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ARXIV:2603.12848 · MULTIMODAL RECOGNITION · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.12848MULTIMODAL RECOGNITIONSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
A multimodal approach for recognizing ambivalence and hesitancy in videos using integrated scene, facial, audio, and text analysis.
Opportunity summary
Pain A multimodal approach for recognizing ambivalence and hesitancy in videos using integrated scene, facial, audio, and text analysis.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
A multimodal approach for recognizing ambivalence and hesitancy in videos using integrated scene, facial, audio, and text analysis. In this paper, a multimodal approach for video-level ambivalence/hesitancy recognition is presented for the 10th ABAW…
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…
ScienceToStartup currently rates this 4.0/10 on the public viability pass. Experiments on the BAH corpus demonstrate clear gains of multimodal fusion over all unimodal baselines.
Multimodal Recognition moved forward this cycle; last verified April 2026. Public score 4.0/10.
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A multimodal approach for recognizing ambivalence and hesitancy in videos using integrated scene, facial, audio, and text analysis.
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10.48550/arXiv.2603.12848A multimodal approach for recognizing ambivalence and hesitancy in videos using integrated scene, facial, audio, and text analysis.
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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unverified0 refs; 0 sources; 17% coverage.
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Dimensions overall score 4.0
PROBLEM
A multimodal approach for recognizing ambivalence and hesitancy in videos using integrated scene, facial, audio, and text analysis. In this paper, a multimodal approach for video-level ambivalence/hesitancy recognition is presented for the 10th ABAW Competition.
METHOD
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 10...
RESULT
ScienceToStartup currently rates this 4.0/10 on the public viability pass. Experiments on the BAH corpus demonstrate clear gains of multimodal fusion over all unimodal baselines.
WHY NOW
Multimodal Recognition moved forward this cycle; last verified April 2026. Public score 4.0/10.
Abstract-backed public claims while anchored extraction refreshes.
A multimodal approach for recognizing ambivalence and hesitancy in videos using integrated scene, facial, audio, and text analysis. In this paper, a multimodal approach for video-level ambivalence/hesitancy recognition is presented for the 10th ABAW Competition.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 4.0/10 on the public viability pass. Experiments on the BAH corpus demonstrate clear gains of multimodal fusion over all unimodal baselines.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Multimodal Recognition moved forward this cycle; last verified April 2026. Public score 4.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Concepts
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A multimodal approach for recognizing ambivalence and hesitancy in videos using integrated scene, facial, audio, and text analysis.
Segment
Multimodal Recognition
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Commercial read
4.0/10 public viability
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Technical feasibility
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