Semantic-Cohesive Knowledge Distillation for Deep Cross-modal Hashing
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866911203558686720 |
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| author | Sun, Changchang Chen, Vickie Yan, Yan |
| author_facet | Sun, Changchang Chen, Vickie Yan, Yan |
| contents | Recently, deep supervised cross-modal hashing methods have achieve compelling success by learning semantic information in a self-supervised way. However, they still suffer from the key limitation that the multi-label semantic extraction process fail to explicitly interact with raw multimodal data, making the learned representation-level semantic information not compatible with the heterogeneous multimodal data and hindering the performance of bridging modality gap. To address this limitation, in this paper, we propose a novel semantic cohesive knowledge distillation scheme for deep cross-modal hashing, dubbed as SODA. Specifically, the multi-label information is introduced as a new textual modality and reformulated as a set of ground-truth label prompt, depicting the semantics presented in the image like the text modality. Then, a cross-modal teacher network is devised to effectively distill cross-modal semantic characteristics between image and label modalities and thus learn a well-mapped Hamming space for image modality. In a sense, such Hamming space can be regarded as a kind of prior knowledge to guide the learning of cross-modal student network and comprehensively preserve the semantic similarities between image and text modality. Extensive experiments on two benchmark datasets demonstrate the superiority of our model over the state-of-the-art methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_09664 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Semantic-Cohesive Knowledge Distillation for Deep Cross-modal Hashing Sun, Changchang Chen, Vickie Yan, Yan Machine Learning Computer Vision and Pattern Recognition Information Retrieval Recently, deep supervised cross-modal hashing methods have achieve compelling success by learning semantic information in a self-supervised way. However, they still suffer from the key limitation that the multi-label semantic extraction process fail to explicitly interact with raw multimodal data, making the learned representation-level semantic information not compatible with the heterogeneous multimodal data and hindering the performance of bridging modality gap. To address this limitation, in this paper, we propose a novel semantic cohesive knowledge distillation scheme for deep cross-modal hashing, dubbed as SODA. Specifically, the multi-label information is introduced as a new textual modality and reformulated as a set of ground-truth label prompt, depicting the semantics presented in the image like the text modality. Then, a cross-modal teacher network is devised to effectively distill cross-modal semantic characteristics between image and label modalities and thus learn a well-mapped Hamming space for image modality. In a sense, such Hamming space can be regarded as a kind of prior knowledge to guide the learning of cross-modal student network and comprehensively preserve the semantic similarities between image and text modality. Extensive experiments on two benchmark datasets demonstrate the superiority of our model over the state-of-the-art methods. |
| title | Semantic-Cohesive Knowledge Distillation for Deep Cross-modal Hashing |
| topic | Machine Learning Computer Vision and Pattern Recognition Information Retrieval |
| url | https://arxiv.org/abs/2510.09664 |