Abstract
With the development of Internet technology, unimodal retrieval techniques are no longer suitable for the current environment, and mutual retrieval between multiple modalities is needed to obtain more complete information. Deep hashing has clearly become a simpler and faster method in cross-modal hashing. In recent years, unsupervised cross-modal hashing has received increasing attention. However, existing methods fail to exploit the common information across modalities, thus resulting in information wastage. In this paper, we propose a new critical separation cross-modal hashing (CSCH) for unsupervised cross-modal retrieval, which explores the similarity information across modalities by highlighting the similarity between instances to help the network learn the hash function, and we carefully design the loss function by introducing the likelihood loss commonly used in supervised learning into the loss function. Extensive experiments on two cross-modal retrieval datasets show that CSCH has better performance.
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Acknowledgement
This work was supported in part by the National Natural Science Foundation of China under Grant 62071157, National Key Research and Development Programme 2022YFD2000500 and Natural Science Foundation of Heilongjiang Province under Grant YQ2019F011.
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© 2023 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Wang, Z., Sun, Y., Liu, L., Li, A. (2023). Critical Separation Hashing for Cross-Modal Retrieval. In: Li, A., Shi, Y., Xi, L. (eds) 6GN for Future Wireless Networks. 6GN 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 504. Springer, Cham. https://doi.org/10.1007/978-3-031-36011-4_15
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DOI: https://doi.org/10.1007/978-3-031-36011-4_15
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