Deep robust multilevel semantic hashing for multi-label cross-modal retrieval
Hashing based cross-modal retrieval has recently made significant progress. But straightforward embedding data from different modalities involving rich semantics into a joint Hamming space will inevitably produce false codes due to the intrinsic modality discrepancy and noises. We present a novel d...
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| Main Authors: | , , , |
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| 其他作者: | |
| 格式: | Article |
| 語言: | English |
| 出版: |
2023
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| 主題: | |
| 在線閱讀: | https://hdl.handle.net/10356/164098 |
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| 機構: | Nanyang Technological University |
| 語言: | English |
| 總結: | Hashing based cross-modal retrieval has recently made significant progress. But straightforward embedding data from different modalities involving rich semantics into a joint Hamming space will inevitably
produce false codes due to the intrinsic modality discrepancy and noises. We present a novel deep Robust Multilevel Semantic Hashing (RMSH) for more accurate multi-label cross-modal retrieval. It seeks to
preserve fine-grained similarity among data with rich semantics,i.e., multi-label, while explicitly require
distances between dissimilar points to be larger than a specific value for strong robustness. For this, we
give an effective bound of this value based on the information coding-theoretic analysis, and the above
goals are embodied into a margin-adaptive triplet loss. Furthermore, we introduce pseudo-codes via fusing multiple hash codes to explore seldom-seen semantics, alleviating the sparsity problem of similarity
information. Experiments on three benchmarks show the validity of the derived bounds, and our method
achieves state-of-the-art performance. |
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