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EfficientNet: Rethinking Model Scaling for 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.
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Learning Transferable Visual Models From Natural Language Supervision
Temporal Aggregate Representations for Long-Range Video Understanding
HAKE: A Knowledge Engine Foundation for Human Activity Understanding
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
Semi-Supervised Classification with Graph Convolutional Networks
Semi-Supervised Classification with Graph Convolutional Networks
Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet?
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
Investigation of Different Skeleton Features for CNN-based 3D Action Recognition
Learning Transferable Visual Models From Natural Language Supervision
Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.