Papers with Code 아카이브
Facial Recognition and Modelling
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Facial tasks in machine learning operate based on images or video frames (or other datasets) focussed on human faces.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Meta-Portrait/MetaPortrait
공식 코드MetaPortrait: Identity-Preserving Talking Head Generation with Fast Personalized Adaptation
https://github.com/Microsoft/Deep3DFaceReconstruction
공식 코드Accurate 3D Face Reconstruction with Weakly-Supervised Learning: From Single Image to Image Set
https://github.com/Microsoft/EdgeML
공식 코드RNNPool: Efficient Non-linear Pooling for RAM Constrained Inference
https://github.com/Microsoft/FERPlus
공식 코드Training Deep Networks for Facial Expression Recognition with Crowd-Sourced Label Distribution
https://github.com/Milan-BUAA/TSAN-brain-age-estimation
공식 코드Brain Age Estimation From MRI Using Cascade Networks with Ranking Loss
https://github.com/Mirevi/UCP-Framework
공식 코드https://github.com/MunchkinChen/FADING
공식 코드https://github.com/Mvrjustid/ACII19-Apex-Time-Network
공식 코드A Novel Apex-Time Network for Cross-Dataset Micro-Expression Recognition
https://github.com/NetEase-Media/ControlTalk
공식 코드Controllable Talking Face Generation by Implicit Facial Keypoints Editing
https://github.com/NetaGlazer/ADD-GP
공식 코드Few-Shot Speech Deepfake Detection Adaptation with Gaussian Processes
https://github.com/NetoPedro/FocusFace
공식 코드FocusFace: Multi-task Contrastive Learning for Masked Face Recognition
https://github.com/Nicholasli1995/VisualizingNDF
공식 코드https://github.com/NickyFot/HitchhikersGuide
공식 코드A Hitchhikers Guide to Fine-Grained Face Forgery Detection Using Common Sense Reasoning
https://github.com/Nithin-GK/UniteandConquer
공식 코드Unite and Conquer: Plug & Play Multi-Modal Synthesis using Diffusion Models
https://github.com/NuayHL/TimeStepGenerating
공식 코드Time Step Generating: A Universal Synthesized Deepfake Image Detector
https://github.com/OceanSummerDay/HSTA_MER
공식 코드Hierarchical Space-Time Attention for Micro-Expression Recognition
https://github.com/Open-Debin/Emotion-FAN
공식 코드Frame attention networks for facial expression recognition in videos
https://github.com/OpenImagingLab/LenslessFace
공식 코드LenslessFace: An End-to-End Optimized Lensless System for Privacy-Preserving Face Verification
https://github.com/OpenRL-Lab/DeepFakeFace
공식 코드Robustness and Generalizability of Deepfake Detection: A Study with Diffusion Models
https://github.com/OpenTAI/wild-deepfake
공식 코드WildDeepfake: A Challenging Real-World Dataset for Deepfake Detection
https://github.com/PRALabBiometrics/APhotoEverydayDB
공식 코드Are Adaptive Face Recognition Systems still Necessary? Experiments on the APE Dataset
https://github.com/PaddlePaddle/PaddleClas
공식 코드https://github.com/PaddlePaddle/models
공식 코드https://github.com/PalomaCantero/Deepfake-Detection-and-the-Impact-of-Limited-Computing-Capabilities
공식 코드Deepfake Detection and the Impact of Limited Computing Capabilities
https://github.com/PanosAntoniadis/emotion-gcn
공식 코드https://github.com/Pea-Shooter/ADNet
공식 코드ADNet: Attention-guided Deformable Convolutional Network for High Dynamic Range Imaging
https://github.com/Pei-KaiHuang/AAAI25-SLIP
공식 코드SLIP: Spoof-Aware One-Class Face Anti-Spoofing with Language Image Pretraining
https://github.com/Peihao-Xiang/MTCAE-DFER
공식 코드MTCAE-DFER: Multi-Task Cascaded Autoencoder for Dynamic Facial Expression Recognition
https://github.com/Powercoder64/TAA-GCN
공식 코드TAA-GCN: A Temporally Aware Adaptive Graph Convolutional Network for Age Estimation
https://github.com/Puspitamajumdariiit/Unravelling-the-Effect-of-Image-Distortions
공식 코드https://github.com/QingyuZhao/BR-Net
공식 코드Representation Learning with Statistical Independence to Mitigate Bias
https://github.com/RQuispeC/multi-stream-crowd-counting-extended
공식 코드Multi-Stream Networks and Ground-Truth Generation for Crowd Counting
https://github.com/RameshaKaru/devi
공식 코드DEVI: Open-source Human-Robot Interface for Interactive Receptionist Systems
https://github.com/Raschka-research-group/coral-cnn
공식 코드Rank consistent ordinal regression for neural networks with application to age estimation
https://github.com/Raul2718/FLUXSynID
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.