https://github.com/2023-MindSpore-1/ms-code-214/tree/main/HMR
Deep High-Resolution Representation Learning for Visual Recognition
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.
Deep High-Resolution Representation Learning for Visual Recognition
Deep High-Resolution Representation Learning for Visual Recognition
High-Resolution Representations for Labeling Pixels and Regions
HiFaceGAN: Face Renovation via Collaborative Suppression and Replenishment
Progressive Growing of GANs for Improved Quality, Stability, and Variation
Very Deep Convolutional Networks for Large-Scale Image Recognition
Video Face Recognition System: RetinaFace-mnet-faster and Secondary Search
ADNet: Attention-guided Deformable Convolutional Network for High Dynamic Range Imaging
Very Deep Convolutional Networks for Large-Scale Image Recognition
CenterFace: Joint Face Detection and Alignment Using Face as Point
RetinaFace: Single-stage Dense Face Localisation in the Wild
Deep High-Resolution Representation Learning for Visual Recognition
HiFaceGAN: Face Renovation via Collaborative Suppression and Replenishment
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
RetinaFace: Single-stage Dense Face Localisation in the Wild
Very Deep Convolutional Networks for Large-Scale Image Recognition
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
Video Face Recognition System: RetinaFace-mnet-faster and Secondary Search
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.