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Renlong Hang

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EAAI Journal 2026 Journal Article

More realistic and accurate precipitation nowcasting with Conditional Rectified Flow Transformers

  • Yunlong Zhou
  • Chen Zhao
  • Fanfan Ji
  • Renlong Hang
  • Qingshan Liu
  • Xiao-Tong Yuan

Precipitation nowcasting plays a critical role in disaster prevention and daily life but remains challenging due to the intricate spatiotemporal dynamics of atmospheric processes. In response to these challenges, recent research has shown that diffusion models can generate visually realistic precipitation results. However, challenges such as accurately predicting precipitation positions and improving inference speed remain unresolved. To address these issues, we propose a novel Conditional Rectified Flow Transformers (CRFT) architecture to improve precipitation nowcasting, which is designed to deliver both predictive accuracy and visual realism. At its core, CRFT features an efficient latent space predictor powered by OmniFormer blocks, which integrate spatial, temporal, and spatiotemporal Transformers to holistically capture the atmosphere dynamics. We explore five variants of spatiotemporal dynamic information interactions for OmniFormer and demonstrate that integrating triple Transformers achieves the best performance. Additionally, we significantly reduce inference time by employing a rectified flow approach, achieving a reduction in inference steps by 98. 4% compared to existing methods, enabling high-quality 20-frame predictions within 2 s. Evaluated on three benchmark datasets, CRFT outperforms state-of-the-art (SOTA) models in both accuracy and quality across multiple metrics, offering an accurate and efficient solution for real-world nowcasting. The code is publicly available at https: //github. com/RuntimeWarning/CRFT.

Details DOI

AAAI Conference 2022 Conference Paper

ReX: An Efficient Approach to Reducing Memory Cost in Image Classification

  • Xuwei Qian
  • Renlong Hang
  • Qingshan Liu

Exiting simple samples in adaptive multi-exit networks through early modules is an effective way to achieve high computational efficiency. One can observe that deployments of multi-exit architectures on resource-constrained devices are easily limited by high memory footprint of early modules. In this paper, we propose a novel approach named recurrent aggregation operator (ReX), which uses recurrent neural networks (RNNs) to effectively aggregate intra-patch features within a large receptive field to get delicate local representations, while bypassing large early activations. The resulting model, named ReXNet, can be easily extended to dynamic inference by introducing a novel consistency-based early exit criteria, which is based on the consistency of classification decisions over several modules, rather than the entropy of the prediction distribution. Extensive experiments on two benchmark datasets, i. e. , Visual Wake Words, ImageNet-1k, demonstrate that our method consistently reduces the peak RAM and average latency of a wide variety of adaptive models on low-power devices.

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v2026.09.13