Unsupervised CNN embedding and clustering of Floquet states recovers known nondispersive wave packet regimes in driven helium without labels.
Learning Spatiotemporal Features with 3D Convolutional Networks
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abstract
We propose a simple, yet effective approach for spatiotemporal feature learning using deep 3-dimensional convolutional networks (3D ConvNets) trained on a large scale supervised video dataset. Our findings are three-fold: 1) 3D ConvNets are more suitable for spatiotemporal feature learning compared to 2D ConvNets; 2) A homogeneous architecture with small 3x3x3 convolution kernels in all layers is among the best performing architectures for 3D ConvNets; and 3) Our learned features, namely C3D (Convolutional 3D), with a simple linear classifier outperform state-of-the-art methods on 4 different benchmarks and are comparable with current best methods on the other 2 benchmarks. In addition, the features are compact: achieving 52.8% accuracy on UCF101 dataset with only 10 dimensions and also very efficient to compute due to the fast inference of ConvNets. Finally, they are conceptually very simple and easy to train and use.
years
2026 2verdicts
UNVERDICTED 2representative citing papers
Hybrid Video Swin-U-Net forecasts next-day fire incidence maps from spatio-temporal satellite and meteorological sequences for major Canadian wildfires 2014-2023.
citing papers explorer
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Unsupervised learning for the systematic identification of nondispersive wave packets in driven helium
Unsupervised CNN embedding and clustering of Floquet states recovers known nondispersive wave packet regimes in driven helium without labels.
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Spatio-Temporal Wildfire Spread Prediction in Canada using a Video Swin-Hybrid-U-Net and Satellite Imagery
Hybrid Video Swin-U-Net forecasts next-day fire incidence maps from spatio-temporal satellite and meteorological sequences for major Canadian wildfires 2014-2023.