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

TL;DR

This paper introduces DCGH, a deep hashing method that effectively preserves intra-class and inter-class relationships for improved multi-label cross-modal retrieval, outperforming existing methods on benchmark datasets.

Contribution

The paper proposes a novel DCGH framework combining proxy loss, pairwise loss, and variance constraint to enhance cross-modal hashing performance.

Findings

DCGH achieves superior retrieval accuracy on benchmark datasets.

The method effectively maintains intra-class cohesion and inter-class structure.

Experimental results outperform existing cross-modal hashing techniques.

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.