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| Format: | Preprint |
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2024
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| Online-Zugang: | https://arxiv.org/abs/2406.10776 |
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| _version_ | 1866917695244468224 |
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| author | Zhan, Yu-Wei Wu, Xiao-Ming Luo, Xin Wei, Yinwei Xu, Xin-Shun |
| author_facet | Zhan, Yu-Wei Wu, Xiao-Ming Luo, Xin Wei, Yinwei Xu, Xin-Shun |
| contents | In the real world, multi-modal data often appears in a streaming fashion, and there is a growing demand for similarity retrieval from such non-stationary data, especially at a large scale. In response to this need, online multi-modal hashing has gained significant attention. However, existing online multi-modal hashing methods face challenges related to the inconsistency of hash codes during long-term learning and inefficient fusion of different modalities. In this paper, we present a novel approach to supervised online multi-modal hashing, called High-level Codes, Fine-grained Weights (HCFW). To address these problems, HCFW is designed by its non-trivial contributions from two primary dimensions: 1) Online Hashing Perspective. To ensure the long-term consistency of hash codes, especially in incremental learning scenarios, HCFW learns high-level codes derived from category-level semantics. Besides, these codes are adept at handling the category-incremental challenge. 2) Multi-modal Hashing Aspect. HCFW introduces the concept of fine-grained weights designed to facilitate the seamless fusion of complementary multi-modal data, thereby generating multi-modal weights at the instance level and enhancing the overall hashing performance. A comprehensive battery of experiments conducted on two benchmark datasets convincingly underscores the effectiveness and efficiency of HCFW. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_10776 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | High-level Codes and Fine-grained Weights for Online Multi-modal Hashing Retrieval Zhan, Yu-Wei Wu, Xiao-Ming Luo, Xin Wei, Yinwei Xu, Xin-Shun Multimedia In the real world, multi-modal data often appears in a streaming fashion, and there is a growing demand for similarity retrieval from such non-stationary data, especially at a large scale. In response to this need, online multi-modal hashing has gained significant attention. However, existing online multi-modal hashing methods face challenges related to the inconsistency of hash codes during long-term learning and inefficient fusion of different modalities. In this paper, we present a novel approach to supervised online multi-modal hashing, called High-level Codes, Fine-grained Weights (HCFW). To address these problems, HCFW is designed by its non-trivial contributions from two primary dimensions: 1) Online Hashing Perspective. To ensure the long-term consistency of hash codes, especially in incremental learning scenarios, HCFW learns high-level codes derived from category-level semantics. Besides, these codes are adept at handling the category-incremental challenge. 2) Multi-modal Hashing Aspect. HCFW introduces the concept of fine-grained weights designed to facilitate the seamless fusion of complementary multi-modal data, thereby generating multi-modal weights at the instance level and enhancing the overall hashing performance. A comprehensive battery of experiments conducted on two benchmark datasets convincingly underscores the effectiveness and efficiency of HCFW. |
| title | High-level Codes and Fine-grained Weights for Online Multi-modal Hashing Retrieval |
| topic | Multimedia |
| url | https://arxiv.org/abs/2406.10776 |