Evolution of ReID: From Early Methods to LLM Integration
Journal:
arXiv
Published Date:
Jun 16, 2025
Abstract
Person re-identification (ReID) has evolved from handcrafted feature-based
methods to deep learning approaches and, more recently, to models incorporating
large language models (LLMs). Early methods struggled with variations in
lighting, pose, and viewpoint, but deep learning addressed these issues by
learning robust visual features. Building on this, LLMs now enable ReID systems
to integrate semantic and contextual information through natural language. This
survey traces that full evolution and offers one of the first comprehensive
reviews of ReID approaches that leverage LLMs, where textual descriptions are
used as privileged information to improve visual matching. A key contribution
is the use of dynamic, identity-specific prompts generated by GPT-4o, which
enhance the alignment between images and text in vision-language ReID systems.
Experimental results show that these descriptions improve accuracy, especially
in complex or ambiguous cases. To support further research, we release a large
set of GPT-4o-generated descriptions for standard ReID datasets. By bridging
computer vision and natural language processing, this survey offers a unified
perspective on the field's development and outlines key future directions such
as better prompt design, cross-modal transfer learning, and real-world
adaptability.
