https://github.com/GibranBenitez/IPN-hand
Real-time Hand Gesture Detection and Classification Using Convolutional Neural 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.
Real-time Hand Gesture Detection and Classification Using Convolutional Neural Networks
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Semi-Supervised Classification with Graph Convolutional Networks
Learning Spatiotemporal Features with 3D Convolutional Networks
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
BSN: Boundary Sensitive Network for Temporal Action Proposal Generation
Semi-Supervised Classification with Graph Convolutional Networks
Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Semi-Supervised Classification with Graph Convolutional Networks
What and How Well You Performed? A Multitask Learning Approach to Action Quality Assessment
What and How Well You Performed? A Multitask Learning Approach to Action Quality Assessment
BMN: Boundary-Matching Network for Temporal Action Proposal Generation
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Ridiculously Fast Shot Boundary Detection with Fully Convolutional Neural Networks
A Closer Look at Spatiotemporal Convolutions for Action Recognition
MViTv2: Improved Multiscale Vision Transformers for Classification and Detection
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.