Large Vision-Language Models with PEFT for Generating Descriptive Annotations in Person Re-Identification

Generating descriptive annotations for person re-identification (Re-ID) images is essential for bridging vision and language domain, improving both interpretability and cross-modal retrieval performance. However, large vision-language models (LVLMs), which trained on broad web-scale corpora, often struggle to generate accurate, context-relevant descriptions for ReID samples due to inherent challenges such as occlusions, low resolution, varying illumination, and diverse viewpoints. In this paper, we propose to apply Parameter-Efficient Fine-Tuning (PEFT) via Low-Rank Adaptation (LoRA) to tune Qwen2-VL for ReID-specific captioning tasks. Leveraging existing Re-ID datasets with paired image-text annotations, our fine-tuned model generates domain-aligned and discriminative captions. Experiments show significant improvements in caption relevance and identity descriptiveness, highlighting the potential of PEFT-tuned LVLMs for real-world ReID applications.