4 papers
Test-time Adaptive Hierarchical Co-enhanced Denoising Network for Reliable Multimodal Classification
Shu Shen, C. L. Philip Chen, Tong Zhang
Reliable learning of multimodal data (e.g., multi-omics) is a widely concerning issue, especially in safety-critical applications such as medical diagnosis. However, low-quality da…
AIM: Adaptive Intra-Network Modulation for Balanced Multimodal Learning
Shu Shen, C. L. Philip Chen, Tong Zhang
Multimodal learning has significantly enhanced machine learning performance but still faces numerous challenges and limitations. Imbalanced multimodal learning is one of the proble…
Reliable Multimodal Learning Via Multi-Level Adaptive DeConfusion
Tong Zhang, Shu Shen, C. L. Philip Chen
Multimodal learning enhances the performance of various machine learning tasks by leveraging complementary information across different modalities. However, existing methods often…
Multi-QuAD: Multi-Level Quality-Adaptive Dynamic Network for Reliable Multimodal Classification
Shu Shen, C. L. Philip Chen, Tong Zhang
Multimodal machine learning has achieved remarkable progress in many scenarios, but its reliability is undermined by varying sample quality. This paper finds that existing reliable…