https://github.com/krishnanpooja/Master-Thesis
Recognizing Surgical Activities with Recurrent Neural Networks
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
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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/liujf69/TD-GCN-Gesture
https://github.com/mHealthBuet/SegCodeNet
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/matyasbohacek/spoter
https://github.com/mcgridles/LENS
Two-Stream Convolutional Networks for Action Recognition in Videos
https://github.com/melax/hand_tracking_samples
https://github.com/metalbubble/TRN-pytorch
https://github.com/midusi/cacic2019-handshapes
https://github.com/midusi/convolutional_handshape
https://github.com/mikecheninoulu/Emotional-gesture-papers
https://github.com/moustafa19m/ishara
Using Deep Convolutional Networks for Gesture Recognition in American Sign Language
https://github.com/multimodallearning/hand-gesture-posture-position
https://github.com/ndesale/BabyMonitoring
A Study of Vision based Human Motion Recognition and Analysis
https://github.com/nnrogers515/ASL-Recognition
Using Deep Convolutional Networks for Gesture Recognition in American Sign Language
https://github.com/open-mmlab/mmaction2
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/patiencefromzhou/simplehand
https://github.com/paty0504/SIGNTEGRATE
Make Skeleton-based Action Recognition Model Smaller, Faster and Better
https://github.com/pfmarteau/ShapeTimeSeparation
Times series averaging and denoising from a probabilistic perspective on time-elastic kernels
https://github.com/pfmarteau/eKATS
Times series averaging and denoising from a probabilistic perspective on time-elastic kernels
https://github.com/piergiaj/pytorch-i3d
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/prinshul/GWSDR
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/rajkundu/myoband
Deep Learning for Electromyographic Hand Gesture Signal Classification Using Transfer Learning
https://github.com/sailordiary/m3t.pytorch
$M^3$T: Multi-Modal Continuous Valence-Arousal Estimation in the Wild
https://github.com/sbhonde1/Multimodal-Learning
Improving the Performance of Unimodal Dynamic Hand-Gesture Recognition with Multimodal Training
https://github.com/sebastiantiesmeyer/deeplabchop3d
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/steve30572/myo_armband
Deep Learning for Electromyographic Hand Gesture Signal Classification Using Transfer Learning
https://github.com/sutariyaashish022/BabyMonitoring
A Study of Vision based Human Motion Recognition and Analysis
https://github.com/timeseriesAI/tsai/blob/main/tsai/models/XceptionTime.py
https://github.com/toytiny/radarocc
milliFlow: Scene Flow Estimation on mmWave Radar Point Cloud for Human Motion Sensing
https://github.com/vijayvee/behavior-recognition
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/vijayvee/behavior_recognition
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/woodfrog/ActionRecognition
Two-Stream Convolutional Networks for Action Recognition in Videos
https://github.com/xiuyu0000/new_papers_codes/tree/main/I3D
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/xiuyu0000/vision/blob/main/mindvision/msvideo/models/i3d.py
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/yaohungt/GSTEG_CVPR_2019
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/zhoubolei/TRN-pytorch
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.