深度跨模态检索的语义分解和增强哈希

IF 7.5 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Lunke Fei , Zhihao He , Wai Keung Wong , Qi Zhu , Shuping Zhao , Jie Wen
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引用次数: 0

摘要

深度哈希已经获得了相当大的兴趣,并在检索领域显示出令人印象深刻的性能。然而,目前大多数哈希技术仅依赖于二元相似性评价标准来评估多标签实例之间的语义关系,这在克服各种模式之间的特征差距方面提出了挑战。在本文中,我们提出了语义分解和增强哈希(SDEH),通过广泛探索不同模态共享的多标签语义信息进行跨模态检索。具体来说,我们首先引入了两个独立的基于注意力的特征学习子网,以捕获具有全局和局部细节的模态特定特征。随后,我们通过分解多模态特征之间共享的语义信息来挖掘多标签向量的语义特征,从而建立不同模态之间的关联。最后,我们在四重损失的指导下共同学习了多模态信息的常见哈希码表示,使哈希码在保持多层语义关系和特征分布一致性的同时具有信息量。在四种常用的多模态数据集上进行的综合实验为我们提出的SDEH的卓越有效性提供了强有力的支持。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Semantic decomposition and enhancement hashing for deep cross-modal retrieval
Deep hashing has garnered considerable interest and has shown impressive performance in the domain of retrieval. However, the majority of the current hashing techniques rely solely on binary similarity evaluation criteria to assess the semantic relationships between multi-label instances, which presents a challenge in overcoming the feature gap across various modalities. In this paper, we propose semantic decomposition and enhancement hashing (SDEH) by extensively exploring the multi-label semantic information shared by different modalities for cross-modal retrieval. Specifically, we first introduce two independent attention-based feature learning subnetworks to capture the modality-specific features with both global and local details. Subsequently, we exploit the semantic features from multi-label vectors by decomposing the shared semantic information among multi-modal features such that the associations of different modalities can be established. Finally, we jointly learn the common hash code representations of multimodal information under the guidelines of quadruple losses, making the hash codes informative while simultaneously preserving multilevel semantic relationships and feature distribution consistency. Comprehensive experiments on four commonly used multimodal datasets offer strong support for the exceptional effectiveness of our proposed SDEH.
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来源期刊
Pattern Recognition
Pattern Recognition 工程技术-工程:电子与电气
CiteScore
14.40
自引率
16.20%
发文量
683
审稿时长
5.6 months
期刊介绍: The field of Pattern Recognition is both mature and rapidly evolving, playing a crucial role in various related fields such as computer vision, image processing, text analysis, and neural networks. It closely intersects with machine learning and is being applied in emerging areas like biometrics, bioinformatics, multimedia data analysis, and data science. The journal Pattern Recognition, established half a century ago during the early days of computer science, has since grown significantly in scope and influence.
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