Proceedings

View-Association-Guided Dynamic Multi-View Classification

Xinyan Liang, Li Lv, Qian Guo, Bingbing Jiang, Feijiang Li, Liang Du, Lu Chen

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Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence. Main Track. Pages 5680-5688.

https://doi.org/10.24963/ijcai.2025/632

Keywords

  • Machine Learning: ML: Classification
  • Machine Learning: ML: Multi-modal learning
  • Machine Learning: ML: Multi-view learning

Abstract

In multi-view classification tasks, integrating information from multiple views effectively is crucial for improving model performance. However, most existing methods fail to fully leverage the complex relationships between views, often treating them independently or using static fusion strategies. In this paper, we propose a View-Association-Guided Dynamic Multi-View Classification method (AssoDMVC) to address these limitations. Our approach dynamically models and incorporates the relationships between different views during the classification process. Specifically, we introduce a view-relation-guided mechanism that captures the dependencies and interactions between views, allowing for more flexible and adaptive feature fusion. This dynamic fusion strategy ensures that each view contributes optimally based on its contextual relevance and the inter-view relationships. Extensive experiments on multiple benchmark datasets demonstrate that our method outperforms traditional multi-view classification techniques, offering a more robust and efficient solution for tasks involving complex multi-view data.

Xinyan Liang, Li Lv, Qian Guo, Bingbing Jiang, Feijiang Li, Liang Du, and Lu Chen. 2025. View-Association-Guided Dynamic Multi-View Classification. In Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25. IJCAI, 5680–5688. https://doi.org/10.24963/ijcai.2025/632
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