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/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/ReIDStrongBaseline
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/MindSpore-paper-code-3/code5/tree/main/resnet50_adv_pruning
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/FaceAttribute
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
https://github.com/MindSpore-paper-code-3/code7/tree/main/FaceRecognition
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/MindSpore-paper-code-3/code7/tree/main/FaceRecognitionForTracking
https://github.com/MindSpore-paper-code-3/code7/tree/main/faceboxes
https://github.com/MindSpore-paper-code-3/code8/tree/main/DeepID
https://github.com/MindSpore-paper-code-3/code8/tree/main/FaceAttribute
https://github.com/MindSpore-paper-code-3/code8/tree/main/FaceDetection
Face Detection with End-to-End Integration of a ConvNet and a 3D Model
https://github.com/MindSpore-paper-code-3/code8/tree/main/FaceRecognition
https://github.com/MindSpore-paper-code-3/code8/tree/main/FaceRecognitionForTracking
https://github.com/MindSpore-paper-code-3/code8/tree/main/faceboxes
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/MindSpore-paper-code-3/code9/tree/main/RetinaFace_ResNet50
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/MindSpore-scientific-2/code-10/tree/main/mgan-mindspore
GANimation: Anatomically-aware Facial Animation from a Single Image
https://github.com/MindSpore-scientific-2/code-11/tree/main/SV-X-Softmax
https://github.com/MindSpore-scientific-2/code-2/tree/main/ZoneoutRNN
RNNPool: Efficient Non-linear Pooling for RAM Constrained Inference
https://github.com/MindSpore-scientific-2/code-4/tree/main/mgan-mindspore
GANimation: Anatomically-aware Facial Animation from a Single Image
https://github.com/MindSpore-scientific-2/code-5/tree/main/Last-Query-Transformer-RNN
RNNPool: Efficient Non-linear Pooling for RAM Constrained Inference
https://github.com/MindSpore-scientific-2/code-7/tree/main/HCP_Avatar_Inferrence
GSmoothFace: Generalized Smooth Talking Face Generation via Fine Grained 3D Face Guidance
https://github.com/MindSpore-scientific-2/code-9/tree/main/Deep_Feature_Factorization_For_Concept_Discovery
https://github.com/MindSpore-scientific/code-12/tree/main/Deep_Feature_Factorization_For_Concept_Discovery
https://github.com/MindSpore-scientific/code-2/tree/main/Three-Dimensional-Lip-Motion-Network-for-Text-Independent-Speaker-Recognition-master
Real-time Neural Radiance Talking Portrait Synthesis via Audio-spatial Decomposition
https://github.com/MindSpore-scientific/code-3/tree/main/circle-loss
Circle Loss: A Unified Perspective of Pair Similarity Optimization
https://github.com/MindSpore-scientific/code-9/tree/main/Zero-DCE%2B%2B
Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.