https://github.com/Olksndr/FaceShifter
FaceShifter: Towards High Fidelity And Occlusion Aware Face Swapping
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/OmarEl-Khashab/FerNeXt-Facial-Expression-Recognition-Using-ConvNeXt-with-Channel-Attention
https://github.com/One-sixth/CVAE-GAN_tensorlayer
CVAE-GAN: Fine-Grained Image Generation through Asymmetric Training
https://github.com/Ontheway361/dul-pytorch
https://github.com/Ontheway361/pfld-pytorch
https://github.com/Oorgien/Scene-Inpainting
https://github.com/OpenTalker/SadTalker
https://github.com/PPPrior/i3d-pytorch
https://github.com/PacktPublishing/Hands-On-Image-Generation-with-TensorFlow-2.0/tree/master/Chapter07
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/PaddlePaddle/PLSC
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/PaddlePaddle/PaddleDetection
https://github.com/PaddlePaddle/PaddleSeg
Deep High-Resolution Representation Learning for Visual Recognition
https://github.com/Panagiotou/Procedural3DTerrain
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/Panagiotou/Random3DTerrain
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/PietroMelzi/GANDiffFace
Synthetic Data for the Mitigation of Demographic Biases in Face Recognition
https://github.com/Podidiving/lgsc-for-fas-pytorch
https://github.com/PolarisAlpha/darknet
https://github.com/PolyArch/fp-diannao
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Popoooo/darknet_test
https://github.com/Porechniy13/FaceReco
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/PowerOfDream/digitx
https://github.com/PrasannaPulakurthi/ProGAN_PyTorch
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/PrashantRanjan09/Structured-Self-Attentive-Sentence-Embedding
https://github.com/PrashanthaTP/wav2mov
A Lip Sync Expert Is All You Need for Speech to Lip Generation In The Wild
https://github.com/Prateek93a/DoodleAI
https://github.com/PuchatekwSzortach/face_detection
Multi-view Face Detection Using Deep Convolutional Neural Networks
https://github.com/Pulkitdzrt/ML-Image-Super-Resolution
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/Punakshi/Deep-Residual-Learning-for-Image-Recognition-Implementation
https://github.com/PushpakBhoge/Face_Recognition_TF
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/Puzer/stylegan-encoder
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Qengineering/Face-Mask-Detection-Jetson-Nano
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/Qengineering/Face-Mask-Detection-Raspberry-Pi-64-bits
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/Qengineering/Face-detection-Raspberry-Pi-32-64-bits
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/Qengineering/LFFD-MNN-Jetson-Nano
https://github.com/Qengineering/LFFD-MNN-Raspberry-Pi-4
https://github.com/Qengineering/LFFD-ncnn-Jetson-Nano
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.