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[2511.11681] MPCM-Net: Multi-scale network integrates partial attention convolution with Mamba for ground-based cloud image segmentation
Summary
The paper presents MPCM-Net, a novel multi-scale network that enhances ground-based cloud image segmentation through partial attention convolution and Mamba architecture, addressing limitations in current deep learning methods.
Why It Matters
This research is significant as it tackles critical challenges in cloud image segmentation, which is vital for photovoltaic power forecasting. By improving segmentation accuracy and computational efficiency, MPCM-Net could enhance predictive models in renewable energy, contributing to more reliable solar power generation.
Key Takeaways
- MPCM-Net integrates partial attention convolution with Mamba architecture for improved segmentation.
- The model addresses limitations of existing methods, such as accuracy-throughput balance and contextual loss.
- A new dataset, CSRC, is introduced to benchmark fine-grained segmentation performance.
- Extensive experiments demonstrate MPCM-Net's superior performance over state-of-the-art methods.
- The research contributes to advancements in cloud image segmentation, crucial for photovoltaic forecasting.
Computer Science > Machine Learning arXiv:2511.11681 (cs) [Submitted on 12 Nov 2025 (v1), last revised 16 Feb 2026 (this version, v2)] Title:MPCM-Net: Multi-scale network integrates partial attention convolution with Mamba for ground-based cloud image segmentation Authors:Penghui Niu, Jiashuai She, Taotao Cai, Yajuan Zhang, Ping Zhang, Junhua Gu, Jianxin Li View a PDF of the paper titled MPCM-Net: Multi-scale network integrates partial attention convolution with Mamba for ground-based cloud image segmentation, by Penghui Niu and 6 other authors View PDF HTML (experimental) Abstract:Ground-based cloud image segmentation is a critical research domain for photovoltaic power forecasting. Current deep learning approaches primarily focus on encoder-decoder architectural refinements. However, existing methodologies exhibit several limitations:(1)they rely on dilated convolutions for multi-scale context extraction, lacking the partial feature effectiveness and interoperability of inter-channel;(2)attention-based feature enhancement implementations neglect accuracy-throughput balance; and (3)the decoder modifications fail to establish global interdependencies among hierarchical local features, limiting inference efficiency. To address these challenges, we propose MPCM-Net, a Multi-scale network that integrates Partial attention Convolutions with Mamba architectures to enhance segmentation accuracy and computational efficiency. Specifically, the encoder incorporates MPAC, which comp...