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

• URL: http://arxiv.org/abs/2607.14702v2
• Date: Tue, 21 Jul 2026 07:24:19 GMT
• Title: Team RAS in 11th ABAW Competition: Multimodal Ambivalence Recognition Approach
• Abstract summary: We present a single text-centered multimodal approach for video-level ambivalence and hesitancy recognition.<n>The proposed approach combines linguistic, acoustic, facial, and scene features using text-centered multimodal fusion model.<n>Experiments on the Behavioural Ambivalence/Hesitancy (BAH) corpus confirm that text is the strongest unimodal modality.
• Score: 52.92973028971813
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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