Team RAS in 11th ABAW Competition: Multimodal Ambivalence Recognition Approach

תקציר מקורי באנגליתarXiv:2607.14702v2 Announce Type: replace-cross Abstract: Automatic recognition of ambivalence and hesitancy is challenging because these states may be expressed through inconsistent linguistic, acoustic, facial, and contextual patterns, while top-performing systems often rely on computationally expensive ensembles. We present a single text-centered multimodal approach for video-level ambivalence and hesitancy recognition for the 11th Affective & Behavior Analysis in-the-Wild (ABAW) Challenge. The proposed approach combines linguistic, acoustic, facial, and scene features using text-centered multimodal fusion model. Text Residual Fusion treats text as the anchor modality and applies gated residual adjustments based on the other modalities. Experiments on the Behavioural Ambivalence/Hesitan