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/Mind23-2/MindCode-3/tree/main/HRNetW48_cls
Deep High-Resolution Representation Learning for Visual Recognition
https://github.com/Mind23-2/MindCode-3/tree/main/HRNetW48_seg
High-Resolution Representations for Labeling Pixels and Regions
https://github.com/Mind23-2/MindCode-3/tree/main/HiFaceGAN
HiFaceGAN: Face Renovation via Collaborative Suppression and Replenishment
https://github.com/Mind23-2/MindCode-3/tree/main/LightCNN
https://github.com/Mind23-2/MindCode-3/tree/main/MTCNN
Real-world adversarial attack on MTCNN face detection system
https://github.com/Mind23-2/MindCode-3/tree/main/MaskedFaceRecognition
https://github.com/Mind23-2/MindCode-4/tree/main/hrnet
Deep High-Resolution Representation Learning for Visual Recognition
https://github.com/Mind23-2/MindCode-41
https://github.com/Mind23-2/MindCode-5/tree/main/PGAN
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/Mind23-2/MindCode-5/tree/main/PyramidBox
PyramidBox++: High Performance Detector for Finding Tiny Face
https://github.com/Mind23-2/MindCode-56
https://github.com/Mind23-2/MindCode-59
Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks
https://github.com/Mind23-2/MindCode-70
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/Mind23-2/MindCode-71
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/Mind23-2/MindCode-88/tree/main/sphereface
SphereFace Revived: Unifying Hyperspherical Face Recognition
https://github.com/Mind23-2/MindCode-95
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/MindCode-4/code-11/tree/main/circle-loss
Circle Loss: A Unified Perspective of Pair Similarity Optimization
https://github.com/MindCode-4/code-14/tree/main/Zero-DCE%2B%2B
Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement
https://github.com/MindCode-4/code-5/tree/main/wavlm
https://github.com/MindCode-4/code-6/tree/main/circle-loss
Circle Loss: A Unified Perspective of Pair Similarity Optimization
https://github.com/MindSpore-paper-code-2/code2/tree/main/PGAN
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/MindSpore-paper-code-2/code2/tree/main/PyramidBox
PyramidBox++: High Performance Detector for Finding Tiny Face
https://github.com/MindSpore-paper-code-2/code3/tree/main/STPM
Deep Spatial Gradient and Temporal Depth Learning for Face Anti-spoofing
https://github.com/MindSpore-paper-code-3/code2/tree/main/DeepID
https://github.com/MindSpore-paper-code-3/code3/tree/main/FaceDetection
Face Detection with End-to-End Integration of a ConvNet and a 3D Model
https://github.com/MindSpore-paper-code-3/code3/tree/main/FaceRecognition
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/MindSpore-paper-code-3/code3/tree/main/FaceRecognitionForTracking
https://github.com/MindSpore-paper-code-3/code4/tree/main/HiFaceGAN
HiFaceGAN: Face Renovation via Collaborative Suppression and Replenishment
https://github.com/MindSpore-paper-code-3/code5/tree/main/retinanet_resnet101
https://github.com/MindSpore-paper-code-3/code5/tree/main/retinanet_resnet152
https://github.com/MindSpore-paper-code-3/code6/tree/main/ADNet
ADNet: Attention-guided Deformable Convolutional Network for High Dynamic Range Imaging
https://github.com/MindSpore-paper-code-3/code6/tree/main/cct
https://github.com/MindSpore-paper-code-3/code6/tree/main/cdp
Consensus-Driven Propagation in Massive Unlabeled Data for Face Recognition
https://github.com/MindSpore-paper-code-3/code6/tree/main/centerface
CenterFace: Joint Face Detection and Alignment Using Face as Point
https://github.com/MindSpore-paper-code-3/code7/tree/main/FaceDetection
Face Detection with End-to-End Integration of a ConvNet and a 3D Model
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.