https://github.com/1adrianb/unsupervised-face-representation
공식 코드Pre-training strategies and datasets for facial representation learning
Papers with Code 아카이브
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>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/BCV-Uniandes/AUNets
공식 코드https://github.com/ZhenghLi/Automated-Detection-of-Equine-Facial-Action-Units
공식 코드https://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/cvi-szu/mdhr
공식 코드Multi-scale Dynamic and Hierarchical Relationship Modeling for Facial Action Units Recognition
https://github.com/cvi-szu/me-graphau
공식 코드Learning Multi-dimensional Edge Feature-based AU Relation Graph for Facial Action Unit Recognition
https://github.com/eglxiang/FacialAU
공식 코드Linear Disentangled Representation Learning for Facial Actions
https://github.com/eglxiang/icassp15_emotion
공식 코드Linear Disentangled Representation Learning for Facial Actions
https://github.com/envision-research/gpt4affectivity
공식 코드GPT as Psychologist? Preliminary Evaluations for GPT-4V on Visual Affective Computing
https://github.com/forever208/fmae-iat
공식 코드Representation Learning and Identity Adversarial Training for Facial Behavior Understanding
https://github.com/ihp-lab/fg-net
공식 코드FG-Net: Facial Action Unit Detection with Generalizable Pyramidal Features
https://github.com/liuhw01/Norface
공식 코드Norface: Improving Facial Expression Analysis by Identity Normalization
https://github.com/lupaopao/gpt4affectivity
공식 코드GPT as Psychologist? Preliminary Evaluations for GPT-4V on Visual Affective Computing
https://github.com/nxsEdson/MLCR
공식 코드Multi-label Co-regularization for Semi-supervised Facial Action Unit Recognition
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/yuankaishen2001/auformer
공식 코드AUFormer: Vision Transformers are Parameter-Efficient Facial Action Unit Detectors
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/AlexHex7/DRML_pytorch
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/tomas-gajarsky/facetorch
Pre-training strategies and datasets for facial representation learning
https://github.com/wtomin/A-Multitask-Solution-for-FAU-EXPR-VA
https://github.com/zkl20061823/DRML
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.