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Learning Transferable Visual Models From Natural Language Supervision
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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 Transferable Visual Models From Natural Language Supervision
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
StNet: Local and Global Spatial-Temporal Modeling for Action Recognition
AVA: A Video Dataset of Spatio-temporally Localized Atomic Visual Actions
BMN: Boundary-Matching Network for Temporal Action Proposal Generation
R-C3D: Region Convolutional 3D Network for Temporal Activity Detection
Context-Aware RCNN: A Baseline for Action Detection in Videos
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
Learning Transferable Visual Models From Natural Language Supervision
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
Learning Spatiotemporal Features with 3D Convolutional Networks
Learning Spatiotemporal Features with 3D Convolutional Networks
BMN: Boundary-Matching Network for Temporal Action Proposal Generation
Learning Spatiotemporal Features with 3D Convolutional Networks
Context-Aware RCNN: A Baseline for Action Detection in Videos
FLAG3D: A 3D Fitness Activity Dataset with Language Instruction
Semi-Supervised Classification with Graph Convolutional Networks
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
A Closer Look at Spatiotemporal Convolutions for Action Recognition
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
Learning Spatiotemporal Features with 3D Convolutional Networks
Learning Spatiotemporal Features with 3D Convolutional Networks
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
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.