A Conflict-Aware Penalty and Statistical Loss Framework for Balancing Modalities and Enhancing Stability in Multimodal Sentiment Analysis
arXiv:2605.28575v1 Announce Type: new Abstract: Multimodal Sentiment Analysis (MSA) fuses text, acoustic, and visual streams to infer sentiment. Because pre-trained text encoders are far more expressive than their acoustic and visual counterparts, the text modality tends to dominate optimization, suppressing weaker modalities and inducing gradient norm conflicts that destabilize training. To address this, we propose a Conflict-aware Penalty (CP) that detects and penalizes gradient norm conflicts at each training step, and a Statistical Loss (SL) that aligns predicted distribution statistics wi
The paper addresses a known limitation in current multimodal AI systems where textual data often overpowers other modalities due to more advanced pre-trained models, a problem becoming increasingly evident as more multimodal applications emerge.
Improving the balance and stability in multimodal AI training will lead to more robust and accurate systems, which directly impacts the reliability and performance of AI applications across various industries.
This framework offers a method to mitigate the dominance of text in multimodal sentiment analysis, potentially leading to more equally weighted contributions from acoustic and visual data, and more stable AI model development.
- · AI developers
- · Multimodal AI applications
- · Sentiment analysis providers
- · AI hardware manufacturers
- · Less sophisticated multimodal AI models
- · Companies relying solely on textual analysis
Multimodal AI models will exhibit improved performance and stability, leading to more reliable sentiment analysis and other 'understanding' tasks.
Enhanced multimodal capabilities could accelerate the development of more human-like AI agents that better interpret complex human communication cues.
More balanced multimodal AI may foster a new generation of interfaces and applications less reliant on explicit textual input, broadening AI accessibility and utility.
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Read at arXiv cs.AI