https://github.com/dflip3k/dflip-3k
공식 코드Linguistic Profiling of Deepfakes: An Open Database for Next-Generation Deepfake Detection
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.
Linguistic Profiling of Deepfakes: An Open Database for Next-Generation Deepfake Detection
Perceptual Quality-preserving Black-Box Attack against Deep Learning Image Classifiers
Joint Co-Speech Gesture and Expressive Talking Face Generation using Diffusion with Adapters
Rethinking Affect Analysis: A Protocol for Ensuring Fairness and Consistency
PTW: Pivotal Tuning Watermarking for Pre-Trained Image Generators
Limited Data, Unlimited Potential: A Study on ViTs Augmented by Masked Autoencoders
Facial Motion Prior Networks for Facial Expression Recognition
Rethinking Bias Mitigation: Fairer Architectures Make for Fairer Face Recognition
Improving the Transferability of Targeted Adversarial Examples through Object-Based Diverse Input
Pose-disentangled Contrastive Learning for Self-supervised Facial Representation
Text-Free Learning of a Natural Language Interface for Pretrained Face Generators
E4S: Fine-grained Face Swapping via Editing With Regional GAN Inversion
E4S: Fine-grained Face Swapping via Editing With Regional GAN Inversion
Real-world adversarial attack on MTCNN face detection system
MST-KD: Multiple Specialized Teachers Knowledge Distillation for Fair Face Recognition
Speech Driven Talking Face Generation from a Single Image and an Emotion Condition
Linear Disentangled Representation Learning for Facial Actions
Linear Disentangled Representation Learning for Facial Actions
Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation
EmoBox: Multilingual Multi-corpus Speech Emotion Recognition Toolkit and Benchmark
Discretization-Induced Dirichlet Posterior for Robust Uncertainty Quantification on Regression
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.