Two-Stage Multimodal Framework for Emotion Mimicry Intensity Prediction
📰 ArXiv cs.AI
Learn to predict emotion mimicry intensity using a two-stage multimodal framework combining text, audio, and video features
Action Steps
- Collect and preprocess multimodal video clips with textual, acoustic, and visual features
- Train modality-specific models for each feature type
- Combine the outputs of each modality-specific model using a fusion strategy
- Optionally, incorporate a motion branch to capture dynamic features
- Evaluate the performance of the framework on a benchmark dataset such as Hume-ABAW10
Who Needs to Know This
Machine learning engineers and affective computing researchers can benefit from this framework to improve emotion recognition and intensity prediction in multimodal data
Key Insight
💡 A two-stage multimodal framework can effectively combine textual, acoustic, and visual features to predict emotion mimicry intensity
Share This
🤖 Predict emotion mimicry intensity with a two-stage multimodal framework! 📹💬
Key Takeaways
Learn to predict emotion mimicry intensity using a two-stage multimodal framework combining text, audio, and video features
Full Article
Title: Two-Stage Multimodal Framework for Emotion Mimicry Intensity Prediction
Abstract:
arXiv:2605.21869v1 Announce Type: cross Abstract: We present our submission to the Hume-ABAW10 Emotional Mimicry Intensity (EMI) Challenge, which aims to predict six continuous emotion intensity dimensions: Admiration, Amusement, Determination, Empathic Pain, Excitement, and Joy, from in-the-wild multimodal video clips. We propose a staged multimodal framework that combines textual, acoustic, and visual representations, with an optional motion branch. Our approach first trains modality-specific
Abstract:
arXiv:2605.21869v1 Announce Type: cross Abstract: We present our submission to the Hume-ABAW10 Emotional Mimicry Intensity (EMI) Challenge, which aims to predict six continuous emotion intensity dimensions: Admiration, Amusement, Determination, Empathic Pain, Excitement, and Joy, from in-the-wild multimodal video clips. We propose a staged multimodal framework that combines textual, acoustic, and visual representations, with an optional motion branch. Our approach first trains modality-specific
DeepCamp AI