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Action Unit Detection
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Action unit detection is the task of detecting action units from a video - for example, types of facial action units (lip tightening, cheek raising) from a video of a face.
<span style="color:grey; opacity: 0.6">( Image credit: AU R-CNN )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/HSE-asavchenko/face-emotion-recognition
공식 코드EmotiEffNet Facial Features in Uni-task Emotion Recognition in Video at ABAW-5 competition
https://github.com/ZhiwenShao/ADLD
공식 코드Unconstrained Facial Action Unit Detection via Latent Feature Domain
https://github.com/ZhiwenShao/PyTorch-JAANet
공식 코드J$\hat{\text{A}}$A-Net: Joint Facial Action Unit Detection and Face Alignment via Adaptive Attention
https://github.com/dkollias/fair-consistent-affect-analysis
공식 코드Rethinking Affect Analysis: A Protocol for Ensuring Fairness and Consistency
https://github.com/ihp-lab/fg-net
공식 코드FG-Net: Facial Action Unit Detection with Generalizable Pyramidal Features
https://github.com/sharpstill/AU_R-CNN
공식 코드AU R-CNN: Encoding Expert Prior Knowledge into R-CNN for Action Unit Detection
https://github.com/wtomin/Multitask-Emotion-Recognition-with-Incomplete-Labels
공식 코드https://github.com/yuankaishen2001/Self-adjusting-AU
공식 코드Multi-scale Promoted Self-adjusting Correlation Learning for Facial Action Unit Detection
https://github.com/zhiwenshao/ac2d
공식 코드https://github.com/ziqiao-shang/aunce
공식 코드Learning Contrastive Feature Representations for Facial Action Unit Detection
https://github.com/AmirSh15/FECNet
https://github.com/GerardLiu96/FECNet
https://github.com/HKUST-NISL/Multitask-Emotion-Recognition-with-Incomplete-Labels
https://github.com/ZhiwenShao/JAANet
Deep Adaptive Attention for Joint Facial Action Unit Detection and Face Alignment
https://github.com/linuxsino/self-adjusting-au
Multi-scale Promoted Self-adjusting Correlation Learning for Facial Action Unit Detection
https://github.com/machanic/AU_R-CNN
AU R-CNN: Encoding Expert Prior Knowledge into R-CNN for Action Unit Detection
https://github.com/wtomin/A-Multitask-Solution-for-FAU-EXPR-VA
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.