https://github.com/cuiziteng/iccv_maet
Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement
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.
Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement
Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
SeesawFaceNets: sparse and robust face verification model for mobile platform
SeesawFaceNets: sparse and robust face verification model for mobile platform
MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition
Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks
High-Resolution Representations for Labeling Pixels and Regions
Very Deep Convolutional Networks for Large-Scale Image Recognition
Exposing DeepFake Videos By Detecting Face Warping Artifacts
In Ictu Oculi: Exposing AI Generated Fake Face Videos by Detecting Eye Blinking
RetinaFace: Single-stage Dense Face Localisation in the Wild
Very Deep Convolutional Networks for Large-Scale Image Recognition
Very Deep Convolutional Networks for Large-Scale Image Recognition
Very Deep Convolutional Networks for Large-Scale Image Recognition
FairFace: Face Attribute Dataset for Balanced Race, Gender, and Age
FaceNet: A Unified Embedding for Face Recognition and Clustering
DeepFakes and Beyond: A Survey of Face Manipulation and Fake Detection
Stacked Dense U-Nets with Dual Transformers for Robust Face Alignment
MobileFaceNets: Efficient CNNs for Accurate Real-Time Face Verification on Mobile Devices
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.