https://github.com/laowang666888/HRNET
High-Resolution Representations for Labeling Pixels and Regions
Papers with Code 아카이브
Facial Landmark Detection
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 3
- 평가표
- 10
- 보존된 평가 기록
- 38
Facial Landmark Detection is a computer vision task that involves detecting and localizing specific points or landmarks on a face, such as the eyes, nose, mouth, and chin. The goal is to accurately identify these landmarks in images or videos of faces in real-time and use them for various applications, such as face recognition, facial expression analysis, and head pose estimation.
<span style="color:grey; opacity: 0.6">( Image credit: Style Aggregated Network for Facial Landmark Detection )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/liuch37/semantic-segmentation
High-Resolution Representations for Labeling Pixels and Regions
https://github.com/mdt48/semantic-segmentation-pytorch
High-Resolution Representations for Labeling Pixels and Regions
https://github.com/mikgur/MADE_CV_1000_facial_landmarks
https://github.com/mohanliu/HRnet-semantic-segmentation-exp
High-Resolution Representations for Labeling Pixels and Regions
https://github.com/mseg-dataset/mseg-semantic
High-Resolution Representations for Labeling Pixels and Regions
https://github.com/nilseuropa/pfdl_ncnn
https://github.com/nilseuropa/pfld_ncnn
https://github.com/nilseuropa/ros_img2pose
img2pose: Face Alignment and Detection via 6DoF, Face Pose Estimation
https://github.com/nova26/facePoseEstimation
https://github.com/oawiles/FAb-Net
Self-supervised learning of a facial attribute embedding from video
https://github.com/olegpolivin/antisleep
https://github.com/open-mmlab/mmpose
https://github.com/polarisZhao/PFLD-pytorch
https://github.com/samuelyu2002/PFLD
https://github.com/sdll/hrnet-pose-estimation
High-Resolution Representations for Labeling Pixels and Regions
https://github.com/shijianjian/HRNet_Keras
High-Resolution Representations for Labeling Pixels and Regions
https://github.com/starhiking/ATF
https://github.com/stergioc/smooth-transformer
Deforming Autoencoders: Unsupervised Disentangling of Shape and Appearance
https://github.com/strivebo/image_segmentation_dl
High-Resolution Representations for Labeling Pixels and Regions
https://github.com/tejaswigowda/semseg-pytorch
High-Resolution Representations for Labeling Pixels and Regions
https://github.com/tomas-gajarsky/facetorch
Pre-training strategies and datasets for facial representation learning
https://github.com/tomasjakab/imm
Unsupervised Learning of Object Landmarks through Conditional Image Generation
https://github.com/visionNoob/hrnet_pytorch
High-Resolution Representations for Labeling Pixels and Regions
https://github.com/wsjzha/deep-high-resolution-net.pytorch
High-Resolution Representations for Labeling Pixels and Regions
https://github.com/xiuyu0000/papers_with_examples/tree/main/pfld
https://github.com/yangyucheng000/pfld
https://github.com/yuanyuanli85/tf-hrnet
High-Resolution Representations for Labeling Pixels and Regions
https://github.com/zenonLEE/MDFLD
Towards Multi-domain Face Landmark Detection with Synthetic Data from Diffusion model
https://github.com/zhixinshu/DeformingAutoencoders-pytorch
Deforming Autoencoders: Unsupervised Disentangling of Shape and Appearance
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.