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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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Hierarchical semantic interaction-based deep hashing network for cross-modal retrieval.

Shubai Chen1, Song Wu1, Li Wang2

  • 1College of Computer and Information Science, Southwest University, Chongqing, People's Republic of China.

Peerj. Computer Science
|June 18, 2021
PubMed
Summary

This study introduces a novel Hierarchical Semantic Interaction-based Deep Hashing Network (HSIDHN) for efficient large-scale cross-modal retrieval. The method enhances hash representations by exploring intermediate network layers and using dual-similarity measurements for multi-label data.

Keywords:
Bidirectional Bi-linear InteractionCross-Modal HashingDeep Neural NetworkDual-Similarity Measurement

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Deep hashing is effective for large-scale cross-modal retrieval.
  • Challenges remain in fine-grained multi-label similarity and intermediate layer information exploration.

Purpose of the Study:

  • To propose a novel Hierarchical Semantic Interaction-based Deep Hashing Network (HSIDHN).
  • To enhance cross-modal retrieval performance by addressing current challenges.

Main Methods:

  • Applying multi-scale and fusion operations to network layers.
  • Designing a Bidirectional Bi-linear Interaction (BBI) policy for hierarchical semantic interaction.
  • Implementing dual-similarity measurements (hard and soft) for semantic similarity.

Main Results:

  • The proposed HSIDHN enhances the capability of hash representations.
  • Dual-similarity measurement better preserves semantic correlation of multi-labels.
  • Experimental results show competitive performance against state-of-the-art methods.

Conclusions:

  • HSIDHN offers a promising approach for high-performance deep cross-modal hashing retrieval.
  • The method effectively handles fine-grained multi-label similarity and leverages intermediate network information.