New Framework Balances Modalities in Multimodal Sentiment Analysis
Researchers have rolled out a new approach called Conflict-aware Penalty and Statistical Loss to address the challenge of modality imbalance in Multimodal Sentiment Analysis (MSA). MSA combines text, visual, and acoustic data for sentiment identification, but often, the stronger text encoders can overshadow less powerful modalities, causing gradient norm conflicts. This new framework incorporates a Conflict-aware Penalty that detects and addresses these conflicts during training, alongside a Statistical Loss that aligns predicted outcomes with real-world statistics. By managing dominant modality influences, it promotes collaborative training using adaptive modality encoding and gated cross-modal fusion. Testing on the CMU-MOSI dataset has shown impressive results, and ablation studies confirm its effectiveness.
Key facts
- The framework addresses text modality dominance in multimodal sentiment analysis.
- Conflict-aware Penalty detects and penalizes gradient norm conflicts.
- Statistical Loss aligns predicted distribution statistics with empirical input statistics.
- The method prevents dominant modality gradients from interfering with the SL objective.
- The framework includes adaptive modality encoding, gated cross-modal fusion, and unimodal auxiliary heads.
- Experiments on CMU-MOSI dataset achieve state-of-the-art performance.
- Ablation studies confirm the effectiveness of each component.
- The paper is available on arXiv with ID 2605.28575.
Entities
Institutions
- arXiv