新进展，提供对新兴趋势的见解，并揭示监督与无监督范式之间性能趋同的潜在可能。这一双焦点综述旨在为行人重识别的演变叙事做出贡献，既捕捉监督技术的成熟格局，也展现无监督学习领域的有前景成果。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
