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Progressive fusion and guidance-aware denoising for robust multimodal sentiment analysis
Ziyu Zheng1, Xinfeng Ye2, Sathiamoorthy Manoharan2
1School of Computer Science, The University of Auckland, Auckland, 1010, New Zealand. zzhe232@aucklanduni.ac.nz.
Scientific Reports
|June 18, 2026
Summary
This study introduces a new framework for multimodal sentiment analysis, improving model performance by reducing data noise and redundancy. The Progressive Fusion and Guidance-Aware Denoising Framework (PFGAD) enhances representation capacity for better accuracy.
Area of Science:
- Artificial Intelligence
- Natural Language Processing
- Computer Vision
Background:
- Multimodal sentiment analysis is crucial for understanding digital media.
- Existing methods struggle with noise and redundancy in real-world data.
- Limited representational capacity hinders model performance.
Purpose of the Study:
- To propose a novel framework, Progressive Fusion and Guidance-Aware Denoising Framework (PFGAD), for robust multimodal sentiment analysis.
- To address limitations of existing fusion strategies by focusing on noise and redundancy reduction.
- To enhance the representational capacity of models for improved sentiment analysis.
Main Methods:
- Progressive fusion to learn joint multimodal representations.
- Variational information bottleneck to remove task-irrelevant noise.
- Guidance-Aware Denoising Module to recover potential information loss.
- Framework balances informativeness and robustness.
Main Results:
- Significant improvements on CMU-MOSI and CMU-MOSEI datasets.
- On CMU-MOSI: achieved Correlation (0.814) and Accuracy-2 (87.5%), outperforming prior results.
- On CMU-MOSEI: achieved Correlation (0.786) and F1-score (86.7%), showing relative gains.
Conclusions:
- PFGAD effectively reduces noise and redundancy in multimodal data.
- The proposed framework enhances sentiment analysis performance and robustness.
- PFGAD represents a significant advancement in multimodal sentiment analysis research.
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