November 12, 2025ElectronicsOpen Access

Advancements and Challenges in Deep Learning-Based Person Re-Identification: A Review

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LZLiang Zhao YHYuyan Han ZCZhihao Chen

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Overview

This review highlights deep learning advancements in person re-identification, suggesting solutions like federated learning to address ethical challenges.

Key Points

Person re-identification advancements include improved feature representation and domain adaptation, addressing security needs.
Key tools analyzed include deep learning methods such as transformer-based models and hybrid networks.
The review critiques persistent obstacles like annotation bias and privacy-utility trade-offs affecting real-world applications.
Causal reasoning and federated learning are proposed as emerging strategies to enhance interpretability and data governance.

Cite This Study

Zhao et al. (2025) studied this question.

synapsesocial.com/papers/692523d4c0ce034ddc355462 https://doi.org/10.3390/electronics14224398

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November 12, 2025ElectronicsOpen Access

Advancements and Challenges in Deep Learning-Based Person Re-Identification: A Review

View Full Paper

Ask AI

Bookmark

Authors

LZLiang Zhao YHYuyan Han ZCZhihao Chen

Discussion

Member takes

Overview

This review highlights deep learning advancements in person re-identification, suggesting solutions like federated learning to address ethical challenges.

Key Points

Person re-identification advancements include improved feature representation and domain adaptation, addressing security needs.
Key tools analyzed include deep learning methods such as transformer-based models and hybrid networks.
The review critiques persistent obstacles like annotation bias and privacy-utility trade-offs affecting real-world applications.
Causal reasoning and federated learning are proposed as emerging strategies to enhance interpretability and data governance.

Cite This Study

Zhao et al. (2025) studied this question.

synapsesocial.com/papers/692523d4c0ce034ddc355462 https://doi.org/10.3390/electronics14224398

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