https://github.com/santhoshpkumar/Hand-gesture-recognition-using-neural-networks
Learning Spatiotemporal Features with 3D Convolutional Networks
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Action Recognition is a computer vision task that involves recognizing human actions in videos or images. The goal is to classify and categorize the actions being performed in the video or image into a predefined set of action classes.
In the video domain, it is an open question whether training an action classification network on a sufficiently large dataset, will give a similar boost in performance when applied to a different temporal task or dataset. The challenges of building video datasets has meant that most popular benchmarks for action recognition are small, having on the order of 10k videos.
Please note some benchmarks may be located in the Action Classification or Video Classification tasks, e.g. Kinetics-400.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Learning Spatiotemporal Features with 3D Convolutional Networks
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
Learning Transferable Visual Models From Natural Language Supervision
Improving the Performance of Unimodal Dynamic Hand-Gesture Recognition with Multimodal Training
Learning Spatiotemporal Features with 3D Convolutional Networks
Online Human Action Detection using Joint Classification-Regression Recurrent Neural Networks
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
Independently Recurrent Neural Network (IndRNN): Building A Longer and Deeper RNN
Independently Recurrent Neural Network (IndRNN): Building A Longer and Deeper RNN
Action Recognition based Industrial Safety Violation Detection
Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Learning Transferable Visual Models From Natural Language Supervision
Learning Transferable Visual Models From Natural Language Supervision
PREDICT & CLUSTER: Unsupervised Skeleton Based Action Recognition
Beyond Short Snippets: Deep Networks for Video Classification
Moments in Time Dataset: one million videos for event understanding
Learning Transferable Visual Models From Natural Language Supervision
Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks
FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks
Learning Transferable Visual Models From Natural Language Supervision
Benchmarking Deep Reinforcement Learning for Continuous Control
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.