Computer Science > Computer Vision and Pattern Recognition
[Submitted on 19 Aug 2023 (v1), last revised 23 Dec 2023 (this version, v2)]
Title:SwinLSTM:Improving Spatiotemporal Prediction Accuracy using Swin Transformer and LSTM
View PDF HTML (experimental)Abstract:Integrating CNNs and RNNs to capture spatiotemporal dependencies is a prevalent strategy for spatiotemporal prediction tasks. However, the property of CNNs to learn local spatial information decreases their efficiency in capturing spatiotemporal dependencies, thereby limiting their prediction accuracy. In this paper, we propose a new recurrent cell, SwinLSTM, which integrates Swin Transformer blocks and the simplified LSTM, an extension that replaces the convolutional structure in ConvLSTM with the self-attention mechanism. Furthermore, we construct a network with SwinLSTM cell as the core for spatiotemporal prediction. Without using unique tricks, SwinLSTM outperforms state-of-the-art methods on Moving MNIST, Human3.6m, TaxiBJ, and KTH datasets. In particular, it exhibits a significant improvement in prediction accuracy compared to ConvLSTM. Our competitive experimental results demonstrate that learning global spatial dependencies is more advantageous for models to capture spatiotemporal dependencies. We hope that SwinLSTM can serve as a solid baseline to promote the advancement of spatiotemporal prediction accuracy. The codes are publicly available at this https URL.
Submission history
From: Song Tang [view email][v1] Sat, 19 Aug 2023 03:08:28 UTC (750 KB)
[v2] Sat, 23 Dec 2023 03:40:47 UTC (754 KB)
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