Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
125 tokens/sec
GPT-4o
53 tokens/sec
Gemini 2.5 Pro Pro
42 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
47 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

SwinLSTM:Improving Spatiotemporal Prediction Accuracy using Swin Transformer and LSTM (2308.09891v2)

Published 19 Aug 2023 in cs.CV and cs.AI

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 https://github.com/SongTang-x/SwinLSTM.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (43)
  1. Long-term on-board prediction of people in traffic scenes under uncertainty. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 4194–4202, 2018.
  2. Swin-unet: Unet-like pure transformer for medical image segmentation. In Computer Vision–ECCV 2022 Workshops: Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part III, pages 205–218. Springer, 2023.
  3. Mau: A motion-aware unit for video prediction and beyond. Advances in Neural Information Processing Systems, 34:26950–26962, 2021.
  4. A^ 2-nets: Double attention networks. Advances in neural information processing systems, 31, 2018.
  5. Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition, pages 248–255. Ieee, 2009.
  6. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929, 2020.
  7. Disentangling propagation and generation for video prediction. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 9006–9015, 2019.
  8. Disentangling physical dynamics from unknown factors for unsupervised video prediction. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11474–11484, 2020.
  9. Long short-term memory. Neural computation, 9(8):1735–1780, 1997.
  10. Human3. 6m: Large scale datasets and predictive methods for 3d human sensing in natural environments. IEEE transactions on pattern analysis and machine intelligence, 36(7):1325–1339, 2013.
  11. Dynamic filter networks. Advances in neural information processing systems, 29, 2016.
  12. Exploring spatial-temporal multi-frequency analysis for high-fidelity and temporal-consistency video prediction. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4554–4563, 2020.
  13. Varnet: Exploring variations for unsupervised video prediction. In 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 5801–5806. IEEE, 2018.
  14. Video pixel networks. In International Conference on Machine Learning, pages 1771–1779. PMLR, 2017.
  15. Adam: A method for stochastic optimization. In ICLR (Poster), 2015.
  16. Predicting future frames using retrospective cycle gan. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1811–1820, 2019.
  17. Yann LeCun. The mnist database of handwritten digits. http://yann. lecun. com/exdb/mnist/, 1998.
  18. Stochastic adversarial video prediction. arXiv preprint arXiv:1804.01523, 2018.
  19. Video prediction recalling long-term motion context via memory alignment learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3054–3063, 2021.
  20. Swin transformer: Hierarchical vision transformer using shifted windows. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 10012–10022, 2021.
  21. Understanding the effective receptive field in deep convolutional neural networks. Advances in neural information processing systems, 29, 2016.
  22. Folded recurrent neural networks for future video prediction. In Proceedings of the European Conference on Computer Vision (ECCV), pages 716–731, 2018.
  23. Recognizing human actions: a local svm approach. In Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004., volume 3, pages 32–36. IEEE, 2004.
  24. Convolutional lstm network: A machine learning approach for precipitation nowcasting. Advances in neural information processing systems, 28, 2015.
  25. Deep learning for precipitation nowcasting: A benchmark and a new model. Advances in neural information processing systems, 30, 2017.
  26. Unsupervised learning of video representations using lstms. In International conference on machine learning, pages 843–852. PMLR, 2015.
  27. Training data-efficient image transformers & distillation through attention. In International Conference on Machine Learning, pages 10347–10357. PMLR, 2021.
  28. Scaling local self-attention for parameter efficient visual backbones. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12894–12904, 2021.
  29. Attention is all you need. Advances in neural information processing systems, 30, 2017.
  30. Decomposing motion and content for natural video sequence prediction. arXiv preprint arXiv:1706.08033, 2017.
  31. Predrnn++: Towards a resolution of the deep-in-time dilemma in spatiotemporal predictive learning. In International Conference on Machine Learning, pages 5123–5132. PMLR, 2018.
  32. Eidetic 3d lstm: A model for video prediction and beyond. In International conference on learning representations, 2018.
  33. Predrnn: Recurrent neural networks for predictive learning using spatiotemporal lstms. Advances in neural information processing systems, 30, 2017.
  34. Memory in memory: A predictive neural network for learning higher-order non-stationarity from spatiotemporal dynamics. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9154–9162, 2019.
  35. Image quality assessment: from error visibility to structural similarity. IEEE transactions on image processing, 13(4):600–612, 2004.
  36. P2t: Pyramid pooling transformer for scene understanding. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022.
  37. Segformer: Simple and efficient design for semantic segmentation with transformers. Advances in Neural Information Processing Systems, 34:12077–12090, 2021.
  38. Predcnn: Predictive learning with cascade convolutions. In IJCAI, pages 2940–2947, 2018.
  39. Efficient and information-preserving future frame prediction and beyond. 2020.
  40. Deep spatio-temporal residual networks for citywide crowd flows prediction. In Thirty-first AAAI conference on artificial intelligence, 2017.
  41. Multi-scale vision longformer: A new vision transformer for high-resolution image encoding. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 2998–3008, 2021.
  42. Point transformer. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 16259–16268, 2021.
  43. Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 6881–6890, 2021.
Citations (30)
View on Semantic Scholar

Summary

  • The paper introduces a novel hybrid model that integrates Swin Transformer blocks with a simplified LSTM to capture global spatial and temporal dependencies effectively.
  • It demonstrates superior performance on datasets like Moving MNIST, Human3.6m, TaxiBJ, and KTH, surpassing baseline models in key metrics.
  • The approach offers promising theoretical insights and practical implications for applications in autonomous systems, weather forecasting, and urban planning.

Analysis of "SwinLSTM: Improving Spatiotemporal Prediction Accuracy using Swin Transformer and LSTM"

The paper "SwinLSTM: Improving Spatiotemporal Prediction Accuracy using Swin Transformer and LSTM" introduces a novel approach to enhancing the performance of spatiotemporal prediction tasks by leveraging the integration of a Vision Transformer architecture with a simplified recurrent neural network structure. This combination aims to mitigate the limitations associated with traditional models such as ConvLSTM, particularly in capturing global spatial dependencies, which is essential in the accurate prediction of complex spatiotemporal patterns.

Methodological Overview

The central innovation in the paper is the development of SwinLSTM, a recurrent cell that amalgamates the Swin Transformer block with a simplified Long Short-Term Memory (LSTM) architecture. The core premise is to replace the convolutional operations used in ConvLSTM with self-attention mechanisms that are inherent to transformer architectures, facilitating a more efficient capture of global spatial dependencies.

The architectural foundation of SwinLSTM incorporates several key components:

  • Swin Transformer Blocks: By employing these blocks, which include both the window-based multi-head self-attention (W-MSA) and shifted-window-based multi-head self-attention (SW-MSA), the model effectively processes long-range dependencies while mitigating computational overheads typically associated with global attention.
  • Simplified LSTM Structure: This adaptation serves to retain temporal dependency learning while allowing for the inclusion of self-attention to process spatial information.

The constructed predictive network based on SwinLSTM demonstrates notable performance improvements without the need for additional complex tricks or modifications. This is particularly evident in its application across diverse datasets including Moving MNIST, Human3.6m, TaxiBJ, and KTH, where it consistently surpasses baseline models in prediction accuracy.

Numerical Results and Performance

The SwinLSTM model exhibits superior performance metrics compared to state-of-the-art methods across multiple datasets. For instance:

  • On the Moving MNIST dataset, SwinLSTM achieves an MSE of 17.7 and an SSIM of 0.962, marking a significant improvement over previous models like ConvLSTM and CrevNet.
  • On the Human3.6m dataset, the MSE reaches 11.9 with an SSIM of 0.913, highlighting the model's ability to handle complex human motion prediction scenarios effectively.
  • In the KTH dataset, when tasked with predicting longer sequences, SwinLSTM consistently maintains high PSNR values, showcasing its robustness in longer temporal predictions.
  • For the TaxiBJ dataset, SwinLSTM reduces the per-frame MSE significantly, affirming its applicability in real-world, dynamic prediction environments.

These numerical results underscore SwinLSTM's enhanced capability in capturing both spatial and temporal dependencies effectively, owing to the global spatial representation provided by the Swin Transformer and its integration with temporal processing mechanisms.

Theoretical and Practical Implications

The introduction of SwinLSTM has notable implications for both theoretical and practical aspects of spatiotemporal predictions:

  • Theoretical Implications: The fusion of Swin Transformer blocks within an LSTM framework opens avenues for further exploration of hybrid transformer-based recurrent models, especially in tasks that demand high spatial awareness coupled with temporal accuracy.
  • Practical Implications: The model's ability to generalize across various types of data and its demonstrated effectiveness in resource-intensive tasks such as traffic prediction and human motion analysis signal its potential for broad applicability in fields like autonomous systems, weather forecasting, and urban planning.

Future Directions

The development of SwinLSTM signifies a stride toward enhancing the complex task of spatiotemporal prediction. Future research can explore:

  • Optimization strategies to further reduce computational costs while maximizing prediction accuracy.
  • Extending the model's applicability to other domains requiring sophisticated spatiotemporal reasoning, such as robotics and environmental monitoring.
  • Investigating the interpretability aspects of SwinLSTM to understand the contributions of various model components to prediction outcomes.

Overall, the integration of Swin Transformer capabilities with LSTM paves the way for more informed and accurate spatiotemporal predictions, elevating both academic and applied research landscapes in the field.

File Document Download Save Streamline Icon: https://streamlinehq.com PDF File Document Download Save Streamline Icon: https://streamlinehq.com Markdown
Github Logo Streamline Icon: https://streamlinehq.com

GitHub

  1. GitHub - SongTang-x/SwinLSTM (132 stars)