SimVPv2: Towards Simple yet Powerful Spatiotemporal Predictive Learning (2211.12509v4)

Abstract: Recent years have witnessed remarkable advances in spatiotemporal predictive learning, with methods incorporating auxiliary inputs, complex neural architectures, and sophisticated training strategies. While SimVP has introduced a simpler, CNN-based baseline for this task, it still relies on heavy Unet-like architectures for spatial and temporal modeling, which still suffers from high complexity and computational overhead. In this paper, we propose SimVPv2, a streamlined model that eliminates the need for Unet architectures and demonstrates that plain stacks of convolutional layers, enhanced with an efficient Gated Spatiotemporal Attention mechanism, can deliver state-of-the-art performance. SimVPv2 not only simplifies the model architecture but also improves both performance and computational efficiency. On the standard Moving MNIST benchmark, SimVPv2 achieves superior performance compared to SimVP, with fewer FLOPs, about half the training time, and 60% faster inference efficiency. Extensive experiments across eight diverse datasets, including real-world tasks such as traffic forecasting and climate prediction, further demonstrate that SimVPv2 offers a powerful yet straightforward solution, achieving robust generalization across various spatiotemporal learning scenarios. We believe the proposed SimVPv2 can serve as a solid baseline to benefit the spatiotemporal predictive learning community.

• The paper introduces a fully convolutional architecture that replaces recurrent layers, significantly simplifying spatiotemporal predictive learning.
• It employs a spatial encoder, a spatiotemporal translator with advanced gSTA modules, and a spatial decoder to efficiently capture and reconstruct features.
• Experiments on benchmarks like Moving MNIST, TaxiBJ, and WeatherBench demonstrate faster computation with lower MSE and higher SSIM compared to traditional models.

SimVP: Towards Simple yet Powerful Spatiotemporal Predictive Learning

The paper introduces SimVP, a spatiotemporal predictive model designed to simplify the complex architectures typically associated with predictive learning tasks. The SimVP model is notably constructed entirely using convolutional networks, eschewing recurrent layers in favor of convolutional layers for both spatial and temporal data handling. This novel approach is motivated by the aim to reduce system complexity while achieving competitive performance on various benchmarks.

Architectural Overview and Methodology

SimVP is structured into three primary components: a spatial encoder, a spatiotemporal translator, and a spatial decoder. This architecture aims to handle the complexities of spatiotemporal dependencies by combining efficient feature extraction and translation:

1. Spatial Encoder: It encodes high-dimensional input data into a lower-dimensional latent space using a series of convolutional layers.
2. Spatiotemporal Translator: This core component leverages various convolutional modules, such as Inception-style or Gated Spatiotemporal Attention (gSTA) modules, to capture temporal dependencies. The gSTA, in particular, utilizes large kernel convolutions decomposed into depth-wise and dilated convolutions, providing effective attention mechanisms without resorting to transformer-like architectures.
3. Spatial Decoder: It reconstructs the predicted output frames from the learned latent representations, completing the end-to-end predictive task.

Such a design allows for efficient training and inference, making SimVP an attractive option for scenarios demanding lower computational overhead.

Experimental Evaluation

SimVP's performance was exhaustively evaluated through experiments on diverse datasets including Moving MNIST, TaxiBJ, and WeatherBench. The results illustrate the following key findings:

• Moving MNIST: SimVP demonstrated superior efficiency and prediction accuracy compared to many recurrent-based models, achieving significantly faster training and inference times while attaining lower mean squared error (MSE) and higher structural similarity index (SSIM).
• TaxiBJ (Traffic Forecasting): SimVP effectively handled complex, real-world traffic datasets, outperforming contemporaneous models by capturing sudden variations in traffic flow dynamics.
• WeatherBench (Climate Prediction): The model showed substantial improvements over traditional climate forecasting methods, excelling in tasks that require understanding complex spatiotemporal weather patterns.

SimVP's ability to generalize across datasets was further validated through transfer learning from KITTI to Caltech Pedestrian, demonstrating robust feature extraction and generalization capabilities. Moreover, the model efficiently predicted future frames with varying lengths, showcasing versatility akin to recurrent approaches but with reduced complexity.

Implications and Future Directions

SimVP refutes the hypothesis that complex recurrent architectures are imperative for effective spatiotemporal predictive learning. The results indicate potential applications in domains requiring rapid, scalable predictions, such as autonomous driving, climate modeling, and traffic management.

Future work could explore enhancing the model's scalability to handle larger datasets or higher-resolution inputs. Additionally, investigating hybrid approaches that integrate the strengths of SimVP with other advanced mechanisms, such as transformer architectures, may yield further improvements in both predictive power and computational efficiency.

The introduction of SimVP marks a promising development in predictive learning, setting a new standard for the balance between simplicity and performance. This line of research not only challenges prevailing assumptions but also opens new avenues for efficient AI applications across various domains.