https://github.com/krantirk/Face-detection-and-counting-
FaceNet: A Unified Embedding for Face Recognition and Clustering
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.
FaceNet: A Unified Embedding for Face Recognition and Clustering
PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
FaceNet: A Unified Embedding for Face Recognition and Clustering
High-Resolution Representations for Labeling Pixels and Regions
DeepFaceLab: Integrated, flexible and extensible face-swapping framework
Real-time Convolutional Neural Networks for Emotion and Gender Classification
Exploiting temporal and depth information for multi-frame face anti-spoofing
High-Resolution Representations for Labeling Pixels and Regions
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
Circle Loss: A Unified Perspective of Pair Similarity Optimization
GroupFace: Learning Latent Groups and Constructing Group-based Representations for Face Recognition
MagFace: A Universal Representation for Face Recognition and Quality Assessment
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
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
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.