https://github.com/Rosie-Brigham/sesmeg
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/SongJieLiu/pfld
https://github.com/SpikeKing/Gaze4Landmarks
https://github.com/Suaro/pidroid
https://github.com/Wastoon/TSAL
Teacher-Student Asynchronous Learning with Multi-Source Consistency for Facial Landmark Detection
https://github.com/WeiHongLee/Learning-to-impute
Learning to Impute: A General Framework for Semi-supervised Learning
https://github.com/YutingZhang/lmdis-rep
Unsupervised Discovery of Object Landmarks as Structural Representations
https://github.com/abhi1kumar/hrnet_pose_single_gpu
High-Resolution Representations for Labeling Pixels and Regions
https://github.com/ainrichman/Peppa-Facial-Landmark-PyTorch
https://github.com/alldbi/Factorized-Spatial-Embeddings
Unsupervised learning of object landmarks by factorized spatial embeddings
https://github.com/anshky/HR-NET
High-Resolution Representations for Labeling Pixels and Regions
https://github.com/baoshengyu/deep-high-resolution-net.pytorch
High-Resolution Representations for Labeling Pixels and Regions
https://github.com/bobetocalo/bobetocalo_eccv18
Face Alignment using a 3D Deeply-initialized Ensemble of Regression Trees
https://github.com/bobetocalo/bobetocalo_prl19
https://github.com/d-shivam/Pose-estimation-based-action-recognition-for-help-Situation-Identification
High-Resolution Representations for Labeling Pixels and Regions
https://github.com/deftruth/torchlm
Pixel-in-Pixel Net: Towards Efficient Facial Landmark Detection in the Wild
https://github.com/ducongju/HRNet
High-Resolution Representations for Labeling Pixels and Regions
https://github.com/edocollins/DFF
https://github.com/epoc88/PFLD_68pts_Pytorch_2020
https://github.com/esimov/pigo
https://github.com/facebookresearch/supervision-by-registration
https://github.com/fenglian425/Agriculture_AI
High-Resolution Representations for Labeling Pixels and Regions
https://github.com/github-luffy/PFLD-68points-tensorflow
https://github.com/github-luffy/PFLD_68points_Pytorch
https://github.com/gox-ai/hrnet-pose-api
High-Resolution Representations for Labeling Pixels and Regions
https://github.com/guoqiangqi/PFLD
https://github.com/hggym/pigo
https://github.com/hqng/imm-pytorch
Unsupervised Learning of Object Landmarks through Conditional Image Generation
https://github.com/iacopomasi/face_specific_augm
https://github.com/jacobgil/pytorch-grad-cam
https://github.com/jiarenchang/facecycle
Self-supervised learning of a facial attribute embedding from video
https://github.com/justusschock/shape-constrained-network
https://github.com/karaposu/facexformer-pipeline
https://github.com/kdhingra307/temp
High-Resolution Representations for Labeling Pixels and Regions
https://github.com/kingcong/HRNetW48_seg
High-Resolution Representations for Labeling Pixels and Regions
https://github.com/kukby/Mish-semantic-segmentation-pytorch
High-Resolution Representations for Labeling Pixels and Regions
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.