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 unse...
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MDPI AG
2025-01-01
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| Online Access: | https://www.mdpi.com/1424-8220/25/2/363 |
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| author | Andrea Asperti Leonardo Naldi Salvatore Fiorilla |
| author_facet | Andrea Asperti Leonardo Naldi Salvatore Fiorilla |
| author_sort | Andrea Asperti |
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| description | 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. |
| format | Article |
| id | doaj-art-df6e925fbc4c41a993bbec838aa7004e |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj-art-df6e925fbc4c41a993bbec838aa7004e2025-01-24T13:48:39ZengMDPI AGSensors1424-82202025-01-0125236310.3390/s25020363An Investigation of the Domain Gap in CLIP-Based Person Re-IdentificationAndrea Asperti0Leonardo Naldi1Salvatore Fiorilla2Department of Informatics—Science and Engineering (DISI), University of Bologna, 40126 Bologna, ItalyDepartment of Informatics—Science and Engineering (DISI), University of Bologna, 40126 Bologna, ItalyDepartment of Informatics—Science and Engineering (DISI), University of Bologna, 40126 Bologna, ItalyPerson 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.https://www.mdpi.com/1424-8220/25/2/363person re-identificationdomain gapCLIPdeep learningcomputer vision |
| spellingShingle | Andrea Asperti Leonardo Naldi Salvatore Fiorilla An Investigation of the Domain Gap in CLIP-Based Person Re-Identification Sensors person re-identification domain gap CLIP deep learning computer vision |
| title | An Investigation of the Domain Gap in CLIP-Based Person Re-Identification |
| title_full | An Investigation of the Domain Gap in CLIP-Based Person Re-Identification |
| title_fullStr | An Investigation of the Domain Gap in CLIP-Based Person Re-Identification |
| title_full_unstemmed | An Investigation of the Domain Gap in CLIP-Based Person Re-Identification |
| title_short | An Investigation of the Domain Gap in CLIP-Based Person Re-Identification |
| title_sort | investigation of the domain gap in clip based person re identification |
| topic | person re-identification domain gap CLIP deep learning computer vision |
| url | https://www.mdpi.com/1424-8220/25/2/363 |
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