Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/164098
Title: Deep robust multilevel semantic hashing for multi-label cross-modal retrieval
Authors: Song, Ge
Tan, Xiaoyang
Zhao, Jun
Yang, Ming
Keywords: Engineering::Computer science and engineering
Issue Date: 2021
Source: Song, G., Tan, X., Zhao, J. & Yang, M. (2021). Deep robust multilevel semantic hashing for multi-label cross-modal retrieval. Pattern Recognition, 120, 108084-. https://dx.doi.org/10.1016/j.patcog.2021.108084
Journal: Pattern Recognition
Abstract: 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.
URI: https://hdl.handle.net/10356/164098
ISSN: 0031-3203
DOI: 10.1016/j.patcog.2021.108084
Rights: © 2021 Elsevier Ltd. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

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