{"ID":2842638,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.11681","arxiv_id":"2511.11681","title":"MPCM-Net: Multi-scale network integrates partial attention convolution with Mamba for ground-based cloud image segmentation","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 comprises:(1)a MPC block with ParCM and ParSM that enables global spatial interaction across multi-scale cloud formations, and (2)a MPA block combining ParAM and ParSM to extract discriminative features with reduced computational complexity. On the decoder side, a M2B is employed to mitigate contextual loss through a SSHD that maintains linear complexity while enabling deep feature aggregation across spatial and scale dimensions. As a key contribution to the community, we also introduce and release a dataset CSRC, which is a clear-label, fine-grained segmentation benchmark designed to overcome the critical limitations of existing public datasets. Extensive experiments on CSRC demonstrate the superior performance of MPCM-Net over state-of-the-art methods, achieving an optimal balance between segmentation accuracy and inference speed. The dataset and source code will be available at https://github.com/she1110/CSRC.","short_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...","url_abs":"https://arxiv.org/abs/2511.11681","url_pdf":"https://arxiv.org/pdf/2511.11681v2","authors":"[\"Penghui Niu\",\"Jiashuai She\",\"Taotao Cai\",\"Yajuan Zhang\",\"Ping Zhang\",\"Junhua Gu\",\"Jianxin Li\"]","published":"2025-11-12T06:17:49Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.CV\"]","methods":"[]","has_code":false,"code_links":[{"ID":607145,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2842638,"paper_url":"https://arxiv.org/abs/2511.11681","paper_title":"MPCM-Net: Multi-scale network integrates partial attention convolution with Mamba for ground-based cloud image segmentation","repo_url":"https://github.com/she1110/CSRC","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}