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/laohur/RelationNet
https://github.com/laoreja/face-identity-transformer
https://github.com/laoshiwei/face-anti-spoofing
Exploiting temporal and depth information for multi-frame face anti-spoofing
https://github.com/laowang666888/HRNET
High-Resolution Representations for Labeling Pixels and Regions
https://github.com/larsoncs/face_detect
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/layumi/Person_reID_baseline_pytorch
Circle Loss: A Unified Perspective of Pair Similarity Optimization
https://github.com/leebebeto/pytorch-implementation
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/leejieun51/transformer
https://github.com/leemathew1998/GradientWeight
https://github.com/leemathew1998/RG
https://github.com/leenaali1114/Hierarchical-Image-Clustering---Unsupervised-Learning
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/leob03/E2E_Object_Pose_Estimator
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/leoluopy/GroupFacePytorch
GroupFace: Learning Latent Groups and Constructing Group-based Representations for Face Recognition
https://github.com/leondgarse/Keras_insightface
MagFace: A Universal Representation for Face Recognition and Quality Assessment
https://github.com/leongatys/DeepTextures
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/leongatys/PytorchNeuralStyleTransfer
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/leorrose/Facial-Expression-Recognition
Challenges in Representation Learning: A report on three machine learning contests
https://github.com/leorrose/Social-Network-Facial-Expression-Recognition
Challenges in Representation Learning: A report on three machine learning contests
https://github.com/letthedataconfess/Keras-triplet-loss-mnist
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/levindaniil/FaceNet_FaceRecognition
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/lhcezx/Graph-FPN
https://github.com/li-yibing/oceannet
https://github.com/libreai/neural-painters-x
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/lidio601/node-facedetect-supyo
Object Detection with Pixel Intensity Comparisons Organized in Decision Trees
https://github.com/lijiannuist/lightDSFD
https://github.com/likelyzhao/peron_attribute
MOON: A Mixed Objective Optimization Network for the Recognition of Facial Attributes
https://github.com/linainsaf/RESNET50_PNEUMONIA_DETECTION
https://github.com/linghu8812/pytorch_insightface
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/linghu8812/tensorrt_tracker
FastReID: A Pytorch Toolbox for General Instance Re-identification
https://github.com/lingzhang1/pointnet_pytorch
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/lingzhang1/pointnet_tensorflow
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/linuxsino/self-adjusting-au
Multi-scale Promoted Self-adjusting Correlation Learning for Facial Action Unit Detection
https://github.com/lippman1125/pytorch_FAN
https://github.com/liqichen6688/duo-attention
https://github.com/liuch37/pointnet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.