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Team LEYA in 10th ABAW Competition: Multimodal Ambivalence/Hesitancy Recognition Approach
arXiv – CS AI|Elena Ryumina (St. Petersburg Federal Research Center of the Russian Academy of Sciences, St. Petersburg, Russia), Alexandr Axyonov (St. Petersburg Federal Research Center of the Russian Academy of Sciences, St. Petersburg, Russia), Dmitry Sysoev (HSE University, St. Petersburg, Russia), Timur Abdulkadirov (HSE University, St. Petersburg, Russia), Kirill Almetov (HSE University, St. Petersburg, Russia), Yulia Morozova (HSE University, St. Petersburg, Russia), Dmitry Ryumin (St. Petersburg Federal Research Center of the Russian Academy of Sciences, St. Petersburg, Russia, HSE University, St. Petersburg, Russia)|
🤖AI Summary
Team LEYA developed a multimodal AI approach for recognizing ambivalence and hesitancy in videos for the 10th ABAW Competition, combining scene, facial, audio, and text analysis. Their fusion model achieved 83.25% accuracy compared to 70.02% for single-modality approaches, demonstrating significant improvements in behavioral recognition technology.
Key Takeaways
- →Multimodal fusion approach achieved 83.25% accuracy versus 70.02% for single-modality baselines in ambivalence/hesitancy recognition.
- →The system integrates four modalities: scene dynamics, facial emotions, acoustic features, and linguistic cues.
- →Final test performance reached 71.43% using an ensemble of five prototype-augmented fusion models.
- →Research demonstrates the importance of combining complementary data sources for complex behavioral recognition tasks.
- →The approach addresses challenges in analyzing subtle, context-dependent behavioral states in unconstrained video environments.
#multimodal-ai#computer-vision#behavioral-recognition#machine-learning#video-analysis#emotion-detection#ai-research#fusion-models
Read Original →via arXiv – CS AI
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