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3D Facial Expression Recognition
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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3D facial expression recognition is the task of modelling facial expressions in 3D from an image or video.
<span style="color:grey; opacity: 0.6">( Image credit: Expression-Net )</span>
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
DrFER: Learning Disentangled Representations for 3D Facial Expression Recognition
0/1 Deep Neural Networks via Block Coordinate Descent
AFNet-M: Adaptive Fusion Network with Masks for 2D+3D Facial Expression Recognition
2D+3D facial expression recognition via embedded tensor manifold regularization
MFEViT: A Robust Lightweight Transformer-based Network for Multimodal 2D+3D Facial Expression Recognition
Deep and Shallow Covariance Feature Quantization for 3D Facial Expression Recognition
2D+3D Facial Expression Recognition via Discriminative Dynamic Range Enhancement and Multi-Scale Learning
Accurate Facial Parts Localization and Deep Learning for 3D Facial Expression Recognition
ExpNet: Landmark-Free, Deep, 3D Facial Expressions
Local Shape Spectrum Analysis for 3D Facial Expression Recognition
Deep Representation of Facial Geometric and Photometric Attributes for Automatic 3D Facial Expression Recognition
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