https://github.com/clarence-cv/fecd
공식 코드DIG-FACE: De-biased Learning for Generalized Facial Expression Category Discovery
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/cross-innovation-lab/rdfer
공식 코드https://github.com/cruiseyugh/gus
공식 코드https://github.com/d-acharya/CovPoolFER
공식 코드https://github.com/donydchen/FMPN-FER
공식 코드Facial Motion Prior Networks for Facial Expression Recognition
https://github.com/dreammr/pcl
공식 코드Pose-disentangled Contrastive Learning for Self-supervised Facial Representation
https://github.com/envision-research/gpt4affectivity
공식 코드GPT as Psychologist? Preliminary Evaluations for GPT-4V on Visual Affective Computing
https://github.com/fengyuentau/pytorch-flops
공식 코드https://github.com/ftmghorbani/EVI-HRnet
공식 코드Expression-aware video inpainting for HMD removal in XR applications
https://github.com/fvmassoli/affwild2-challenge
공식 코드A Multi-resolution Approach to Expression Recognition in the Wild
https://github.com/fvmassoli/mafer-multires-facial-expression-recognition
공식 코드MAFER: a Multi-resolution Approach to Facial Expression Recognition
https://github.com/gabrella/qot
공식 코드Quaternion Orthogonal Transformer for Facial Expression Recognition in the Wild
https://github.com/gsethan17/CAPNet_ABAW2021
공식 코드Causal affect prediction model using a facial image sequence
https://github.com/hangyu94/ada-cm
공식 코드Towards Semi-Supervised Deep Facial Expression Recognition with An Adaptive Confidence Margin
https://github.com/hengxyz/Dynamic_multi-task-learning
공식 코드Dynamic Multi-Task Learning for Face Recognition with Facial Expression
https://github.com/ihp-lab/libreface
공식 코드LibreFace: An Open-Source Toolkit for Deep Facial Expression Analysis
https://github.com/irisdominguez/dataset-bias-metrics
공식 코드https://github.com/irisdominguez/dataset_bias_metrics
공식 코드Metrics for Dataset Demographic Bias: A Case Study on Facial Expression Recognition
https://github.com/irisdominguez/dsap
공식 코드DSAP: Analyzing Bias Through Demographic Comparison of Datasets
https://github.com/jiarenchang/facecycle
공식 코드Learning Facial Representations from the Cycle-consistency of Face
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/katerynaCh/MMA-DFER
공식 코드https://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/liaolea/emoverse
공식 코드EmoVerse: Exploring Multimodal Large Language Models for Sentiment and Emotion Understanding
https://github.com/lupaopao/gpt4affectivity
공식 코드GPT as Psychologist? Preliminary Evaluations for GPT-4V on Visual Affective Computing
https://github.com/lxq1000/swinface
공식 코드https://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 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.