Deep Multiscale Fusion Hashing for Cross-Modal Retrieval
| Autor: | Fanchang Hao, Xiushan Nie, Yilong Yin, Jiajia Li, Muwei Jian, Bowei Wang |
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| Rok vydání: | 2021 |
| Předmět: |
Computer science
business.industry Deep learning Hash function Relaxation (iterative method) 02 engineering and technology computer.software_genre Electronic mail Modal 0202 electrical engineering electronic engineering information engineering Media Technology Benchmark (computing) 020201 artificial intelligence & image processing Data mining Artificial intelligence Electrical and Electronic Engineering Layer (object-oriented design) business computer |
| Zdroj: | IEEE Transactions on Circuits and Systems for Video Technology. 31:401-410 |
| ISSN: | 1558-2205 1051-8215 |
| DOI: | 10.1109/tcsvt.2020.2974877 |
| Popis: | Owing to the rapid development of deep learning and the high efficiency of hashing, hashing methods based on deep learning models have been extensively adopted in the area of cross-modal retrieval. In general, in existing deep model-based methods, modality-specific features play an important role during the hash learning. However, most existing methods only use the modality-specific features from the final fully connected layer, ignoring the semantic relevance among modality-specific features with different scales in multiple layers. To address this issue, in this study, we put forward an end-to-end deep hashing method called deep multiscale fusion hashing (DMFH) for cross-modal retrieval. For the proposed DMFH, we first design different network branches for two modalities and then adopt multiscale fusion models for each branch network to fuse the multiscale semantics, which can be used to explore the semantic relevance. Furthermore, the multi-fusion models also embed the multiscale semantics into the final hash codes, making the final hash codes more representative. In addition, the proposed DMFH can learn common hash codes directly without a relaxation, thereby avoiding a loss in accuracy during hash learning. Experimental results on three benchmark datasets prove the relative superiority of the proposed method. |
| Databáze: | OpenAIRE |
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