https://github.com/code-implementation1/Code2/tree/main/3dcnn
Learning Spatiotemporal Features with 3D Convolutional 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.
Learning Spatiotemporal Features with 3D Convolutional Networks
ECO: Efficient Convolutional Network for Online Video Understanding
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Learning Spatiotemporal Features with 3D Convolutional Networks
Temporal Convolutional Networks for Action Segmentation and Detection
RegionViT: Regional-to-Local Attention for Vision Transformers
Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet?
BSN: Boundary Sensitive Network for Temporal Action Proposal Generation
Semi-Supervised Classification with Graph Convolutional Networks
Two-Stream Convolutional Networks for Action Recognition in Videos
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
Rethinking Video ViTs: Sparse Video Tubes for Joint Image and Video Learning
Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet?
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Semi-Supervised Classification with Graph Convolutional Networks
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Is Space-Time Attention All You Need for Video Understanding?
RegionViT: Regional-to-Local Attention for Vision Transformers
Image Manipulation Detection by Multi-View Multi-Scale Supervision
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
Semi-Supervised Classification with Graph Convolutional Networks
Unsupervised Learning of Object Keypoints for Perception and Control
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.