https://github.com/nikolaevs92/DL-articles-implementation
Very Deep Convolutional Networks for Large-Scale Image Recognition
Papers with Code 아카이브
3D Face Reconstruction
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 1
- 평가표
- 9
- 보존된 평가 기록
- 94
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/nilseuropa/pfdl_ncnn
https://github.com/nilseuropa/pfld_ncnn
https://github.com/nilseuropa/ros_img2pose
img2pose: Face Alignment and Detection via 6DoF, Face Pose Estimation
https://github.com/niranjana98/Image-Classification
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/niuxiaozhang/mxnet-retinaface-to-gluon
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/njordsir/Clustering-Faces
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/nmhaddad/python-machine-learning-youtube
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/nodefluxio/automatic-masking-master
https://github.com/noelmat/facelandmarks_wingloss
Wing Loss for Robust Facial Landmark Localisation with Convolutional Neural Networks
https://github.com/northeastsquare/effficientnet
https://github.com/notecola/facenet-light
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/nova26/facePoseEstimation
https://github.com/nsom/Basic-Models
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/nsom/VGG16
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/ntrin/CIFAR_10_ML
https://github.com/nu11us/WC-MS-Celeb
MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition
https://github.com/nveldt/LamCC
https://github.com/oarriaga/paz
https://github.com/oawiles/FAb-Net
Self-supervised learning of a facial attribute embedding from video
https://github.com/obj2vec/obj2vec
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/oellop/facial_expression
Challenges in Representation Learning: A report on three machine learning contests
https://github.com/okeer/nst-tele-bot
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/oktapodi/ps_resNet
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/olaviinha/NeuralImageSuperResolution
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/olegpolivin/antisleep
https://github.com/olgi9911/MMDG
Suppress and Rebalance: Towards Generalized Multi-Modal Face Anti-Spoofing
https://github.com/ollewelin/libtorch-GPU-CNN-test-MNIST-with-Batchnorm
https://github.com/omaarelsherif/Face-Detection-Using-Deep-Learning
Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks
https://github.com/omaarelsherif/Face-Detection-Using-MTCNN
Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks
https://github.com/omarsayed7/Deep-Emotion
Deep-Emotion: Facial Expression Recognition Using Attentional Convolutional Network
https://github.com/omidmn/Engagement-Recognition
Automatic Recognition of Student Engagement using Deep Learning and Facial Expression
https://github.com/omidnezami/Engagement-Recognition
Automatic Recognition of Student Engagement using Deep Learning and Facial Expression
https://github.com/omkarghugarkar007/Neural_Super_Sampling
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/omrijsharon/torchlex
https://github.com/omron-sinicx/sbsfigures
PetFace: A Large-Scale Dataset and Benchmark for Animal Identification
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.