A Reflection on Leveraging Vision Language Model for Visual Analysis in Image-Based Person Re-Identification

Image-based person re-identification (Re-ID) aims to identify and track individuals across multiple camera views using query images. While machine learning methods have made progress, their real-world performance remains limited. A key challenge lies in the nature of person retrieval, which requires users to perform fine-grained matching and filtering. This process often involves manually comparing and evaluating a large number of candidate images, resulting in low retrieval efficiency and being time-consuming. To address these issues, we introduce textual information to assist users in performing fine-grained retrieval tasks. Specifically, we utilize vision-language models fine-tuned with domain knowledge to generate hierarchical textual descriptions as retrieval cues. We also provide a visual analysis tool, which adopts multi-view and adjustable visual encodings to aggregate and present image data, supporting interactive browsing and retrieval. Finally, we conduct a case study and visual analysis experiments to evaluate the effectiveness of the textual retrieval cues. The evaluation results reveal the potential of textual information in optimizing person retrieval and offers insights for future work.