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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2502.19674 |
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Table of Contents:
- Multimodal learning enhances the performance of various machine learning tasks by leveraging complementary information across different modalities. However, existing methods often learn multimodal representations that retain substantial inter-class confusion, making it difficult to achieve high-confidence predictions, particularly in real-world scenarios with low-quality or noisy data. To address this challenge, we propose Multi-Level Adaptive DeConfusion (MLAD), which eliminates inter-class confusion in multimodal data at both global and sample levels, significantly enhancing the classification reliability of multimodal models. Specifically, MLAD first learns class-wise latent distributions with global-level confusion removed via dynamic-exit modality encoders that adapt to the varying discrimination difficulty of each class and a cross-class residual reconstruction mechanism. Subsequently, MLAD further removes sample-specific confusion through sample-adaptive cross-modality rectification guided by confusion-free modality priors. These priors are constructed from low-confusion modality features, identified by evaluating feature confusion using the learned class-wise latent distributions and selecting those with low confusion via a Gaussian mixture model. Experiments demonstrate that MLAD outperforms state-of-the-art methods across multiple benchmarks and exhibits superior reliability.