https://github.com/wufan-tb/gmm_dae
Video Anomaly Detection by Estimating Likelihood of Representations
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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.
Video Anomaly Detection by Estimating Likelihood of Representations
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild
Benchmarking Deep Reinforcement Learning for Continuous Control
BSN: Boundary Sensitive Network for Temporal Action Proposal Generation
Lightweight Network Architecture for Real-Time Action Recognition
Learning Graph Convolutional Network for Skeleton-based Human Action Recognition by Neural Searching
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
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
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
A Closer Look at Spatiotemporal Convolutions for Action Recognition
BMN: Boundary-Matching Network for Temporal Action Proposal Generation
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
Semi-Supervised Classification with Graph Convolutional Networks
Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet?
Semi-Supervised Classification with Graph Convolutional Networks
DMCL: Distillation Multiple Choice Learning for Multimodal Action Recognition
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
UniPose: Unified Human Pose Estimation in Single Images and Videos
Semi-Supervised Classification with Graph Convolutional Networks
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
Constructing Stronger and Faster Baselines for Skeleton-based Action Recognition
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.