Abstract
Hash learning has been a great success in large-scale data retrieval field because of its superior retrieval efficiency and storage consumption. However, labels for large-scale data are difficult to obtain, thus supervised learning-based hashing methods are no longer applicable. In this paper, we introduce a method called Semi-Supervised Semantic Adaptive Cross-modal Hashing (S3ACH), which improves performance of unsupervised hash retrieval by exploiting a small amount of available label information. Specifically, we first propose a higher-order dynamic weight public space collaborative computing method, which balances the contribution of different modalities in the common potential space by invoking adaptive higher-order dynamic variable. Then, less available label information is utilized to enhance the semantics of hash codes. Finally, we propose a discrete optimization strategy to solve the quantization error brought by the relaxation strategy and improve the accuracy of hash code production. The results show that S3ACH achieves better effects than current advanced unsupervised methods and provides more applicable while balancing performance compared with the existing cross-modal hashing.
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Acknowledgements
This work is supported in part by the National Natural Science Foundation of China under the Grant No.62202501, No.62172451 and No.U2003208, in part by the National Key R &D Program of China under Grant No.2021YFB3900902, in part by the Science and Technology Plan of Hunan Province under Grant No.2022JJ40638 and in part by Open Research Projects of Zhejiang Lab under the Grant No.2022KG0AB01.
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Yang, L., Zhang, K., Li, Y., Chen, Y., Long, J., Yang, Z. (2024). S3ACH: Semi-Supervised Semantic Adaptive Cross-Modal Hashing. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Lecture Notes in Computer Science, vol 14450. Springer, Singapore. https://doi.org/10.1007/978-981-99-8070-3_20
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