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/bogireddytejareddy/micro-expression-recognition
https://github.com/borhanMorphy/fastface
https://github.com/borhanMorphy/light-face-detection
https://github.com/boris127/vehicle-detection
https://github.com/boseaslcohort/youtube-8m
https://github.com/boukhayma/face_normals
Cross-modal Deep Face Normals with Deactivable Skip Connections
https://github.com/bovifocr/insightface
Sample and Computation Redistribution for Efficient Face Detection
https://github.com/brendanmallery9/entropic-barycenters
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/bruceeeee/facenet
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/bruinxiong/Evaluation_IJBA
A Good Practice Towards Top Performance of Face Recognition: Transferred Deep Feature Fusion
https://github.com/brunocalogero/AMLSassignment
From Facial Parts Responses to Face Detection: A Deep Learning Approach
https://github.com/bryandlee/FreezeG
Few-shot Knowledge Transfer for Fine-grained Cartoon Face Generation
https://github.com/brycexu/MR-Residual-Net
https://github.com/bt77/pointnet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/bubbliiiing/retinaface-keras
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/buptdbj/darknet-windows-linux
https://github.com/busyboxs/Some-resources-useful-for-me
Training Region-based Object Detectors with Online Hard Example Mining
https://github.com/bxz9200/ultraclean
Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning
https://github.com/c1ph3rr/Deep-Residual-Learning-for-Image-Recognition
https://github.com/c1ph3rr/Very-Deep-Convolutional-Networks-For-Large-Scale-Image-Recognition
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/caihaocong/Yolov3_AlexeyAB_Version
https://github.com/caihuaiguang/CHG-Shapley-for-Data-Selection
https://github.com/calmisential/TensorFlow2.0_Image_Classification
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/calmisential/TensorFlow2.0_ResNet
https://github.com/camel007/caffe-moon
MOON: A Mixed Objective Optimization Network for the Recognition of Facial Attributes
https://github.com/cameronchoi/r3det-copy
https://github.com/canturan10/light_side
Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement
https://github.com/canturan10/satellighte
https://github.com/caojoshua/Plastic-Recyclability
https://github.com/carlosb1/upc-aidl-19-team4
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/carolgithubv1/convnets-keras
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/cat-claws/RobFace
https://github.com/cc-hpc-itwm/DeepFakeDetection
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/cchinchristopherj/Right-Whale-Unsupervised-Model
https://github.com/cecile-hi/regularized-adaptive-weight-modification
What to Remember: Self-Adaptive Continual Learning for Audio Deepfake Detection
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.