https://github.com/jiaxin-ye/emo-dna
공식 코드Emo-DNA: Emotion Decoupling and Alignment Learning for Cross-Corpus Speech Emotion Recognition
Papers with Code 아카이브
Facial Expression Recognition (FER)
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Facial Expression Recognition (FER) is a computer vision task aimed at identifying and categorizing emotional expressions depicted on a human face. The goal is to automate the process of determining emotions in real-time, by analyzing the various features of a face such as eyebrows, eyes, mouth, and other features, and mapping them to a set of emotions such as anger, fear, surprise, sadness and happiness.
<span style="color:grey; opacity: 0.6">( Image credit: DeXpression )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/jlee118/nprm
공식 코드https://github.com/jlrex/patt-lite
공식 코드PAtt-Lite: Lightweight Patch and Attention MobileNet for Challenging Facial Expression Recognition
https://github.com/jungyu0413/Navigating-Label-Ambiguity-NLA-
공식 코드Navigating Label Ambiguity for Facial Expression Recognition in the Wild
https://github.com/jungyu0413/V-NAW
공식 코드V-NAW: Video-based Noise-aware Adaptive Weighting for Facial Expression Recognition
https://github.com/jymoon0613/simfle
공식 코드https://github.com/kaiwang960112/Challenge-condition-FER-dataset
공식 코드Region Attention Networks for Pose and Occlusion Robust Facial Expression Recognition
https://github.com/kaiwang960112/Self-Cure-Network
공식 코드Suppressing Uncertainties for Large-Scale Facial Expression Recognition
https://github.com/kdhht2334/elim_fer
공식 코드Optimal Transport-based Identity Matching for Identity-invariant Facial Expression Recognition
https://github.com/leeisack/latent-ofer
공식 코드https://github.com/liuhw01/Norface
공식 코드Norface: Improving Facial Expression Analysis by Identity Normalization
https://github.com/liuyvchi/mie-x
공식 코드How to Synthesize a Large-Scale and Trainable Micro-Expression Dataset?
https://github.com/lupaopao/gpt4affectivity
공식 코드GPT as Psychologist? Preliminary Evaluations for GPT-4V on Visual Affective Computing
https://github.com/miccunifi/nefer
공식 코드https://github.com/minhnhatvt/label-distribution-learning-fer-tf
공식 코드Uncertainty-aware Label Distribution Learning for Facial Expression Recognition
https://github.com/mrkoujan/DeepFaceFlow
공식 코드https://github.com/mrkoujan/FER
공식 코드Real-time Facial Expression Recognition "In The Wild'' by Disentangling 3D Expression from Identity
https://github.com/msa-lmc/s2d
공식 코드https://github.com/msy1412/abaw4
공식 코드AU-Supervised Convolutional Vision Transformers for Synthetic Facial Expression Recognition
https://github.com/muse1998/IAL-for-Facial-Expression-Recognition
공식 코드Intensity-Aware Loss for Dynamic Facial Expression Recognition in the Wild
https://github.com/muse1998/mmnet
공식 코드MMNet: Muscle motion-guided network for micro-expression recognition
https://github.com/negarhdr/diversified-facial-expression-recognition
공식 코드Learning Diversified Feature Representations for Facial Expression Recognition in the Wild
https://github.com/nickyfot/emoclip
공식 코드EmoCLIP: A Vision-Language Method for Zero-Shot Video Facial Expression Recognition
https://github.com/nii-yamagishilab/AfriHuBERT
공식 코드AfriHuBERT: A self-supervised speech representation model for African languages
https://github.com/o-ikne/emotion-gan
공식 코드https://github.com/omidmnezami/Engagement-Recognition
공식 코드Automatic Recognition of Student Engagement using Deep Learning and Facial Expression
https://github.com/osamazeeshan/subject-based-domain-adaptation-for-fer
공식 코드Subject-Based Domain Adaptation for Facial Expression Recognition
https://github.com/pablovin/AffectiveMemoryFramework
공식 코드The FaceChannel: A Light-weight Deep Neural Network for Facial Expression Recognition
https://github.com/pedrodiamel/ferattention
공식 코드https://github.com/purpleleaves007/ferdenoise
공식 코드ReSup: Reliable Label Noise Suppression for Facial Expression Recognition
https://github.com/ryanhe312/LatentFace
공식 코드A Generative Framework for Self-Supervised Facial Representation Learning
https://github.com/samir-atra/emotion_estimation_from_video_footage_with_lstm_ml_algorithm
공식 코드https://github.com/sashankmvv/Emotion-recognition
공식 코드Facial Emotion Recognition: A multi-task approach using deep learning
https://github.com/sbelharbi/interpretable-fer-aus
공식 코드Guided Interpretable Facial Expression Recognition via Spatial Action Unit Cues
https://github.com/shuchaoduan/qafe-net
공식 코드QAFE-Net: Quality Assessment of Facial Expressions with Landmark Heatmaps
https://github.com/shuvenduroy/activefer
공식 코드Active Learning with Contrastive Pre-training for Facial Expression Recognition
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.