电子学报2025，Vol.53Issue(1)：209-220,12.DOI:10.12263/DZXB.20240032
基于迁移知识的跨模态双重哈希
Cross-Modal Dual Hashing Based on Transfer Knowledge
摘要
Abstract
With the popularity of social networks and the rapid growth of multimedia data,efficient cross-modal re-trieval has attracted more and more attention.Hashing is widely used in cross-modal retrieval tasks due to its high retrieval efficiency and low storage cost.However,most of these deep learning-based cross-modal hashing retrieval methods utilize image networks and text networks to respectively generate corresponding modal hash codes,making it difficult to obtain more efficient hash codes and unable to further reduce the modal gap between different modal data.To better improve the performance of cross-modal hashing retrieval,this paper proposes a cross-modal dual hashing based on transfer knowledge(CDHTK).CDHTK performs cross-modal hashing retrieval tasks by combining an image network,a transfer knowledge network,and a text network.For the image modality,CDHTK combines the hash codes generated separately by the image network and the knowledge transfer network to generate discriminative hash codes.For the text modality,CDHTK fuses the hash codes generated separately by the text network and the knowledge transfer network to generate efficient hash codes.CDHTK employs a combination of cross-entropy loss for label prediction,joint triplet quantization loss for hash code gener-ation,and differential loss for transfer knowledge to jointly optimize the hash code generation process,thereby improving the retrieval performance of the model.Experiments on two commonly used data sets(IAPR TC-12,MIR-Flickr 25K)veri-fied the effectiveness of CDHTK,which outperforms the current state-of-the-art cross-modal hashing method ALECH(Adaptive Label correlation based asymmEtric Cross-modal Hashing)by 6.82%and 5.13%,respectively.关键词
跨模态/图像-文本检索/双重哈希/迁移知识Key words
cross-modal/image-text retrieval/dual hashing/transfer knowledge分类
信息技术与安全科学引用本文复制引用
钟建奇,林秋斌,曹文明..基于迁移知识的跨模态双重哈希[J].电子学报,2025,53(1):209-220,12.基金项目
国家自然科学基金(No.617714322) （No.617714322）
深圳市基础研究基金(No.JCYJ20220531100814033) National Natural Science Foundation of China(No.617714322) （No.JCYJ20220531100814033）
Fundamental Research Foun-dation of Shenzhen(No.JCYJ20220531100814033) （No.JCYJ20220531100814033）
