Core Concepts
Utilizing temporal convolution and GPT-2 enhances AU detection accuracy by integrating audio-visual data for nuanced emotional expression understanding.
Abstract
Abstract
Integrating audio and visual data is crucial for understanding human emotions.
Proposed method enhances AU detection accuracy by leveraging multimodal data.
Introduction
AUs are fundamental for expressing emotions, challenging to detect accurately in uncontrolled environments.
Traditional methods limited adaptability to diverse facial expressions.
Method
Preprocessing video into audio and visual streams initiates AU detection process.
TCN captures temporal dynamics efficiently, enhancing model's ability.
Data Extraction
"Our method achieves good performance (53.7%) on the official validation set."
Experiment
Aff-Wild2 dataset provides comprehensive annotated data for affective behavior analysis.
Training details include fine-tuning iResNet network and optimizing learning rate schedule.
Results
Model performance evaluated using F1 scores on the official validation set, showcasing significant improvements with different components.
Conclusion
Integration of TCN, pre-trained models like iResNet and GPT-2, improves AU detection accuracy significantly.
Stats
"Our method achieves good performance (53.7%) on the official validation set."