Team LEYA in 10th ABAW Competition: Multimodal Ambivalence/Hesitancy Recognition Approach

• URL: http://arxiv.org/abs/2603.12848v1
• Date: Fri, 13 Mar 2026 09:50:03 GMT
• Title: Team LEYA in 10th ABAW Competition: Multimodal Ambivalence/Hesitancy Recognition Approach
• Abstract summary: This paper presents a multimodal approach for ambivalence/hesitancy recognition in unconstrained videos.<n>The proposed approach integrates four complementary modalities: scene, face, audio, and text.<n>Experiments on the BAH corpus demonstrate clear gains of multimodal fusion over all unimodal baselines.
• Score: 43.09390014734485
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Ambivalence/hesitancy recognition in unconstrained videos is a challenging problem due to the subtle, multimodal, and context-dependent nature of this behavioral state. In this paper, a multimodal approach for video-level ambivalence/hesitancy recognition is presented for the 10th ABAW Competition. The proposed approach integrates four complementary modalities: scene, face, audio, and text. Scene dynamics are captured with a VideoMAE-based model, facial information is encoded through emotional frame-level embeddings aggregated by statistical pooling, acoustic representations are extracted with EmotionWav2Vec2.0 and processed by a Mamba-based temporal encoder, and linguistic cues are modeled using fine-tuned transformer-based text models. The resulting unimodal embeddings are further combined using multimodal fusion models, including prototype-augmented variants. Experiments on the BAH corpus demonstrate clear gains of multimodal fusion over all unimodal baselines. The best unimodal configuration achieved an average MF1 of 70.02%, whereas the best multimodal fusion model reached 83.25%. The highest final test performance, 71.43%, was obtained by an ensemble of five prototype-augmented fusion models. The obtained results highlight the importance of complementary multimodal cues and robust fusion strategies for ambivalence/hesitancy recognition.

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