# DDUNet: Dual Dynamic U-Net for Highly-Efficient Cloud Segmentation

arXiv: https://arxiv.org/abs/2501.15385 · HTML: https://mlsanity.com/paper/2501.15385/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 50 references that are in the map.
- First posted on arXiv 26 Jan 2025.

## Builds on (most cited first, 28 of 28)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [Pyramid Scene Parsing Network](https://mlsanity.com/paper/1612.01105/) (2016)
- [Rethinking Atrous Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1706.05587/) (2017)
- [TFB: Towards Comprehensive and Fair Benchmarking of Time Series Forecasting Methods](https://mlsanity.com/paper/2403.20150/) (2024)
- [DUET: Dual Clustering Enhanced Multivariate Time Series Forecasting](https://mlsanity.com/paper/2412.10859/) (2024)
- [CATCH: Channel-Aware multivariate Time Series Anomaly Detection via Frequency Patching](https://mlsanity.com/paper/2410.12261/) (2024)
- [MultiRC: Joint Learning for Time Series Anomaly Prediction and Detection with Multi-scale Reconstructive Contrast](https://mlsanity.com/paper/2410.15997/) (2024)
- [CT-PatchTST: Channel-Time Patch Time-Series Transformer for Long-Term Renewable Energy Forecasting](https://mlsanity.com/paper/2501.08620/) (2025)
- [CloudSegNet: A Deep Network for Nychthemeron Cloud Image Segmentation](https://mlsanity.com/paper/1904.07979/) (2019)
- [Superpixel-based and Spatially-regularized Diffusion Learning for Unsupervised Hyperspectral Image Clustering](https://mlsanity.com/paper/2312.15447/) (2023)
- [AlignGroup: Learning and Aligning Group Consensus with Member Preferences for Group Recommendation](https://mlsanity.com/paper/2409.02580/) (2024)
- [Color-based Segmentation of Sky/Cloud Images From Ground-based Cameras](https://mlsanity.com/paper/1606.03669/) (2016)
- [AirShot: Efficient Few-Shot Detection for Autonomous Exploration](https://mlsanity.com/paper/2404.05069/) (2024)
- [Systematic study of color spaces and components for the segmentation of sky/cloud images](https://mlsanity.com/paper/1701.04520/) (2017)
- [Nighttime sky/cloud image segmentation](https://mlsanity.com/paper/1705.10583/) (2017)
- [Multi-label Cloud Segmentation Using a Deep Network](https://mlsanity.com/paper/1903.06562/) (2019)
- [DMCNet: Diversified Model Combination Network for Understanding Engagement from Video Screengrabs](https://mlsanity.com/paper/2204.06454/) (2022)
- [AbHE: All Attention-based Homography Estimation](https://mlsanity.com/paper/2212.03029/) (2022)
- [RapVerse: Coherent Vocals and Whole-Body Motions Generations from Text](https://mlsanity.com/paper/2405.20336/) (2024)
- [CPDR: Towards Highly-Efficient Salient Object Detection via Crossed Post-decoder Refinement](https://mlsanity.com/paper/2501.06441/) (2025)
- [Stereo Matching Based on Visual Sensitive Information](https://mlsanity.com/paper/2105.10831/) (2021)
- [Real-Time Localization and Bimodal Point Pattern Analysis of Palms Using UAV Imagery](https://mlsanity.com/paper/2410.11124/) (2024)
- [UCloudNet: A Residual U-Net with Deep Supervision for Cloud Image Segmentation](https://mlsanity.com/paper/2501.06440/) (2025)
- [Design of low-cost, compact and weather-proof whole sky imagers for high-dynamic-range captures](https://mlsanity.com/paper/1704.05678/) (2017)

## Built on by (most cited first, 6 of 6)

- [Enhancing Customer Contact Efficiency with Graph Neural Networks in Credit Card Fraud Detection Workflow](https://mlsanity.com/paper/2504.02275/) (2025)
- [K2VAE: A Koopman-Kalman Enhanced Variational AutoEncoder for Probabilistic Time Series Forecasting](https://mlsanity.com/paper/2505.23017/) (2025)
- [Image-to-Image Translation with Diffusion Transformers and CLIP-Based Image Conditioning](https://mlsanity.com/paper/2505.16001/) (2025)
- [TrafficKAN-GCN: Graph Convolutional-based Kolmogorov-Arnold Network for Traffic Flow Optimization](https://mlsanity.com/paper/2503.03276/) (2025)
- [Segregation and Context Aggregation Network for Real-time Cloud Segmentation](https://mlsanity.com/paper/2504.14178/) (2025)
- [MPCM-Net: Multi-scale network integrates partial attention convolution with Mamba for ground-based cloud image segmentation](https://mlsanity.com/paper/2511.11681/) (2025)

## Abstract (arXiv)

Cloud segmentation amounts to separating cloud pixels from non-cloud pixels in an image. Current deep learning methods for cloud segmentation suffer from three issues. (a) Constrain on their receptive field due to the fixed size of the convolution kernel. (b) Lack of robustness towards different scenarios. (c) Requirement of a large number of parameters and limitations for real-time implementation. To address these issues, we propose a Dual Dynamic U-Net (DDUNet) for supervised cloud segmentation. The DDUNet adheres to a U-Net architecture and integrates two crucial modules: the dynamic multi-scale convolution (DMSC), improving merging features under different reception fields, and the dynamic weights and bias generator (DWBG) in classification layers to enhance generalization ability. More importantly, owing to the use of depth-wise convolution, the DDUNet is a lightweight network that can achieve 95.3% accuracy on the SWINySEG dataset with only 0.33M parameters, and achieve superior performance over three different configurations of the SWINySEg dataset in both accuracy and efficiency.