https://github.com/discipleofhamilton/RetinaFace
RetinaFace: Single-stage Dense Face Localisation in the Wild
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
Facial tasks in machine learning operate based on images or video frames (or other datasets) focussed on human faces.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
RetinaFace: Single-stage Dense Face Localisation in the Wild
Very Deep Convolutional Networks for Large-Scale Image Recognition
Progressive Growing of GANs for Improved Quality, Stability, and Variation
Very Deep Convolutional Networks for Large-Scale Image Recognition
BlazeFace: Sub-millisecond Neural Face Detection on Mobile GPUs
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
Multi-view Face Detection Using Deep Convolutional Neural Networks
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
PatchNet: A Simple Face Anti-Spoofing Framework via Fine-Grained Patch Recognition
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
GANimation: Anatomically-aware Facial Animation from a Single Image
FaceNet: A Unified Embedding for Face Recognition and Clustering
A General Framework for Adversarial Examples with Objectives
ADNet: Attention-guided Deformable Convolutional Network for High Dynamic Range Imaging
Deep Learning for ECG Analysis: Benchmarks and Insights from PTB-XL
High-Resolution Representations for Labeling Pixels and Regions
FaceNet: A Unified Embedding for Face Recognition and Clustering
FaceNet: A Unified Embedding for Face Recognition and Clustering
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
Exploring the Open World Using Incremental Extreme Value Machines
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.