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  3. A Dimensional Structure Based Knowledge Distillation Method For Cross-modal Learning.
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  3. A Dimensional Structure Based Knowledge Distillation Method For Cross-modal Learning.

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Cross-Modal Multivariate Pattern Analysis
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A dimensional structure based knowledge distillation method for cross-modal learning.

Lingyu Si1, Hongwei Dong1, Fan Yang2

  • 1Institute of Software, Chinese Academy of Sciences, China.

Neural Networks : the Official Journal of the International Neural Network Society
|February 10, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

This study reveals that dimensional structure (DS) of features correlates with their discriminability. A new DS-based knowledge distillation method improves cross-modal learning by guiding low-quality data using high-quality data's structural knowledge.

Keywords:
Cross-modal learningDatasetDimensional structureKnowledge distillation

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Cross-modal learning (CML) often relies on transferring knowledge from high-quality to low-quality data modalities.
  • The underlying reasons for the effectiveness of knowledge transfer in CML are not well understood.

Purpose of the Study:

  • To investigate the relationship between feature discriminability and dimensional structure (DS) in CML.
  • To propose a novel DS-based knowledge distillation method to enhance CML performance, particularly when data quality differs significantly.

Main Methods:

  • Analyzing the correlation between feature discriminability and DS by examining features from high- and low-quality modalities.
  • Expressing DS using spatial distribution of intermediate features and channel-wise correlation of output features.
  • Developing a DS-based knowledge distillation technique that enforces uniform intermediate feature distribution and channel-wise independence in the low-quality modality.

    • Main Results:

    • High-quality features exhibit superior DS compared to low-quality features.
    • The proposed DS-based knowledge distillation method significantly improves CML performance.
    • Introduced the IIS-ISCAS dataset for marine target recognition, featuring paired optics and radar modalities.

    Conclusions:

    • The dimensional structure of features is crucial for discriminability and effective knowledge transfer in CML.
    • DS-based knowledge distillation offers a promising approach to enhance CML, especially in scenarios with imbalanced data quality.
    • The IIS-ISCAS dataset will facilitate further research in CML for marine target recognition.