https://github.com/Garsonjw/AdvFAS
공식 코드AdvFAS: A robust face anti-spoofing framework against adversarial examples
Papers with Code 아카이브
Facial Recognition and Modelling
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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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/HCIILAB/SCUT-FBP5500-Database-Release
공식 코드SCUT-FBP5500: A Diverse Benchmark Dataset for Multi-Paradigm Facial Beauty Prediction
https://github.com/HCPLab-SYSU/AUE-CRL
공식 코드AU-Expression Knowledge Constrained Representation Learning for Facial Expression Recognition
https://github.com/HCPLab-SYSU/CD-FER-Benchmark
공식 코드Adversarial Graph Representation Adaptation for Cross-Domain Facial Expression Recognition
https://github.com/HDCVLab/EDFace-Celeb-1M
공식 코드EDFace-Celeb-1M: Benchmarking Face Hallucination with a Million-scale Dataset
https://github.com/HSE-asavchenko/face-emotion-recognition
공식 코드https://github.com/HSE-asavchenko/mobile-visual-preferences
공식 코드https://github.com/Hangz-nju-cuhk/Rotate-and-Render
공식 코드Rotate-and-Render: Unsupervised Photorealistic Face Rotation from Single-View Images
https://github.com/Hangz-nju-cuhk/Talking-Face_PC-AVS
공식 코드Pose-Controllable Talking Face Generation by Implicitly Modularized Audio-Visual Representation
https://github.com/HanxunH/Unlearnable-Examples
공식 코드https://github.com/Helix-IIIT-Delhi/COFFE-Singing_Voice_Deepfake
공식 코드Towards Source Attribution of Singing Voice Deepfake with Multimodal Foundation Models
https://github.com/HeyingGithub/Baseline-project-for-MEGC2020_spotting
공식 코드Spotting Macro- and Micro-expression Intervals in Long Video Sequences
https://github.com/HongwenZhang/ECT-FaceAlignment
공식 코드Combining Data-driven and Model-driven Methods for Robust Facial Landmark Detection
https://github.com/HuangYG123/CurricularFace
공식 코드CurricularFace: Adaptive Curriculum Learning Loss for Deep Face Recognition
https://github.com/HuangYG123/DDL
공식 코드Improving Face Recognition from Hard Samples via Distribution Distillation Loss
https://github.com/Hzzone/MTLFace
공식 코드When Age-Invariant Face Recognition Meets Face Age Synthesis: A Multi-Task Learning Framework
https://github.com/Hzzone/PFA-GAN
공식 코드PFA-GAN: Progressive Face Aging with Generative Adversarial Network
https://github.com/IDT-ITI/Adv-XAI-Deepfakes
공식 코드https://github.com/InterDigitalInc/HRFAE
공식 코드https://github.com/IrvingMeng/MagFace
공식 코드MagFace: A Universal Representation for Face Recognition and Quality Assessment
https://github.com/JDAI-CV/FaceX-Zoo/tree/main/addition_module/DMUE
공식 코드https://github.com/JDAI-CV/faceX-Zoo
공식 코드https://github.com/JDAI-CV/fast-reid
공식 코드FastReID: A Pytorch Toolbox for General Instance Re-identification
https://github.com/JFChi/Understanding-and-Mitigating-Accuracy-Disparity-in-Regression
공식 코드Understanding and Mitigating Accuracy Disparity in Regression
https://github.com/JOY-MM/JoyGen
공식 코드JoyGen: Audio-Driven 3D Depth-Aware Talking-Face Video Editing
https://github.com/JRConti/EthicalModule_vMF
공식 코드Mitigating Gender Bias in Face Recognition Using the von Mises-Fisher Mixture Model
https://github.com/JStehouwer/FFD_CVPR2020
공식 코드https://github.com/Jasonxu1225/Improved-Lightweight-YOLOv5-for-Face-Mask-Detection
공식 코드An Improved Lightweight YOLOv5 Model Based on Attention Mechanism for Face Mask Detection
https://github.com/Jessegator/Audio_robustness_evaluation
공식 코드https://github.com/Jia1018/GoHD
공식 코드https://github.com/JianZhang-chick/EMOdiffhead
공식 코드EMOdiffhead: Continuously Emotional Control in Talking Head Generation via Diffusion
https://github.com/JianghaiSCU/Diffusion-Low-Light
공식 코드Low-Light Image Enhancement with Wavelet-based Diffusion Models
https://github.com/JiejiangWu/FaceG2E
공식 코드https://github.com/Juyong/CaricatureFace
공식 코드Landmark Detection and 3D Face Reconstruction for Caricature using a Nonlinear Parametric Model
https://github.com/Juyong/FacePSNet
공식 코드https://github.com/KIT-MRT/latent_diffusion_face_anonymization
공식 코드LDFA: Latent Diffusion Face Anonymization for Self-driving Applications
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.