Abstract

Text-based person re-identification (Re-ID) is a challenging topic in the field of complex multimodal analysis, 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).

Related Files

Open access link

DOI

10.48550/arXiv.2408.00096
https://dx.doi.org/10.48550/arXiv.2408.00096

Citation

BibTeX

@article{CoRRReID,
  author       = {Fanzhi Jiang and Su Yang and Mark W. Jones and Liumei Zhang},
  title        = {From Attributes to Natural Language: {A} Survey and Foresight on Text-based Person Re-identification},
  journal      = {CoRR},
  volume       = {abs/2408.00096},
  year         = {2024},
  url          = {https://doi.org/10.48550/arXiv.2408.00096},
  doi          = {10.48550/ARXIV.2408.00096},
  eprinttype   = {arXiv},
  eprint       = {2408.00096},
}