https://github.com/Cmeijiang/PointNet_MS
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
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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.
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
Temporal Segment Networks: Towards Good Practices for Deep Action Recognition
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Benchmarking Deep Reinforcement Learning for Continuous Control
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Unsupervised Learning of Object Keypoints for Perception and Control
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
HAKE: A Knowledge Engine Foundation for Human Activity Understanding
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
Unsupervised Learning of Object Keypoints for Perception and Control
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
Two-Stream Adaptive Graph Convolutional Networks for Skeleton-Based Action Recognition
In-Context Editing: Learning Knowledge from Self-Induced Distributions
Learning Actor Relation Graphs for Group Activity Recognition
Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks
FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks
Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks
Enhancing Unsupervised Video Representation Learning by Decoupling the Scene and the Motion
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
Learning Transferable Visual Models From Natural Language Supervision
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
BSN: Boundary Sensitive Network for Temporal Action Proposal Generation
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
Learning Transferable Visual Models From Natural Language Supervision
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.