Hierarchical Granularity Alignment and State Space Modeling for Robust Multimodal AU Detection in the Wild
Hierarchical Granularity Alignment and State Space Modeling for Robust Multimodal AU Detection in the Wild explores A novel multimodal framework for robust Facial Action Unit detection leveraging advanced alignment and state space modeling techniques.. Commercial viability score: 7/10 in Multimodal Emotion Recognition.
BUILDER'S SANDBOX
Build This Paper
Use an AI coding agent to implement this research.
OpenAI CodexAI Agent
Lightweight coding agent in your terminal.
Claude CodeAI Agent
Agentic coding tool for terminal workflows.
AntiGravity IDEScaffolding
AI agent mindset installer and workflow scaffolder.
CursorIDE
AI-first code editor built on VS Code.
VS CodeIDE
Free, open-source editor by Microsoft.
Recommended Stack
Startup Essentials
Estimated $9K - $13K over 6-10 weeks.
See exactly what it costs to build this -- with 3 comparable funded startups.
7-day free trial. Cancel anytime.
Discover the researchers behind this paper and find similar experts.
7-day free trial. Cancel anytime.
Related Papers
- Builds On ThisTeam RAS in 10th ABAW Competition: Multimodal Valence and Arousal Estimation ApproachViability: 4.0
- Builds On ThisMultimodal Emotion Recognition via Bi-directional Cross-Attention and Temporal ModelingViability: 6.0
- Builds On ThisHSEmotion Team at ABAW-10 Competition: Facial Expression Recognition, Valence-Arousal Estimation, Action Unit Detection and Fine-Grained Violence ClassificationViability: 6.0
- Builds On ThisTeam LEYA in 10th ABAW Competition: Multimodal Ambivalence/Hesitancy Recognition ApproachViability: 4.0
- Prior Work