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/yangyucheng000/Paper-2/tree/main/ARCM-main
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/yangyucheng000/Paper-4/tree/main/JLONMFSC
Optimal Projections for Discriminative Dictionary Learning using the JL-lemma
https://github.com/yangyucheng000/Paper-4/tree/main/LADS-mindspore
LAD-RCNN:A Powerful Tool for Livestock Face Detection and Normalization
https://github.com/yangyucheng000/Paper-4/tree/main/PDOC_mindspore
SFace: Sigmoid-Constrained Hypersphere Loss for Robust Face Recognition
https://github.com/yangyucheng000/RetinaFace
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/yangyucheng000/paper
CenterFace: Joint Face Detection and Alignment Using Face as Point
https://github.com/yangyucheng000/papercode-2/tree/main/JLONMFSC-master
Optimal Projections for Discriminative Dictionary Learning using the JL-lemma
https://github.com/yangyucheng000/papercode-2/tree/main/LADS-mindspore
LAD-RCNN:A Powerful Tool for Livestock Face Detection and Normalization
https://github.com/yangyucheng000/papercode-2/tree/main/SFAT-MindSpore-main
https://github.com/yangyucheng000/papercode-2/tree/main/TAA_MindSpore-main
TAA-GCN: A Temporally Aware Adaptive Graph Convolutional Network for Age Estimation
https://github.com/yangyucheng000/pfld
https://github.com/yangyucheng000/resnet_Ascend
https://github.com/yangyucheng000/sphereface
https://github.com/yanqi1811/attention-is-all-you-need
https://github.com/yashclone999/ResNet_MODEL
https://github.com/yashraj02/RetinaFace
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/yaxingwang/MineGAN
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/ycao5602/SAL
https://github.com/ycxioooong/MovieSynopsisAssociation
Unifying Identification and Context Learning for Person Recognition
https://github.com/yczhang1017/SSD_resnet_pytorch
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/yehengchen/FaceNet-FaceRecognition
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/yehengchen/FaceRecognition
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/yehengchen/FaceRecognition-FaceNet
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/yehengchen/SmartCar-FaceRecognition
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/yellowfighter2333/ticket-machine-with-face-recognition
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/yellowfighter2333/ticket-mechine-with-face-recognition
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/yerfor/geneface
GeneFace: Generalized and High-Fidelity Audio-Driven 3D Talking Face Synthesis
https://github.com/yfeng95/deca
VGGFace2: A dataset for recognising faces across pose and age
https://github.com/yfsin/deepfake_pro
DeepFaceLab: Integrated, flexible and extensible face-swapping framework
https://github.com/yh-iro/Keras-TP-GAN
https://github.com/yhenon/pytorch-retinanet
https://github.com/yibeiou/Advanced_Business_Intelligence
https://github.com/yifeizhou02/bt-2
https://github.com/yijiaceline/Final-Project-Group4
https://github.com/yijiazh/DFER_Summer2019
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.