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

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/Hesitancy (BAH) corpus confirm that text is the strongest unimodal modality. The Text Residual Fusion model achieves an average Macro F1-score (MF1) of 75.14% across the Development and Public Test subsets. On the Private Test subset, it reaches an MF1 of 78.24%, outperforming the text model by 4.03%. These results demonstrate that complementary multimodal information can improve recognition performance without requiring a large model ensemble.

Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)

arXiv:2607.14702 [cs.CV]

(or arXiv:2607.14702v2 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2607.14702 Focus to learn more arXiv-issued DOI via DataCite

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