https://github.com/piaohe111/gittest
RetinaFace: Single-stage Dense Face Localisation in the Wild
Papers with Code 아카이브
3D Face Reconstruction
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3D Face Reconstruction is a computer vision task that involves creating a 3D model of a human face from a 2D image or a set of images. The goal of 3D face reconstruction is to reconstruct a digital 3D representation of a person's face, which can be used for various applications such as animation, virtual reality, and biometric identification.
<span style="color:grey; opacity: 0.6">( Image credit: 3DDFA_V2 )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/pierluigiferrari/ssd_keras
https://github.com/pikabite/segmentations_tf2
Deep High-Resolution Representation Learning for Visual Recognition
https://github.com/piotrkawa/audio-deepfake-source-tracing
https://github.com/pipidog/CNLP
https://github.com/playerkk/face-py-faster-rcnn
https://github.com/pocheck-v2/Pocheck-V2-Beta
https://github.com/podgorskiy/StyleGan
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/pohwa065/Deep-Learning-for-Super-Resolution-and-Image-Enhancement
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/pohwa065/SRGAN-for-Super-Resolution-and-Image-Enhancement
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/polarisZhao/PFLD-pytorch
https://github.com/polarisZhao/mtcnn-pytorch
Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks
https://github.com/polimi-ispl/icpr2020dfdc
FaceForensics++: Learning to Detect Manipulated Facial Images
https://github.com/politecat314/MEBeauty_CNN
https://github.com/pothabattulasantosh/Detection-of-face-Manipulated-videos
FaceForensics++: Learning to Detect Manipulated Facial Images
https://github.com/pothabattulasantosh/Detection-of-facial-Manipulated-videos
FaceForensics++: Learning to Detect Manipulated Facial Images
https://github.com/pouya-parsa/progressive_gan
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/ppokhare/Handling-Imbalanced-Dataset-for-classification-task
https://github.com/ppriyank/Pytorch-Additive_Margin_Softmax_for_Face_Verification
https://github.com/prajinkhadka/face_det_check
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/prakashpandey9/Text-Classification-Pytorch
https://github.com/prakharbhardwaj1/emotion-recognition
Training Deep Networks for Facial Expression Recognition with Crowd-Sourced Label Distribution
https://github.com/pranavbudhwant/ACVAEGAN
CVAE-GAN: Fine-Grained Image Generation through Asymmetric Training
https://github.com/pranavjadhav001/arcface_inversion
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/prasadsawant5/PointNet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/princeton-vl/SimpleView
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/pshashk/seesaw-facenet
AirFace: Lightweight and Efficient Model for Face Recognition
https://github.com/pterhoer/FaceImageQuality
SER-FIQ: Unsupervised Estimation of Face Image Quality Based on Stochastic Embedding Robustness
https://github.com/pterhoer/FingerprintImageQuality
SER-FIQ: Unsupervised Estimation of Face Image Quality Based on Stochastic Embedding Robustness
https://github.com/ptran1203/facial_beauty
SCUT-FBP5500: A Diverse Benchmark Dataset for Multi-Paradigm Facial Beauty Prediction
https://github.com/pulp-platform/hwpe-tb
An IoT Endpoint System-on-Chip for Secure and Energy-Efficient Near-Sensor Analytics
https://github.com/pulp-platform/pulp
An IoT Endpoint System-on-Chip for Secure and Energy-Efficient Near-Sensor Analytics
https://github.com/pulp-platform/pulpissimo
An IoT Endpoint System-on-Chip for Secure and Energy-Efficient Near-Sensor Analytics
https://github.com/purdue-m2/robust_fairness_withNoisy_labels
Analyzing Fairness in Deepfake Detection With Massively Annotated Databases
https://github.com/pwc-1/Paper-9/tree/main/5/wavlm
https://github.com/pwc-1/Paper-9/tree/main/6/Zero-DCE
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.