Modality Discrepancy Transformer for Ambivalence and Hesitancy Recognition
Ambivalence and hesitancy (A/H) are affective states in which individuals express contradictory signals across facial, vocal, and linguistic channels. Automatically recognising A/H in clinical videos requires detecting cross-modal disagreement -- the signal that standard fusion methods suppress. Based on the conflict-aware multimodal fusion framework of Bekhouche et al., we present the Modality Discrepancy Transformer (MDT). MDT enriches the original 6-token design to a 9-token representation comprising three modality embeddings, three absolute-difference features, and three Hadamard-product discrepancy features learned through linear projections. These nine tokens undergo Transformer sel...
| Authors | Shiyu Luo, Yu Wang, Jiawen Huang et al. |
|---|---|
| Primary category | cs.CL |
| Published | 18 July 2026 |
| Last updated at source | 18 July 2026 |
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