AUFormer: Parameter-Efficient Facial Action Unit Detectors
Facial Action Units (AU) detection is enhanced by the innovative PETL paradigm, MoKE collaboration mechanism, and MDWA-Loss in AUFormer.
Advancing Action Unit Detection with Temporal Convolution and GPT-2 in Wild Audiovisual Contexts
Utilizing temporal convolution and GPT-2 enhances AU detection accuracy by integrating audio-visual data for nuanced emotional expression understanding.
AUFormer: Vision Transformers for Parameter-Efficient Facial Action Unit Detection
AUFormer introduces a Parameter-Efficient Transfer Learning paradigm for efficient AU detection, showcasing state-of-the-art performance without relying on additional data.
Contrastive Feature Representations for Improved Facial Action Unit Detection
A novel contrastive learning framework that incorporates both self-supervised and supervised signals to enhance the learning of discriminative features for accurate facial action unit detection, addressing challenges such as class imbalance and noisy labels.
Facial Action Unit Detection by Adaptively Constraining Self-Attention and Causally Deconfounding Sample Characteristics
The proposed AC2D framework adaptively constrains the self-attention weight distribution and causally deconfounds the sample confounder to improve facial action unit detection performance.
