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Deep Information-Balanced Multimodal Learning
Summary
Multimodal learning faces challenges due to imbalanced data optimization. This study introduces Multimodal Information Balance (MIB) theory and an Information-Balanced Multimodal Learning (IBML) framework to ensure balanced information retention for improved perception.
Area of Science:
- Artificial Intelligence
- Machine Learning
- Information Theory
Background:
- Multimodal learning integrates diverse data for enhanced real-world understanding.
- Data discrepancies across modalities cause imbalanced optimization, hindering performance.
Purpose of the Study:
- To address imbalanced optimization in multimodal learning.
- To propose a theoretical framework for balancing complementary information retention across modalities.
Main Methods:
- Developed Multimodal Information Balance (MIB) theory and criterion.
- Introduced an Information-Balanced Multimodal Learning (IBML) framework.
- Incorporated Balance Information Optimization (BIO) and Task Complexity Modulation (TCM) modules.
Main Results:
- The MIB criterion adaptively balances complementary information preservation.
- IBML framework achieves comprehensive and balanced multimodal information mining.
- Experiments on eight datasets demonstrate IBML's superiority in audio-visual, image-text, and 2D-3D recognition.
Conclusions:
- The proposed MIB theory provides an explainable perspective on multimodal learning imbalances.
- IBML framework effectively enhances multimodal fusion and learning performance.
- The approach offers a robust solution for optimizing multimodal data integration.
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