https://github.com/yuezunli/WIFS2018_In_Ictu_Oculi
In Ictu Oculi: Exposing AI Generated Fake Face Videos by Detecting Eye Blinking
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.
In Ictu Oculi: Exposing AI Generated Fake Face Videos by Detecting Eye Blinking
An IoT Endpoint System-on-Chip for Secure and Energy-Efficient Near-Sensor Analytics
Circle Loss: A Unified Perspective of Pair Similarity Optimization
Deep High-Resolution Representation Learning for Visual Recognition
Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation
Interpreting the Latent Space of GANs for Semantic Face Editing
FaceForensics++: Learning to Detect Manipulated Facial Images
UR Channel-Robust Synthetic Speech Detection System for ASVspoof 2021
FaceNet: A Unified Embedding for Face Recognition and Clustering
FaceNet: A Unified Embedding for Face Recognition and Clustering
FaceNet: A Unified Embedding for Face Recognition and Clustering
Towards Multi-domain Face Landmark Detection with Synthetic Data from Diffusion model
Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural Networks
FaceNet: A Unified Embedding for Face Recognition and Clustering
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
Deforming Autoencoders: Unsupervised Disentangling of Shape and Appearance
BlazeFace: Sub-millisecond Neural Face Detection on Mobile GPUs
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.