https://github.com/wonsu0513/ros2_foxy_facial_expressions
Training Deep Networks for Facial Expression Recognition with Crowd-Sourced Label Distribution
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/wsjzha/deep-high-resolution-net.pytorch
High-Resolution Representations for Labeling Pixels and Regions
https://github.com/wtomin/A-Multitask-Solution-for-FAU-EXPR-VA
https://github.com/wujiyang/Face_Pytorch
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/wujpbb7/caffe_circleloss
Circle Loss: A Unified Perspective of Pair Similarity Optimization
https://github.com/wwhio/megmodels
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/xacheng1996/vfd
https://github.com/xalanq/chinese-sentiment-classification
https://github.com/xg416/DATUM
Expanding Accurate Person Recognition to New Altitudes and Ranges: The BRIAR Dataset
https://github.com/xg416/mambatm
Expanding Accurate Person Recognition to New Altitudes and Ranges: The BRIAR Dataset
https://github.com/xialuxi/arcface-caffe
Wing Loss for Robust Facial Landmark Localisation with Convolutional Neural Networks
https://github.com/xiamenwcy/extended-caffe
https://github.com/xiaoccer/mobilefacenet_pytorch
MobileFaceNets: Efficient CNNs for Accurate Real-Time Face Verification on Mobile Devices
https://github.com/xiaoxiong74/face-cluster-by-infomap
https://github.com/xilinx/finn-examples
https://github.com/xiuyu0000/papers_with_examples/tree/main/pfld
https://github.com/xmu-xiaoma666/External-Attention-pytorch
RepMLP: Re-parameterizing Convolutions into Fully-connected Layers for Image Recognition
https://github.com/xmu-xiaoma666/RepMLP-pytorch
RepMLP: Re-parameterizing Convolutions into Fully-connected Layers for Image Recognition
https://github.com/xuanyuzhou98/SqueezeSeg
https://github.com/xytpai/retinanet
https://github.com/yakhyo/faceboxes-pytorch
https://github.com/yakhyo/gaze-estimation
Sample and Computation Redistribution for Efficient Face Detection
https://github.com/yakhyo/head-pose-estimation
https://github.com/yakhyo/retinaface-pytorch
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/yangyi02/densebox
DenseBox: Unifying Landmark Localization with End to End Object Detection
https://github.com/yangyucheng000/-insightface
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/yangyucheng000/ESRGAN
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/yangyucheng000/HRNetW48_cls
Deep High-Resolution Representation Learning for Visual Recognition
https://github.com/yangyucheng000/MSpaper/tree/main/U-GAT-IT
https://github.com/yangyucheng000/Paper-2/tree/main/ARCM-main
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/yangyucheng000/Paper-4/tree/main/JLONMFSC
Optimal Projections for Discriminative Dictionary Learning using the JL-lemma
https://github.com/yangyucheng000/Paper-4/tree/main/LADS-mindspore
LAD-RCNN:A Powerful Tool for Livestock Face Detection and Normalization
https://github.com/yangyucheng000/Paper-4/tree/main/PDOC_mindspore
SFace: Sigmoid-Constrained Hypersphere Loss for Robust Face Recognition
https://github.com/yangyucheng000/RetinaFace
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/yangyucheng000/paper
CenterFace: Joint Face Detection and Alignment Using Face as Point
https://github.com/yangyucheng000/papercode-2/tree/main/JLONMFSC-master
Optimal Projections for Discriminative Dictionary Learning using the JL-lemma
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.