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<p>Deep hashing technology has gained widespread attention due to its low storage cost and high retrieval efficiency. Although existing cross-modal hashing methods have achieved good retrieval results, there is still the problem of facing modal heterogeneity leading to semantic separation. Therefore, this paper proposes a deep hashing method based on prototype-aware hardness-weighted, which uses the CLIP model after fine-tuning the large language model as a feature extractor to better obtain the feature information of the two modalities. And a set of loss functions are designed to better handle difficult samples by prototype-aware hardness-weighted loss to ensure that the data can be embedded in the appropriate location, bringing relevant data closer and pulling irrelevant data away. After conducting experiments on three datasets and comparing with some advanced cross-modal retrieval methods in recent years, it can be shown that our proposed DPHS method has excellent performance. The code and datasets used in this article can be obtained from <a href="https://github.com/chmy180/dphs-main">https://github.com/chmy180/dphs-main</a>.</p>

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Deep hashing with prototype-aware hardness-weighted for supervised cross-modal retrieval

  • 期刊论文

  • 发表日期：

    2025年9月9日

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  • Bin Ge,
  • Mengyan Cheng,
  • Chenxing Xia

摘要

Deep hashing technology has gained widespread attention due to its low storage cost and high retrieval efficiency. Although existing cross-modal hashing methods have achieved good retrieval results, there is still the problem of facing modal heterogeneity leading to semantic separation. Therefore, this paper proposes a deep hashing method based on prototype-aware hardness-weighted, which uses the CLIP model after fine-tuning the large language model as a feature extractor to better obtain the feature information of the two modalities. And a set of loss functions are designed to better handle difficult samples by prototype-aware hardness-weighted loss to ensure that the data can be embedded in the appropriate location, bringing relevant data closer and pulling irrelevant data away. After conducting experiments on three datasets and comparing with some advanced cross-modal retrieval methods in recent years, it can be shown that our proposed DPHS method has excellent performance. The code and datasets used in this article can be obtained from https://github.com/chmy180/dphs-main.