https://github.com/a736875071/clip-vit-large-patch14
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
Sequence-to-Sequence Modeling for Action Identification at High Temporal Resolution
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Semi-Supervised Classification with Graph Convolutional Networks
Ridiculously Fast Shot Boundary Detection with Fully Convolutional Neural Networks
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
Learning Transferable Visual Models From Natural Language Supervision
Learning Spatiotemporal Features with 3D Convolutional Networks
Multigrid Predictive Filter Flow for Unsupervised Learning on Videos
Learning Spatiotemporal Features with 3D Convolutional Networks
Learning Transferable Visual Models From Natural Language Supervision
Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
VATT: Transformers for Multimodal Self-Supervised Learning from Raw Video, Audio and Text
The "something something" video database for learning and evaluating visual common sense
Semi-Supervised Classification with Graph Convolutional Networks
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
Learning Transferable Visual Models From Natural Language Supervision
Semi-Supervised Classification with Graph Convolutional Networks
Semi-Supervised Classification with Graph Convolutional Networks
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Learning from Failure: Training Debiased Classifier from Biased Classifier
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
SeCo: Exploring Sequence Supervision for Unsupervised Representation Learning
Independently Recurrent Neural Network (IndRNN): Building A Longer and Deeper RNN
Richly Activated Graph Convolutional Network for Action Recognition with Incomplete Skeletons
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.