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VMRNN: Integrating Vision Mamba and LSTM for Efficient and Accurate Spatiotemporal Forecasting
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Combining Convolutional Neural Networks (CNNs) or Vision Transformers(ViTs) with Recurrent Neural Networks (RNNs) for spatiotemporal forecasting has yielded unparalleled results in predicting temporal and spatial dynamics. However, modeling extensive global information remains a formidable challenge; CNNs are limited by their narrow receptive fields, and ViTs struggle with the intensive computational demands of their attention mechanisms. The emergence of recent Mamba-based architectures has been met with enthusiasm for their exceptional long-sequence modeling capabilities, surpassing established vision models in efficiency and accuracy, which motivates us to develop an innovative architecture tailored for spatiotemporal forecasting. In this paper, we propose the VMRNN cell, a new recurrent unit that integrates the strengths of Vision Mamba blocks with LSTM. We construct a network centered on VMRNN cells to tackle spatiotemporal prediction tasks effectively. Our extensive evaluations show that our proposed approach secures competitive results on a variety of tasks while maintaining a smaller model size. Our code is available at https://github.com/yyyujintang/VMRNN-PyTorch.
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SWRVM: Sliding Window Recurrent Vision Mamba Model for Long-Term Spatial-Temporal Prediction
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VMRA-MaR: An Asymmetry-Aware Temporal Framework for Longitudinal Breast Cancer Risk PredictionPreprint
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Convolutional Spatio-Temporal Prediction Network with Trainable Positional Encoding
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A Framework With Multi-Scale Hybrid Mamba Voxel Flow for Video Prediction
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An Edge-Deployable Lightweight Intrusion Detection System for Industrial Control
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Frequency-Enhanced Hilbert Scanning Mamba for Short-Term Arctic Sea Ice Concentration Prediction
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Spatial PDE-aware Selective State-space with Nested Memory for Mobile Traffic Grid ForecastingPreprint
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Diffusion Distillation for Remote Sensing Salient Object Detection
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FlowGPT: A GPT-Conditioned Vision-Mamba Framework for Fine-Grained Urban Flow Inferences
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A Video Frame Prediction Method Based on Latent-Space Autoregressive Modeling
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ReCalib: Representation Reconstruction for Online LiDAR-Camera Calibration
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Improving 10 m Wind Speed Forecasts over the Northwest Pacific Using a Deep Learning Network
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Learning Video Dynamics with Predictive Differentiable Rendering
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SCVM3D: A Hybrid Backbone With Sparse Convolutions and Mamba-2 for Voxel-Based 3D Object Detection
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Self-Distilled Spatiotemporal Mamba Model for Efficient Video Prediction
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Metadata
- Authors
- Tang, Y., Dong, P., Tang, Z., Chu, X., & Liang, J.
- Affiliations
- Hong Kong University of Science and Technology (HK)
- Venue
- 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
- Publisher
- IEEE
- Published
- 2024-06-17
- Pages
- 5663-5673
- OpenAlex
- W4402916931
- Semantic Scholar
- 7381d645f6ad239aaef2d42899515f21ece650d3
- Cited by
- 70
- References
- 69
- Citations by year
- 2026: 29 · 2025: 40 · 2024: 1
- FWCI
- 73.44
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Rare & Special e-Zone (The Hong Kong University of Science and Technology) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Natural Science Foundation of China
- Topics
- Social Sciences › Transportation › Traffic Prediction and Management Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Signal Processing › Time Series Analysis and Forecasting
- Keywords
- spatiotemporal prediction, Vision Mamba, LSTM, long sequence modeling, CNN-LSTM hybrid model, model size reduction
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