https://github.com/xxtars/emo-llama
공식 코드EMO-LLaMA: Enhancing Facial Emotion Understanding with Instruction Tuning
Papers with Code 아카이브
Facial Expression Recognition
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/yagguc/deep_impression
공식 코드https://github.com/yao-papercodes/aglrls
공식 코드https://github.com/yaoing/dan
공식 코드Distract Your Attention: Multi-head Cross Attention Network for Facial Expression Recognition
https://github.com/yelboudouri/EmoNeXt
공식 코드https://github.com/youqingxiaozhua/apvit
공식 코드Vision Transformer with Attentive Pooling for Robust Facial Expression Recognition
https://github.com/yyx-future/la-cmfer
공식 코드https://github.com/zczcwh/poster
공식 코드POSTER: A Pyramid Cross-Fusion Transformer Network for Facial Expression Recognition
https://github.com/zengqunzhao/dfer-clip
공식 코드Prompting Visual-Language Models for Dynamic Facial Expression Recognition
https://github.com/zengqunzhao/exp-clip
공식 코드Enhancing Zero-Shot Facial Expression Recognition by LLM Knowledge Transfer
https://github.com/zerosansan/fg2020_abaw
공식 코드Deep Convolutional Neural Network Based Facial Expression Recognition in the Wild
https://github.com/zfkarl/leaf
공식 코드LEAF: Unveiling Two Sides of the Same Coin in Semi-supervised Facial Expression Recognition
https://github.com/zhelishisongjie/ssfer
공식 코드https://github.com/zouxinyi0625/cdnet
공식 코드https://github.com/zyh-uaiaaaa/erasing-attention-consistency
공식 코드Learn From All: Erasing Attention Consistency for Noisy Label Facial Expression Recognition
https://github.com/zyh-uaiaaaa/generalizable-fer
공식 코드https://github.com/2023-MindSpore-4/Code8/tree/main/cct
https://github.com/AmrElsersy/Emotions-Recognition
https://github.com/Conero007/Emotion-Detection-Pytorch
Challenges in Representation Learning: A report on three machine learning contests
https://github.com/Hamiedamr/EmotionNet-Nano-An-Efficient-Deep-Convolutional-Neural-Network-Design-for-Real-time-Facial-Expressi
https://github.com/KLT20/Realtime-Face-Emotion-Recognition
Deep-Emotion: Facial Expression Recognition Using Attentional Convolutional Network
https://github.com/MaxLikesMath/DeepLearningImplementations
DeXpression: Deep Convolutional Neural Network for Expression Recognition
https://github.com/MindSpore-paper-code-3/code6/tree/main/cct
https://github.com/NJNischal/Facial-Expression-Recognition-with-CNNs
Challenges in Representation Learning: A report on three machine learning contests
https://github.com/NPilis/Facial-Expression-Recognition
https://github.com/NathanHouwaart/EmotionRecognition
Training Deep Networks for Facial Expression Recognition with Crowd-Sourced Label Distribution
https://github.com/RainbowRui/Landmark-Driven-Facial-Expression-Recognition
Suppressing Uncertainties for Large-Scale Facial Expression Recognition
https://github.com/Recognito-Vision/Linux-FaceRecognition-FaceLivenessDetection
https://github.com/SeongwoongJo/Multimodal-Emotional-Recognition
Frame attention networks for facial expression recognition in videos
https://github.com/ShiqiYu/libfacedetection
https://github.com/WhiTExB3AR/Emotion_Diary
Facial Motion Prior Networks for Facial Expression Recognition
https://github.com/WhiTExB3AR/PreProduceCode-FMPN-FER
Facial Motion Prior Networks for Facial Expression Recognition
https://github.com/anshu123priya/Real-v-s-Fake-Emotion-Challenge
MicroExpNet: An Extremely Small and Fast Model For Expression Recognition From Face Images
https://github.com/anshu123priya/Real-vs-Fake-Emotion-Challenge
MicroExpNet: An Extremely Small and Fast Model For Expression Recognition From Face Images
https://github.com/apuayush/face_express
Facial Expression Recognition using Convolutional Neural Networks: State of the Art
https://github.com/av-savchenko/emotiefflib
Frame-level Prediction of Facial Expressions, Valence, Arousal and Action Units for Mobile Devices
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.