Journal
IEEE TRANSACTIONS ON MULTIMEDIA
Volume 22, Issue 12, Pages 3101-3114Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TMM.2020.2969792
Keywords
Semantics; Correlation; Aircraft propulsion; Deep learning; Bridges; Aircraft; Task analysis; Cross-modal retrieval; adversarial hashing; multi-level correlation
Categories
Funding
- National Natural Science Foundation of China [61720106006, 61721004, 61832002, 61532009, U1705262, U1836220, 61702511, 61802053]
- Key Research Program of Frontier Sciences, CAS [QYZDJSSWJSC039]
- Research Program of National Laboratory of Pattern Recognition [Z-2018007]
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Cross-modal hashing (CMH) has been widely used for similarity search in multimedia retrieval applications, thanks to low storage cost and fast query speed. However, preserving the content similarities in finite-length hash codes between different data modalities is still challenging due to the existing heterogeneity gap. To further address the crucial bottleneck, we propose a Multi-Level Correlation Adversarial Hashing (MLCAH) algorithm to integrate the multi-level correlation information into hash codes. The proposed MLCAH model enjoys several merits. First, to the best of our knowledge, it is the early attempt of leveraging the multi-level correlation information for cross-modal hashing retrieval. Second, we propose global and local semantic alignment mechanisms, which can effectively encode multi-level correlation information, including global information, local information, and label information into hash codes. Third, a label-consistency attention mechanism with adversarial training is designed for exploiting the local cross-modality similarity from multi-modality data. Extensive evaluations on four benchmarks demonstrate that the proposed model brings significant improvements over several state-of-the-art cross-modal hashing methods.
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