Graph-Based Cross-Domain Knowledge Distillation for Cross-Dataset Text-to-Image Person Retrieval Article Swipe
Bingjun Luo
,
Jinpeng Wang
,
Zewen Wang
,
Junjie Zhu
,
Xibin Zhao
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2501.15052
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2501.15052
Video surveillance systems are crucial components for ensuring public safety and management in smart city. As a fundamental task in video surveillance, text-to-image person retrieval aims to retrieve the target person from an image gallery that best matches the given text description. Most existing text-to-image person retrieval methods are trained in a supervised manner that requires sufficient labeled data in the target domain. However, it is common in practice that only unlabeled data is available in the target domain due to the difficulty and cost of data annotation, which limits the generalization of existing methods in practical application scenarios. To address this issue, we propose a novel unsupervised domain adaptation method, termed Graph-Based Cross-Domain Knowledge Distillation (GCKD), to learn the cross-modal feature representation for text-to-image person retrieval in a cross-dataset scenario. The proposed GCKD method consists of two main components. Firstly, a graph-based multi-modal propagation module is designed to bridge the cross-domain correlation among the visual and textual samples. Secondly, a contrastive momentum knowledge distillation module is proposed to learn the cross-modal feature representation using the online knowledge distillation strategy. By jointly optimizing the two modules, the proposed method is able to achieve efficient performance for cross-dataset text-to-image person retrieval. acExtensive experiments on three publicly available text-to-image person retrieval datasets demonstrate the effectiveness of the proposed GCKD method, which consistently outperforms the state-of-the-art baselines.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2501.15052
- https://arxiv.org/pdf/2501.15052
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4406880059
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- OpenAlex ID
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https://openalex.org/W4406880059Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2501.15052Digital Object Identifier
- Title
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Graph-Based Cross-Domain Knowledge Distillation for Cross-Dataset Text-to-Image Person RetrievalWork title
- Type
-
preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-01-25Full publication date if available
- Authors
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Bingjun Luo, Jinpeng Wang, Zewen Wang, Junjie Zhu, Xibin ZhaoList of authors in order
- Landing page
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https://arxiv.org/abs/2501.15052Publisher landing page
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https://arxiv.org/pdf/2501.15052Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2501.15052Direct OA link when available
- Concepts
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Computer science, Graph, Knowledge graph, Distillation, Domain (mathematical analysis), Image (mathematics), Information retrieval, Artificial intelligence, Theoretical computer science, Mathematics, Chemistry, Chromatography, Mathematical analysisTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.annotation, | 87 |
| abstract_inverted_index.application | 97 |
| abstract_inverted_index.components. | 139 |
| abstract_inverted_index.contrastive | 161 |
| abstract_inverted_index.correlation | 152 |
| abstract_inverted_index.cross-modal | 120, 171 |
| abstract_inverted_index.demonstrate | 210 |
| abstract_inverted_index.experiments | 201 |
| abstract_inverted_index.fundamental | 17 |
| abstract_inverted_index.graph-based | 142 |
| abstract_inverted_index.multi-modal | 143 |
| abstract_inverted_index.outperforms | 220 |
| abstract_inverted_index.performance | 194 |
| abstract_inverted_index.propagation | 144 |
| abstract_inverted_index.Cross-Domain | 113 |
| abstract_inverted_index.Distillation | 115 |
| abstract_inverted_index.consistently | 219 |
| abstract_inverted_index.cross-domain | 151 |
| abstract_inverted_index.description. | 41 |
| abstract_inverted_index.distillation | 164, 178 |
| abstract_inverted_index.surveillance | 1 |
| abstract_inverted_index.unsupervised | 107 |
| abstract_inverted_index.cross-dataset | 129, 196 |
| abstract_inverted_index.effectiveness | 212 |
| abstract_inverted_index.surveillance, | 21 |
| abstract_inverted_index.text-to-image | 22, 44, 124, 197, 206 |
| abstract_inverted_index.generalization | 91 |
| abstract_inverted_index.representation | 122, 173 |
| abstract_inverted_index.state-of-the-art | 222 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile |