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Facial Action Unit Detection
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Facial action unit detection is the task of detecting action units from a video of a face - for example, lip tightening and cheek raising.
<span style="color:grey; opacity: 0.6">( Image credit: Self-supervised Representation Learning from Videos for Facial Action Unit Detection )</span>
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
AU-TTT: Vision Test-Time Training model for Facial Action Unit Detection
Decoupled Doubly Contrastive Learning for Cross Domain Facial Action Unit Detection
Facial Action Unit Detection by Adaptively Constraining Self-Attention and Causally Deconfounding Sample
Towards Unified Facial Action Unit Recognition Framework by Large Language Models
Towards End-to-End Explainable Facial Action Unit Recognition via Vision-Language Joint Learning
Norface: Improving Facial Expression Analysis by Identity Normalization
Representation Learning and Identity Adversarial Training for Facial Behavior Understanding
Multi-scale Dynamic and Hierarchical Relationship Modeling for Facial Action Units Recognition
AUD-TGN: Advancing Action Unit Detection with Temporal Convolution and GPT-2 in Wild Audiovisual Contexts
Leveraging Synthetic Data for Generalizable and Fair Facial Action Unit Detection
GPT as Psychologist? Preliminary Evaluations for GPT-4V on Visual Affective Computing
AUFormer: Vision Transformers are Parameter-Efficient Facial Action Unit Detectors
Contrastive Learning of Person-independent Representations for Facial Action Unit Detection
Learning Contrastive Feature Representations for Facial Action Unit Detection
Distribution Matching for Multi-Task Learning of Classification Tasks: a Large-Scale Study on Faces & Beyond
Boosting Facial Action Unit Detection Through Jointly Learning Facial Landmark Detection and Domain Separation and Reconstruction
FG-Net: Facial Action Unit Detection with Generalizable Pyramidal Features
Multi-scale Promoted Self-adjusting Correlation Learning for Facial Action Unit Detection
Occlusion Aware Student Emotion Recognition based on Facial Action Unit Detection
Fighting over-fitting with quantization for learning deep neural networks on noisy labels
Multi-modal Facial Action Unit Detection with Large Pre-trained Models for the 5th Competition on Affective Behavior Analysis in-the-wild
Local Region Perception and Relationship Learning Combined with Feature Fusion for Facial Action Unit Detection
Self-supervised Facial Action Unit Detection with Region and Relation Learning
FAN-Trans: Online Knowledge Distillation for Facial Action Unit Detection
Facial Action Unit Detection and Intensity Estimation from Self-supervised Representation
Global-to-local Expression-aware Embeddings for Facial Action Unit Detection
Multimodal Channel-Mixing: Channel and Spatial Masked AutoEncoder on Facial Action Unit Detection
Learning Multi-dimensional Edge Feature-based AU Relation Graph for Facial Action Unit Recognition
Causal Intervention for Subject-Deconfounded Facial Action Unit Recognition
Weakly Supervised Regional and Temporal Learning for Facial Action Unit Recognition
Facial Action Unit Recognition Based on Transfer Learning
Facial Action Unit Recognition With Multi-models Ensembling
Multi-modal Multi-label Facial Action Unit Detection with Transformer
Multi-Order Networks for Action Unit Detection
Self-Supervised Regional and Temporal Auxiliary Tasks for Facial Action Unit Recognition
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