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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2406.18007 |
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| _version_ | 1866911933420011520 |
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| author | Zhu, Jian Zou, Xin Cui, Yu Huang, Zhangmin Hu, Chenshu Lyu, Bo |
| author_facet | Zhu, Jian Zou, Xin Cui, Yu Huang, Zhangmin Hu, Chenshu Lyu, Bo |
| contents | Inspired by the excellent performance of Mamba networks, we propose a novel Deep Mamba Multi-modal Learning (DMML). It can be used to achieve the fusion of multi-modal features. We apply DMML to the field of multimedia retrieval and propose an innovative Deep Mamba Multi-modal Hashing (DMMH) method. It combines the advantages of algorithm accuracy and inference speed. We validated the effectiveness of DMMH on three public datasets and achieved state-of-the-art results. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_18007 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Deep Mamba Multi-modal Learning Zhu, Jian Zou, Xin Cui, Yu Huang, Zhangmin Hu, Chenshu Lyu, Bo Multimedia Inspired by the excellent performance of Mamba networks, we propose a novel Deep Mamba Multi-modal Learning (DMML). It can be used to achieve the fusion of multi-modal features. We apply DMML to the field of multimedia retrieval and propose an innovative Deep Mamba Multi-modal Hashing (DMMH) method. It combines the advantages of algorithm accuracy and inference speed. We validated the effectiveness of DMMH on three public datasets and achieved state-of-the-art results. |
| title | Deep Mamba Multi-modal Learning |
| topic | Multimedia |
| url | https://arxiv.org/abs/2406.18007 |