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arXiv · 2410.15387

Deep Class-guided Hashing for Multi-label Cross-modal Retrieval

Hao Chen·Lei Zhu·Xinghui Zhu
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Abstract

Deep hashing, due to its low cost and efficient retrieval advantages, is widely valued in cross-modal retrieval. However, existing cross-modal hashing methods either explore the relationships between data points, which inevitably leads to intra-class dispersion, or explore the relationships between data points and categories while ignoring the preservation of inter-class structural relationships, resulting in the generation of suboptimal hash codes. How to maintain both intra-class aggregation and inter-class structural relationships, In response to this issue, this paper proposes a DCGH method. Specifically, we use proxy loss as the mainstay to maintain intra-class aggregation of data, combined with pairwise loss to maintain inter-class structural relationships, and on this basis, further propose a variance constraint to address the semantic bias issue caused by the combination. A large number of comparative experiments on three benchmark datasets show that the DCGH method has comparable or even better performance compared to existing cross-modal retrieval methods. The code for the implementation of our DCGH framework is available at https://github.com/donnotnormal/DCGH.

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Hao Chen, Lei Zhu, Xinghui Zhu. 2024-10-20. Deep Class-guided Hashing for Multi-label Cross-modal Retrieval. https://arxiv.org/abs/2410.15387

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