https://github.com/MSA-LMC/VAEmo
공식 코드VAEmo: Efficient Representation Learning for Visual-Audio Emotion with Knowledge Injection
Papers with Code 아카이브
Video Emotion Recognition
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Peihao-Xiang/MTCAE-DFER
공식 코드MTCAE-DFER: Multi-Task Cascaded Autoencoder for Dynamic Facial Expression Recognition
https://github.com/Peihao-Xiang/MultiMAE-DFER
공식 코드MultiMAE-DER: Multimodal Masked Autoencoder for Dynamic Emotion Recognition
https://github.com/julieeF/CS231N-Project
공식 코드Emotion Recognition in Audio and Video Using Deep Neural Networks
https://github.com/qlwei89/fv2es
공식 코드FV2ES: A Fully End2End Multimodal System for Fast Yet Effective Video Emotion Recognition Inference
https://github.com/wtomin/MIMAMO-Net
공식 코드MIMAMO Net: Integrating Micro- and Macro-motion for Video Emotion Recognition
https://github.com/xuecwu/emotions
공식 코드Towards Emotion Analysis in Short-form Videos: A Large-Scale Dataset and Baseline
https://github.com/HKUST-NISL/MIMAMO-Net
MIMAMO Net: Integrating Micro- and Macro-motion for Video Emotion Recognition
https://github.com/madhurchhajed/Facial-Emotion-Recognition
Technical Report for Valence-Arousal Estimation on Affwild2 Dataset
https://github.com/mink007/Emotion_Recog_Deep_Neural_Networks/blob/master/CS231N_Project_Presentation.pptx
Emotion Recognition in Audio and Video Using Deep Neural Networks
https://github.com/nku-zhichengzhang/wecl
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.