mlsanityData to 7 Oct 2026

From Attributes to Natural Language: A Survey and Foresight on Text-based Person Re-identification

arXiv 2408.00096 ↗First posted 31 Jul 2024cs.CV cs.AIcited by 5

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Abstract (arXiv)

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).

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 108 of its 195 references that are in the map. First posted on arXiv 31 Jul 2024.

Builds on

108 of 195 references.

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50 most cited of 108 All 108 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 3; 2026: 2.

5 papers.

  1. 2025LATex: Leveraging Attribute-based Text Knowledge for Aerial-Ground Person Re-Identificationcited by 8
  2. 2025Decoupled Cross-Modal Alignment Network for Text-RGBT Person Retrieval and A High-Quality Benchmarkcited by 1
  3. 2025Addressing the ID-Matching Challenge in Long Video Captioningcited by 1
  4. 2026Document Classification Pattern Recognition via Information Fusion: A Systematic Review of Multimodal and Multiview Representation Approachescited by 1
  5. 2026Where Should I Join? Robot Group Joining via Language-Guided Goal Predictioncited by 0