# Fusion-supervised Deep Cross-modal Hashing

arXiv: https://arxiv.org/abs/1904.11171 · HTML: https://mlsanity.com/paper/1904.11171/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Apr 2019.

## Built on by (most cited first, 1 of 1)

- [Deep Supervised Information Bottleneck Hashing for Cross-modal Retrieval based Computer-aided Diagnosis](https://mlsanity.com/paper/2205.08365/) (2022)

## Abstract (arXiv)

Deep hashing has recently received attention in cross-modal retrieval for its impressive advantages. However, existing hashing methods for cross-modal retrieval cannot fully capture the heterogeneous multi-modal correlation and exploit the semantic information. In this paper, we propose a novel Fusion-supervised Deep Cross-modal Hashing (FDCH) approach. Firstly, FDCH learns unified binary codes through a fusion hash network with paired samples as input, which effectively enhances the modeling of the correlation of heterogeneous multi-modal data. Then, these high-quality unified hash codes further supervise the training of the modality-specific hash networks for encoding out-of-sample queries. Meanwhile, both pair-wise similarity information and classification information are embedded in the hash networks under one stream framework, which simultaneously preserves cross-modal similarity and keeps semantic consistency. Experimental results on two benchmark datasets demonstrate the state-of-the-art performance of FDCH.