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From attributes to natural language: A survey and foresight on text-based person re-identification

Fanzhi Jiang Orcid Logo, Scott Yang Orcid Logo, Mark Jones Orcid Logo, Liumei Zhang.

Information Fusion, Start page: 102879

Swansea University Authors: Scott Yang Orcid Logo, Mark Jones Orcid Logo

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DOI (Published version): 10.1016/j.inffus.2024.102879

Abstract

Text-based person re-identification (Re-ID) is a challenging topic in the field of complex multimodalanalysis, its ultimate aim is to recognize specific pedestrians by scrutinizing attributes/natural language descriptions. Despite the wide range of applicable areas such as security surveillance, vid...

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Published in: Information Fusion
ISSN: 1566-2535
Published: Elsevier BV 2025
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URI: https://cronfa.swan.ac.uk/Record/cronfa68609
Abstract: Text-based person re-identification (Re-ID) is a challenging topic in the field of complex multimodalanalysis, its ultimate aim is to recognize specific pedestrians by scrutinizing attributes/natural language descriptions. Despite the wide range of applicable areas such as security surveillance, video retrieval, person tracking, and social media analytics, there is a notable absence of comprehensive reviews dedicated to summarizing the text-based person Re-ID from a technical perspective. To address this gap, we propose to introduce a taxonomy spanning Evaluation, Strategy, Architecture, and Optimization dimensions, providing a comprehensive survey of the text-based person Re-ID task. We start by laying the groundwork for text-based person Re-ID, elucidating fundamental concepts related to attribute/natural language-based identification. Then a thorough examination of existing benchmark datasets and metrics is presented. Subsequently, we further delve into prevalent feature extraction strategies employed in text-based person Re-ID research, followed by a concise summary of common network architectures within the domain. Prevalent loss functions utilized for model optimization and modality alignment in text-based person Re-ID are also scrutinized. To conclude, we offer a concise summary of our findings, pinpointing challenges in text-based person Re-ID. In response to these challenges, we outline potential avenues for future open-set text-based person Re-ID and present a baseline architecture for text-based pedestrian image generation guided re-identification (TBPGR).
College: Faculty of Science and Engineering
Funders: This document is the results of the research project funded by The Engineering and Physical Sciences Research Council of UK Research and Innovation (UKRI)
Start Page: 102879

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