https://github.com/sunjungAn/Tensorflow2.x-CV
Very Deep Convolutional Networks for Large-Scale Image 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.
Very Deep Convolutional Networks for Large-Scale Image Recognition
Real-time Convolutional Neural Networks for Emotion and Gender Classification
FaceNet: A Unified Embedding for Face Recognition and Clustering
Very Deep Convolutional Networks for Large-Scale Image Recognition
FaceNet: A Unified Embedding for Face Recognition and Clustering
Very Deep Convolutional Networks for Large-Scale Image Recognition
4DFAB: A Large Scale 4D Facial Expression Database for Biometric Applications
In Ictu Oculi: Exposing AI Generated Fake Face Videos by Detecting Eye Blinking
Very Deep Convolutional Networks for Large-Scale Image Recognition
Progressive Growing of GANs for Improved Quality, Stability, and Variation
Real-time Convolutional Neural Networks for Emotion and Gender Classification
FaceNet: A Unified Embedding for Face Recognition and Clustering
Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks
Very Deep Convolutional Networks for Large-Scale Image Recognition
FaceShifter: Towards High Fidelity And Occlusion Aware Face Swapping
Facial Motion Prior Networks for Facial Expression Recognition
FaceNet: A Unified Embedding for Face Recognition and Clustering
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.