Papers with Code 아카이브
Facial Recognition and Modelling
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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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/aosokin/cnn_head_detection
공식 코드https://github.com/apchenstu/Facial_Details_Synthesis
공식 코드https://github.com/aqeelanwar/MaskTheFace
공식 코드https://github.com/arassadin/emotiw2017
공식 코드Group-level Emotion Recognition using Transfer Learning from Face Identification
https://github.com/aravinda27/extswap
공식 코드ExtSwap: Leveraging Extended Latent Mapper for Generating High Quality Face Swapping
https://github.com/architect-road/icongan
공식 코드Design What You Desire: Icon Generation from Orthogonal Application and Theme Labels
https://github.com/arctrinity9/datasets-reachocc-and-synchocc
공식 코드Self-Supervised Robustifying Guidance for Monocular 3D Face Reconstruction
https://github.com/aria-zhangjl/e3-facenet
공식 코드https://github.com/arin-upna/biased-heritage
공식 코드Biased Heritage: How Datasets Shape Models in Facial Expression Recognition
https://github.com/arkel23/animesion
공식 코드https://github.com/arnesh2212/SocialDF
공식 코드https://github.com/arpitbansal297/universal-guided-diffusion
공식 코드https://github.com/arturjordao/IncrementalDimensionalityReduction
공식 코드Covariance-free Partial Least Squares: An Incremental Dimensionality Reduction Method
https://github.com/aryan-garg/styledcgan
공식 코드https://github.com/asankagp/mod20
공식 코드A Multi-viewpoint Outdoor Dataset for Human Action Recognition
https://github.com/ashafahi/inceptionv3-transferLearn-poison
공식 코드Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural Networks
https://github.com/asindel/artfacepoints
공식 코드ArtFacePoints: High-resolution Facial Landmark Detection in Paintings and Prints
https://github.com/asvspoof-challenge/2021
공식 코드ASVspoof 2021: Automatic Speaker Verification Spoofing and Countermeasures Challenge Evaluation Plan
https://github.com/atzoriandrea/Exploratory-Analysis-in-Face-Authentication
공식 코드Explaining Bias in Deep Face Recognition via Image Characteristics
https://github.com/audeering/w2v2-how-to
공식 코드Dawn of the transformer era in speech emotion recognition: closing the valence gap
https://github.com/autonomousvehiclelaboratory/anonymization
공식 코드https://github.com/awslabs/cis-matching-tasks
공식 코드https://github.com/axeber01/dold
공식 코드https://github.com/azadlab/FExGAN
공식 코드https://github.com/azadlab/FExGAN-Meta
공식 코드https://github.com/azgo14/classification_metric_learning
공식 코드Classification is a Strong Baseline for Deep Metric Learning
https://github.com/azrealwang/sgadv
공식 코드Similarity-based Gray-box Adversarial Attack Against Deep Face Recognition
https://github.com/baidu/AdvBox
공식 코드Advbox: a toolbox to generate adversarial examples that fool neural networks
https://github.com/bairdzhang/smallhardface
공식 코드Robust Face Detection via Learning Small Faces on Hard Images
https://github.com/baiyangwang/emgan
공식 코드https://github.com/baoshengyu/H3R
공식 코드https://github.com/barisbatuhan/dass_det_inference
공식 코드Domain-Adaptive Self-Supervised Pre-Training for Face & Body Detection in Drawings
https://github.com/barisbatuhan/dass_detector
공식 코드Domain-Adaptive Self-Supervised Pre-Training for Face & Body Detection in Drawings
https://github.com/barisgecer/OSTeC
공식 코드https://github.com/barisgecer/TBGAN
공식 코드Synthesizing Coupled 3D Face Modalities by Trunk-Branch Generative Adversarial Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.