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/liqichen6688/duo-attention
https://github.com/liuch37/semantic-segmentation
High-Resolution Representations for Labeling Pixels and Regions
https://github.com/liuf1990/CFSM
Controllable and Guided Face Synthesis for Unconstrained Face Recognition
https://github.com/liulaomo/go-ai-related
Object Detection with Pixel Intensity Comparisons Organized in Decision Trees
https://github.com/liuliuliu11/pixel2style2pixel-liu
Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation
https://github.com/liuzhen03/ADNet
ADNet: Attention-guided Deformable Convolutional Network for High Dynamic Range Imaging
https://github.com/livingbio/MakeItTalk
https://github.com/liyemei/Deformable-active-convolutional
https://github.com/lizhuoq/ESRGAN-pytorch
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/llSeedll/Facial-Recognition-using-Facenet
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/llsouder/screen-ocr
https://github.com/longchr123/insightface-analysis
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/longnguyen2/blazeface
BlazeFace: Sub-millisecond Neural Face Detection on Mobile GPUs
https://github.com/lonlyness/pggan-structure
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/lonlyness/variational_temporal_abstraction
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/lsaiml/CaVINet
Deep Cross Modal Learning for Caricature Verification and Identification(CaVINet)
https://github.com/lucasleesw/PCANet_python_ver
PCANet: A Simple Deep Learning Baseline for Image Classification?
https://github.com/lucasxlu/ComboLoss
SCUT-FBP5500: A Diverse Benchmark Dataset for Multi-Paradigm Facial Beauty Prediction
https://github.com/luciennnnnnn/dualformer
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/luckycallor/insightface-tensorflow
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/lyatdawn/lightcnn-mxnet
https://github.com/lyk19940625/MyRFBNet
https://github.com/lyp-deeplearning/awesome-retinal-registration
https://github.com/lzx551402/ASLFeat
Circle Loss: A Unified Perspective of Pair Similarity Optimization
https://github.com/m-kahla/Label-Only-Model-Inversion-Attacks-via-Boundary-Repulsion
https://github.com/ma921/XRDidentifier
AdaCos: Adaptively Scaling Cosine Logits for Effectively Learning Deep Face Representations
https://github.com/ma9o/facematch
https://github.com/machanic/AU_R-CNN
AU R-CNN: Encoding Expert Prior Knowledge into R-CNN for Action Unit Detection
https://github.com/madhavambati/Face-Recognition
https://github.com/magicsunday/pico
Object Detection with Pixel Intensity Comparisons Organized in Decision Trees
https://github.com/magnificent1208/r3det-on-mmdetection
https://github.com/mahathi26/ciss
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/mahxn0/RetinaFace_For_Face_Plate_Detect
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/malin9402/2020-0221
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/malin9402/arcface
RetinaFace: Single-stage Dense Face Localisation in the Wild
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.