Papers with Code 아카이브
Facial Recognition and Modelling
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 25
- 평가표
- 0
- 보존된 평가 기록
- 0
Facial tasks in machine learning operate based on images or video frames (or other datasets) focussed on human faces.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/royorel/StyleSDF
공식 코드StyleSDF: High-Resolution 3D-Consistent Image and Geometry Generation
https://github.com/rperi/trustworthy-asv-fairness
공식 코드https://github.com/rshaojimmy/AAAI2020-RFMetaFAS
공식 코드https://github.com/rshaojimmy/deepfake-adapter
공식 코드https://github.com/rshaojimmy/multimodal-deepfake
공식 코드https://github.com/rshaojimmy/seqdeepfake
공식 코드https://github.com/rub-syssec/generatedmediasurvey
공식 코드A Representative Study on Human Detection of Artificially Generated Media Across Countries
https://github.com/rub-syssec/wavefake
공식 코드https://github.com/ruizhe-l/tgb-gan
공식 코드https://github.com/ry85/Stochastic-Talking-Face-Generation-Using-Latent-Distribution-Matching
공식 코드Stochastic Talking Face Generation Using Latent Distribution Matching
https://github.com/ryanhe312/LatentFace
공식 코드A Generative Framework for Self-Supervised Facial Representation Learning
https://github.com/ryansteed/ieat
공식 코드Image Representations Learned With Unsupervised Pre-Training Contain Human-like Biases
https://github.com/ryansteed/learning-appearance-bias
공식 코드A Set of Distinct Facial Traits Learned by Machines Is Not Predictive of Appearance Bias in the Wild
https://github.com/sachith500/ContrastiveFaceRepresentation
공식 코드Multi-Dataset Benchmarks for Masked Identification using Contrastive Representation Learning
https://github.com/sahasubhajit/Speech-Spoofing-
공식 코드https://github.com/sahilg06/EmoGen
공식 코드https://github.com/saic-violet/bilayer-model
공식 코드Fast Bi-layer Neural Synthesis of One-Shot Realistic Head Avatars
https://github.com/sam575/axial-gan
공식 코드https://github.com/samir-atra/emotion_estimation_from_video_footage_with_lstm_ml_algorithm
공식 코드https://github.com/sanjaysaha1311/temporal-deepfake-segmentation
공식 코드https://github.com/sanoojan/reface
공식 코드Realistic and Efficient Face Swapping: A Unified Approach with Diffusion Models
https://github.com/sashankmvv/Emotion-recognition
공식 코드Facial Emotion Recognition: A multi-task approach using deep learning
https://github.com/sbelharbi/interpretable-fer-aus
공식 코드Guided Interpretable Facial Expression Recognition via Spatial Action Unit Cues
https://github.com/sbelharbi/structured-output-ae
공식 코드Deep Neural Networks Regularization for Structured Output Prediction
https://github.com/sccsok/Locate-and-Verify
공식 코드Locate and Verify: A Two-Stream Network for Improved Deepfake Detection
https://github.com/sciencefans/Quality-Aware-Network
공식 코드https://github.com/sciencefans/RSA-for-object-detection
공식 코드https://github.com/sclbd/deepfakebench
공식 코드DeepfakeBench: A Comprehensive Benchmark of Deepfake Detection
https://github.com/scu-zjz/forensichub
공식 코드ForensicHub: A Unified Benchmark & Codebase for All-Domain Fake Image Detection and Localization
https://github.com/searecluse/cvprw2024
공식 코드https://github.com/seasonSH/LiftedGAN
공식 코드https://github.com/seasonSH/Probabilistic-Face-Embeddings
공식 코드https://github.com/secml-lab-vt/EvolvingThreat-DeepfakeImageDetect
공식 코드An Analysis of Recent Advances in Deepfake Image Detection in an Evolving Threat Landscape
https://github.com/securifai/masked_faces
공식 코드A realistic approach to generate masked faces applied on two novel masked face recognition data sets
https://github.com/sefaburakokcu/finn-quantized-yolo
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.