https://github.com/leob03/E2E_Object_Pose_Estimator
Very Deep Convolutional Networks for Large-Scale Image Recognition
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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>
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Very Deep Convolutional Networks for Large-Scale Image Recognition
GroupFace: Learning Latent Groups and Constructing Group-based Representations for Face Recognition
MagFace: A Universal Representation for Face Recognition and Quality Assessment
Very Deep Convolutional Networks for Large-Scale Image Recognition
Very Deep Convolutional Networks for Large-Scale Image Recognition
Challenges in Representation Learning: A report on three machine learning contests
Challenges in Representation Learning: A report on three machine learning contests
FaceNet: A Unified Embedding for Face Recognition and Clustering
FaceNet: A Unified Embedding for Face Recognition and Clustering
Very Deep Convolutional Networks for Large-Scale Image Recognition
Object Detection with Pixel Intensity Comparisons Organized in Decision Trees
MOON: A Mixed Objective Optimization Network for the Recognition of Facial Attributes
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
FastReID: A Pytorch Toolbox for General Instance Re-identification
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
Multi-scale Promoted Self-adjusting Correlation Learning for Facial Action Unit Detection
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
High-Resolution Representations for Labeling Pixels and Regions
Controllable and Guided Face Synthesis for Unconstrained Face Recognition
Object Detection with Pixel Intensity Comparisons Organized in Decision Trees
Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation
ADNet: Attention-guided Deformable Convolutional Network for High Dynamic Range Imaging
Very Deep Convolutional Networks for Large-Scale Image Recognition
Very Deep Convolutional Networks for Large-Scale Image Recognition
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.