Papers with Code 아카이브
Facial Expression Recognition
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://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/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/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/patrickstarl/sit-fer
공식 코드https://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/sayeh1994/facial-expression-recognition
공식 코드How far generated data can impact Neural Networks performance?
https://github.com/sayeh1994/synthesizin_facial_expression
공식 코드How far generated data can impact Neural Networks performance?
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
https://github.com/shuvenduroy/ssl_fer
공식 코드Analysis of Semi-Supervised Methods for Facial Expression Recognition
https://github.com/shuvenduroy/ssl_fer_ood
공식 코드https://github.com/sridharsola/msd-e
공식 코드https://github.com/starhiking/ExpLLM-TMM
공식 코드ExpLLM: Towards Chain of Thought for Facial Expression Recognition
https://github.com/sunlicai/mae-dfer
공식 코드MAE-DFER: Efficient Masked Autoencoder for Self-supervised Dynamic Facial Expression Recognition
https://github.com/sunlicai/svfap
공식 코드https://github.com/swjtuma/fer-yolo-mamba
공식 코드FER-YOLO-Mamba: Facial Expression Detection and Classification Based on Selective State Space
https://github.com/takihasan/arbex
공식 코드ARBEx: Attentive Feature Extraction with Reliability Balancing for Robust Facial Expression Learning
https://github.com/talented-q/ifer-master
공식 코드More comprehensive facial inversion for more effective expression recognition
https://github.com/talented-q/poster_v2
공식 코드POSTER++: A simpler and stronger facial expression recognition network
https://github.com/taylor-xy0827/Ada-DF
공식 코드Ada-DF: An Adaptive Label Distribution Fusion Network For Facial Expression Recognition
https://github.com/thorbenortmann/emohevrdb-sfer
공식 코드https://github.com/thorbenortmann/emoji-hero-vr-database
공식 코드https://github.com/tub-cv-group/conclugen
공식 코드Multi-Task Multi-Modal Self-Supervised Learning for Facial Expression Recognition
https://github.com/wangyanckxx/surveyfer
공식 코드A Survey on Facial Expression Recognition of Static and Dynamic Emotions
https://github.com/wrudman/NOTICE
공식 코드https://github.com/xiaoyaoxinyi/m2se
공식 코드EmoVerse: Exploring Multimodal Large Language Models for Sentiment and Emotion Understanding
https://github.com/xinqi-fan/ABAW2021
공식 코드Spatial and Temporal Networks for Facial Expression Recognition in the Wild Videos
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.