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S3ACH: Semi-Supervised Semantic Adaptive Cross-Modal Hashing

EasyChair Preprint no. 11136

18 pagesDate: October 23, 2023


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 However, labels for large-scale data are difficult to obtain, thus supervised learning-based hashing methods are no longer applicable.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.

Keyphrases: cross-modal retrieval, Hashing, semi-supervised

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Liu Yang and Kaiting Zhang and Yinan Li and Yunfei Chen and Jun Long and Zhan Yang},
  title = {S3ACH: Semi-Supervised Semantic Adaptive Cross-Modal Hashing},
  howpublished = {EasyChair Preprint no. 11136},

  year = {EasyChair, 2023}}
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