https://github.com/saimj7/Action-Recognition-in-Real-Time
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/sainimohit23/FaceNet-Real-Time-face-recognition
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/saiteja-talluri/Speech2Face
Wav2Pix: Speech-conditioned Face Generation using Generative Adversarial Networks
https://github.com/sajjjadayobi/FaceLib
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/saloni1998/Cifar-10
https://github.com/samisoto/keras_cosine_based_loss
L2-constrained Softmax Loss for Discriminative Face Verification
https://github.com/sammyamajumdar/ResNet50
https://github.com/samsonadmin/modified-alexeyab-darknet
https://github.com/samuelyu2002/PFLD
https://github.com/sanantoniochili/PointCloud_KNN
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/sanaullah-ashfat/Face-recognition-with-Arcface-Tensorflow
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/sanaullah-ashfat/Face-recognition-with-Arcface-Tensowflow-
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/sandipsahajoy/Predicting-Lymphoma-using-CNN-in-Keras
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/sangHa0411/CIFAR100
https://github.com/sangHa0411/ResNetCIFAR100
https://github.com/sanghyun-son/clustering-kernels
https://github.com/sangyun884/Face2Webtoon
Few-shot Knowledge Transfer for Fine-grained Cartoon Face Generation
https://github.com/sangyun884/WebtoonFaces
Few-shot Knowledge Transfer for Fine-grained Cartoon Face Generation
https://github.com/sankn123/Very-deep-convolutional-networks-for-large-scale-image-recognition-Implementation
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/sanoyo/dl-model
https://github.com/sarthakTUM/roofn3d
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/sarthakbaiswar/Artistic-Styling
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/say2sarwar/DeepAcid
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/sayakpaul/Adaptive-Gradient-Clipping
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/sayakpaul/Revisiting-Pooling-in-CNNs
RNNPool: Efficient Non-linear Pooling for RAM Constrained Inference
https://github.com/sayannath/Zero-DCE-TFLite
Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement
https://github.com/sberbank-ai/DigiTales
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/sberbank-ai/DigiTeller
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/sberbank-ai/real-esrgan
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/scarlettjiali/Tools_Project
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/scoutant/face-blur
https://github.com/sdamolini/LooksLikeWho
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/sdauzcm/sr-basicsr
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/sdll/hrnet-pose-estimation
High-Resolution Representations for Labeling Pixels and Regions
https://github.com/sdu2011/darknet_alexyab
https://github.com/seansoleyman/cifar10-resnet
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.