Domain-Robust Deep Hashing: A Unified Framework for Fast Person Re-Identification.
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| Title: | Domain-Robust Deep Hashing: A Unified Framework for Fast Person Re-Identification. |
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| Authors: | LUO, Qi1 lq_wn@126.com |
| Source: | Technical Gazette / Tehnički Vjesnik. 2026, Vol. 33 Issue 3, p930-937. 8p. |
| Subjects: | Binary codes, Loss functions (Statistics), Feature extraction, Video surveillance, Image retrieval, Machine learning |
| Abstract: | Person re-identification (ReID) in large-scale surveillance requires methods that are both accurate and efficient. While deep hashing enables compact binary representations, it often suffers from accuracy degradation due to the domain gap between raw features and hash codes. This paper proposes a unified, open-source framework for fast person ReID that introduces a cross-domain loss function to explicitly bridge the feature and hash spaces. Our model-agnostic training strategy integrates seamlessly with existing architectures such as ResNet and OSNet. Experiments on Market1501 and CUHK03 demonstrate that the proposed framework outperforms state-of-the-art deep hashing and fast ReID methods, achieving up to 8.61% higher mean Average Precision (mAP). Extensive ablation studies validate the contribution of the cross-domain loss, and evaluations across multiple backbones confirm the framework's versatility. The results show that our approach not only improves accuracy but also provides a strong, reproducible baseline for efficient person re-identification. [ABSTRACT FROM AUTHOR] |
| Copyright of Technical Gazette / Tehnički Vjesnik is the property of Tehnicki Vjesnik and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 195131778 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Domain-Robust Deep Hashing: A Unified Framework for Fast Person Re-Identification. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22LUO%2C+Qi%22">LUO, Qi</searchLink><relatesTo>1</relatesTo><i> lq_wn@126.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Technical+Gazette+%2F+Tehnički+Vjesnik%22">Technical Gazette / Tehnički Vjesnik</searchLink>. 2026, Vol. 33 Issue 3, p930-937. 8p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Binary+codes%22">Binary codes</searchLink><br /><searchLink fieldCode="DE" term="%22Loss+functions+%28Statistics%29%22">Loss functions (Statistics)</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+extraction%22">Feature extraction</searchLink><br /><searchLink fieldCode="DE" term="%22Video+surveillance%22">Video surveillance</searchLink><br /><searchLink fieldCode="DE" term="%22Image+retrieval%22">Image retrieval</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Person re-identification (ReID) in large-scale surveillance requires methods that are both accurate and efficient. While deep hashing enables compact binary representations, it often suffers from accuracy degradation due to the domain gap between raw features and hash codes. This paper proposes a unified, open-source framework for fast person ReID that introduces a cross-domain loss function to explicitly bridge the feature and hash spaces. Our model-agnostic training strategy integrates seamlessly with existing architectures such as ResNet and OSNet. Experiments on Market1501 and CUHK03 demonstrate that the proposed framework outperforms state-of-the-art deep hashing and fast ReID methods, achieving up to 8.61% higher mean Average Precision (mAP). Extensive ablation studies validate the contribution of the cross-domain loss, and evaluations across multiple backbones confirm the framework's versatility. The results show that our approach not only improves accuracy but also provides a strong, reproducible baseline for efficient person re-identification. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Technical Gazette / Tehnički Vjesnik is the property of Tehnicki Vjesnik and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.17559/TV-20250621002765 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 8 StartPage: 930 Subjects: – SubjectFull: Binary codes Type: general – SubjectFull: Loss functions (Statistics) Type: general – SubjectFull: Feature extraction Type: general – SubjectFull: Video surveillance Type: general – SubjectFull: Image retrieval Type: general – SubjectFull: Machine learning Type: general Titles: – TitleFull: Domain-Robust Deep Hashing: A Unified Framework for Fast Person Re-Identification. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: LUO, Qi IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: 2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 13303651 Numbering: – Type: volume Value: 33 – Type: issue Value: 3 Titles: – TitleFull: Technical Gazette / Tehnički Vjesnik Type: main |
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