An Investigation of the Domain Gap in CLIP-Based Person Re-Identification

Person re-identification (re-id) is a critical computer vision task aimed at identifying individuals across multiple non-overlapping cameras, with wide-ranging applications in intelligent surveillance systems. Despite recent advances, the domain gap-performance degradation when models encounter unseen datasets-remains a critical challenge. CLIP-based models, leveraging multimodal pre-training, offer potential for mitigating this issue by aligning visual and textual representations. In this study, we provide a comprehensive quantitative analysis of the domain gap in CLIP-based re-id systems across standard benchmarks, including Market-1501, DukeMTMC-reID, MSMT17, and Airport, simulating real-world deployment conditions. We systematically measure the performance of these models in terms of mean average precision (mAP) and Rank-1 accuracy, offering insights into the challenges faced during dataset transitions. Our analysis highlights the specific advantages introduced by CLIP's visual-textual alignment and evaluates its contribution relative to strong image encoder baselines. Additionally, we evaluate the impact of extending training sets with non-domain-specific data and incorporating random erasing augmentation, achieving an average improvement of +4.3% in mAP and +4.0% in Rank-1 accuracy. Our findings underscore the importance of standardized benchmarks and systematic evaluations for enhancing reproducibility and guiding future research. This work contributes to a deeper understanding of the domain gap in re-id, while highlighting pathways for improving model robustness and generalization in diverse, real-world scenarios.