Researchers have analyzed the effectiveness of SHAP-weighted cross-modal expert fusion ("xgaf") for emotion and sentiment recognition. The study found that using sum-abs reduction for SHAP attribution magnitudes, particularly when experts have unequal feature dimensionalities, preserves total attribution mass and leads to improved performance. This method nearly matches early fusion on the MELD emotion recognition task and slightly exceeds early fusion on the CMU-MOSEI sentiment recognition task, while significantly outperforming traditional late fusion. AI
IMPACT This research offers a more transparent and effective approach to multimodal fusion, potentially improving AI systems' ability to understand human emotion and sentiment.
RANK_REASON The cluster contains an academic paper detailing a new method for multimodal emotion and sentiment recognition.
- Adis Alihodzic
- CMU-MOSEI
- McNemar testing
- MELD
- Shap
- Transformer++
- TreeSHAP
- XAI-guided adaptive fusion
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