https://github.com/maharshi95/Pose2vec
Unsupervised Feature Learning of Human Actions as Trajectories in Pose Embedding Manifold
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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.
Unsupervised Feature Learning of Human Actions as Trajectories in Pose Embedding Manifold
Semi-Supervised Classification with Graph Convolutional Networks
Semi-Supervised Classification with Graph Convolutional Networks
Semi-Supervised Classification with Graph Convolutional Networks
Semi-Supervised Classification with Graph Convolutional Networks
Learning Transferable Visual Models From Natural Language Supervision
Learning Transferable Visual Models From Natural Language Supervision
Learning Spatiotemporal Features with 3D Convolutional Networks
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Action Genome: Actions as Composition of Spatio-temporal Scene Graphs
Is Space-Time Attention All You Need for Video Understanding?
Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Semi-Supervised Classification with Graph Convolutional Networks
Memory Attention Networks for Skeleton-based Action Recognition
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
AR-Net: Adaptive Frame Resolution for Efficient Action Recognition
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
Learning Transferable Visual Models From Natural Language Supervision
Moments in Time Dataset: one million videos for event understanding
Learning Transferable Visual Models From Natural Language Supervision
A Closer Look at Spatiotemporal Convolutions for Action Recognition
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.