https://github.com/visionjo/pykinship
공식 코드Recognizing Families In the Wild: White Paper for the 4th Edition Data Challenge
Papers with Code 아카이브
Gesture Recognition
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
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- 보존된 평가 기록
- 26
Gesture Recognition is an active field of research with applications such as automatic recognition of sign language, interaction of humans and robots or for new ways of controlling video games.
Source: Gesture Recognition in RGB Videos Using Human Body Keypoints and Dynamic Time Warping
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/whisperyi/mmvr
공식 코드https://github.com/willogy-team/hand-gesture-recognition-smc2021
공식 코드Fine-grained Hand Gesture Recognition in Multi-viewpoint Hand Hygiene
https://github.com/windmark/static-gesture-recognition
공식 코드https://github.com/xyanchen/wifi-csi-sensing-benchmark
공식 코드AutoFi: Towards Automatic WiFi Human Sensing via Geometric Self-Supervised Learning
https://github.com/yuxiaochen1103/DG-STA
공식 코드Construct Dynamic Graphs for Hand Gesture Recognition via Spatial-Temporal Attention
https://github.com/zhang-xie/wimuse
공식 코드https://github.com/zhoubenjia/RAAR3DNet
공식 코드Regional Attention with Architecture-Rebuilt 3D Network for RGB-D Gesture Recognition
https://gitlab.com/EAVISE/hand-gesture-recognition
공식 코드Low-latency hand gesture recognition with a low resolution thermal imager
https://gitlab.com/nct_tso_public/surgical_gesture_recognition
공식 코드https://gitlab.com/nvm_iitd_research/xrage
공식 코드https://github.com/2023-MindSpore-1/ms-code-24
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/2023-MindSpore-1/ms-code-7/tree/main/trn
https://github.com/AbdurrahmanNadi/activity_recognition_web_service
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/AdamGoldbraikh/Bounded-Future-MS-TCNPP-for-Surgical-Gesture-Recognition
https://github.com/AlbertoSabater/Domain-and-View-point-Agnostic-Hand-Action-Recognition
https://github.com/Alexyuda/action_recognition
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/Blitzkrieg37/Kinesic
Real-time Hand Gesture Detection and Classification Using Convolutional Neural Networks
https://github.com/Blueprintf/pointlstm-gesture-recognition-pytorch
https://github.com/BlurryLight/DD-Net-Pytorch
Make Skeleton-based Action Recognition Model Smaller, Faster and Better
https://github.com/CMU-CREATE-Lab/deep-smoke-machine
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/Dou-Yu-xuan/pykinship
Recognizing Families In the Wild: White Paper for the 4th Edition Data Challenge
https://github.com/FrederikSchorr/sign-language
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/HsinYingLee/OPN
Two-Stream Convolutional Networks for Action Recognition in Videos
https://github.com/Intelligent-Perception-Lab/HIBER
Towards Domain-Independent and Real-Time Gesture Recognition Using mmWave Signal
https://github.com/JeffCHEN2017/WSSTG
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/KingGugu/I3D
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/LEChaney/Real-time-SSAR
Real-time Hand Gesture Detection and Classification Using Convolutional Neural Networks
https://github.com/LiChenyang-Github/Temporal-Transformer-Module
https://github.com/LukasHedegaard/co3d
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/MadanRaj-H/CS766-Project
Using Deep Convolutional Networks for Gesture Recognition in American Sign Language
https://github.com/MarkoLewis-Projects/Sign_language_detection
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/Michaelgod/test
Two-Stream Convolutional Networks for Action Recognition in Videos
https://github.com/Microsoft/EdgeML
https://github.com/MindSpore-paper-code-2/code3/tree/main/trn
https://github.com/OanaIgnat/i3d_keras
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.