Papers › Convolutional Tensor-Train LSTM for Spatio-temporal Learning
Convolutional Tensor-Train LSTM for Spatio-temporal Learning
Jiahao Su, Wonmin Byeon, Jean Kossaifi, Furong Huang, Jan Kautz, Animashree Anandkumar
Learning from spatio-temporal data has numerous applications such as human-behavior analysis, object tracking, video compression, and physics simulation.However, existing methods still perform poorly on challenging video tasks such as long-term forecasting. This is because these kinds of challenging tasks require learning long-term spatio-temporal correlations in the video sequence. In this paper, we propose a higher-order convolutional LSTM model that can efficiently learn these correlations, along with a succinct representations of the history. This is accomplished through a novel tensor train module that performs prediction by combining convolutional features across time. To make this feasible in terms of computation and memory requirements, we propose a novel convolutional tensor-train decomposition of the higher-order model. This decomposition reduces the model complexity by jointly approximating a sequence of convolutional kernels asa low-rank tensor-train factorization. As a result, our model outperforms existing approaches, but uses only a fraction of parameters, including the baseline models.Our results achieve state-of-the-art performance in a wide range of applications and datasets, including the multi-steps video prediction on the Moving-MNIST-2and KTH action datasets as well as early activity recognition on the Something-Something V2 dataset.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Video Prediction | KTH | Conv-TT-LSTM | Cond | 10 | #23 of 31 | Archive leaderboard | report |
| Video Prediction | KTH | Conv-TT-LSTM | LPIPS | 0.196 | #23 of 31 | Archive leaderboard | report |
| Video Prediction | KTH | Conv-TT-LSTM | PSNR | 27.62 | #23 of 31 | Archive leaderboard | report |
| Video Prediction | KTH | Conv-TT-LSTM | Pred | 20 | #23 of 31 | Archive leaderboard | report |
| Video Prediction | KTH | Conv-TT-LSTM | SSIM | 0.815 | #23 of 31 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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