Papers with Code 아카이브
Facial Recognition and Modelling
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 25
- 평가표
- 0
- 보존된 평가 기록
- 0
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/BradyFU/DVG
공식 코드Dual Variational Generation for Low-Shot Heterogeneous Face Recognition
https://github.com/Carlyx/DPE
공식 코드DPE: Disentanglement of Pose and Expression for General Video Portrait Editing
https://github.com/ChenqiKONG/OSDFD
공식 코드Open-Set Deepfake Detection: A Parameter-Efficient Adaptation Method with Forgery Style Mixture
https://github.com/ClaudiaShu/SSL-FER
공식 코드Revisiting Self-Supervised Contrastive Learning for Facial Expression Recognition
https://github.com/Clench/TAR_resAE
공식 코드TAR: Generalized Forensic Framework to Detect Deepfakes using Weakly Supervised Learning
https://github.com/CodeShareBot/Micro-Attention-for-Micro-Expression
공식 코드https://github.com/ControlNet/LAV-DF
공식 코드https://github.com/ControlNet/MARLIN
공식 코드MARLIN: Masked Autoencoder for facial video Representation LearnINg
https://github.com/Coral79/CDDB
공식 코드A Continual Deepfake Detection Benchmark: Dataset, Methods, and Essentials
https://github.com/CreativePapers/Learn-to-Synthesize-and-Synthesize-to-Learn
공식 코드https://github.com/D-X-Y/SAN
공식 코드https://github.com/D-X-Y/landmark-detection
공식 코드Teacher Supervises Students How to Learn From Partially Labeled Images for Facial Landmark Detection
https://github.com/Darg-Iztech/gender-prediction-from-tweets
공식 코드Gender Prediction from Tweets: Improving Neural Representations with Hand-Crafted Features
https://github.com/DariusAf/MesoNet
공식 코드https://github.com/DengPingFan/Scoot
공식 코드Face Sketch Synthesis Style Similarity:A New Structure Co-occurrence Texture Measure
https://github.com/DeokyunKim/Progressive-Face-Super-Resolution
공식 코드Progressive Face Super-Resolution via Attention to Facial Landmark
https://github.com/DetionDX/Attribute2Sketch2Face
공식 코드Face Synthesis from Visual Attributes via Sketch using Conditional VAEs and GANs
https://github.com/DetionDX/GP-GAN-Gender-Preserving-GAN-for-Synthesizing-Faces-from-Landmarks
공식 코드GP-GAN: Gender Preserving GAN for Synthesizing Faces from Landmarks
https://github.com/DetionDX/GP-GAN-GenderPreserving-GAN-for-Synthesizing-Faces-from-Landmarks
공식 코드GP-GAN: Gender Preserving GAN for Synthesizing Faces from Landmarks
https://github.com/Dianezzy/ParaLip
공식 코드https://github.com/DingXiaoH/RepMLP
공식 코드RepMLP: Re-parameterizing Convolutions into Fully-connected Layers for Image Recognition
https://github.com/Drkun/ZSTCI
공식 코드https://github.com/ESanchezLozano/GANnotation
공식 코드Triple consistency loss for pairing distributions in GAN-based face synthesis
https://github.com/EduardaCaldeira/DiffProb
공식 코드https://github.com/EndlessSora/DeeperForensics-1.0
공식 코드DeeperForensics-1.0: A Large-Scale Dataset for Real-World Face Forgery Detection
https://github.com/EricGzq/AMTENnet
공식 코드Fake face detection via adaptive manipulation traces extraction network
https://github.com/FER-LMC/S2D
공식 코드https://github.com/FaceOnLive/Face-Recognition-SDK-Android
공식 코드Cross-Age Contrastive Learning for Age-Invariant Face Recognition
https://github.com/FacePerceiver/FaRL
공식 코드General Facial Representation Learning in a Visual-Linguistic Manner
https://github.com/Faceplugin-ltd/FaceLivenessDetection-Android
공식 코드Generalized Face Liveness Detection via De-fake Face Generator
https://github.com/FeiiYin/StyleHEAT
공식 코드StyleHEAT: One-Shot High-Resolution Editable Talking Face Generation via Pre-trained StyleGAN
https://github.com/Fictionarry/InsTaG
공식 코드InsTaG: Learning Personalized 3D Talking Head from Few-Second Video
https://github.com/FuxiCV/3D-Face-GCNs
공식 코드https://github.com/FuxiCV/MeInGame
공식 코드MeInGame: Create a Game Character Face from a Single Portrait
https://github.com/FuxiVirtualHuman/Write-a-Speaker
공식 코드Write-a-speaker: Text-based Emotional and Rhythmic Talking-head Generation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.