MPCM-Net: Multi-scale network integrates partial attention convolution with Mamba for ground-based cloud image segmentation

• URL: http://arxiv.org/abs/2511.11681v1
• Date: Wed, 12 Nov 2025 06:17:49 GMT
• 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,
• Abstract summary: Ground-based cloud image segmentation is a critical research domain for photovoltaic power forecasting.<n>We propose MPCM-Net, a Multi-scale network that integrates Partial attention Convolutions with Mamba architectures to enhance segmentation accuracy and computational efficiency.<n>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.
• Score: 13.137436418148896
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• A Lightweight Group Multiscale Bidirectional Interactive Network for Real-Time Steel Surface Defect Detection [15.140649886958945]
  Group Multiscale Bidirectional Interactive (GMBI) modules enhance multiscale feature extraction and interaction.<n>Experiments on SD-Saliency-900 and NRSD-MN datasets demonstrate that GMBINet delivers competitive accuracy with real-time speeds of 1048 FPS on GPU and 16.53 FPS on CPU at 512 resolution.
  arXiv  Detail & Related papers  (2025-08-22T13:58:35Z)
• RPCANet++: Deep Interpretable Robust PCA for Sparse Object Segmentation [51.37553739930992]
  RPCANet++ is a sparse object segmentation framework that fuses the interpretability of RPCA with efficient deep architectures.<n>Our approach unfolds a relaxed RPCA model into a structured network comprising a Background Approximation Module (BAM), an Object Extraction Module (OEM) and an Image Restoration Module (IRM)<n>Experiments on diverse datasets demonstrate that RPCANet++ achieves state-of-the-art performance under various imaging scenarios.
  arXiv  Detail & Related papers  (2025-08-06T08:19:37Z)
• AMMNet: An Asymmetric Multi-Modal Network for Remote Sensing Semantic Segmentation [11.92569805944134]
  Asymmetric Multi-Modal Network (AMMNet) is a novel asymmetric architecture that achieves robust segmentation through three designs tailored for RGB-DSM input pairs.<n>AMMNet attains state-of-the-art segmentation accuracy among multi-modal networks while reducing computational and memory requirements.
  arXiv  Detail & Related papers  (2025-07-22T02:07:19Z)
• CFMD: Dynamic Cross-layer Feature Fusion for Salient Object Detection [7.262250906929891]
  Cross-layer feature pyramid networks (CFPNs) have achieved notable progress in multi-scale feature fusion and boundary detail preservation for salient object detection.<n>To address these challenges, we propose CFMD, a novel cross-layer feature pyramid network that introduces two key innovations.<n>First, we design a context-aware feature aggregation module (CFLMA), which incorporates the state-of-the-art Mamba architecture to construct a dynamic weight distribution mechanism.<n>Second, we introduce an adaptive dynamic upsampling unit (CFLMD) that preserves spatial details during resolution recovery.
  arXiv  Detail & Related papers  (2025-04-02T03:22:36Z)
• Semi-supervised Semantic Segmentation with Multi-Constraint Consistency Learning [81.02648336552421]
  We propose a Multi-Constraint Consistency Learning approach to facilitate the staged enhancement of the encoder and decoder.<n>Self-adaptive feature masking and noise injection are designed in an instance-specific manner to perturb the features for robust learning of the decoder.<n> Experimental results on Pascal VOC2012 and Cityscapes datasets demonstrate that our proposed MCCL achieves new state-of-the-art performance.
  arXiv  Detail & Related papers  (2025-03-23T03:21:33Z)
• Remote Sensing Image Segmentation Using Vision Mamba and Multi-Scale Multi-Frequency Feature Fusion [9.098711843118629]
  This paper introduces state space model (SSM) and proposes a novel hybrid semantic segmentation network based on vision Mamba (CVMH-UNet) This method designs a cross-scanning visual state space block (CVSSBlock) that uses cross 2D scanning (CS2D) to fully capture global information from multiple directions. By incorporating convolutional neural network branches to overcome the constraints of Vision Mamba (VMamba) in acquiring local information, this approach facilitates a comprehensive analysis of both global and local features.
  arXiv  Detail & Related papers  (2024-10-08T02:17:38Z)
• ELGC-Net: Efficient Local-Global Context Aggregation for Remote Sensing Change Detection [65.59969454655996]
  We propose an efficient change detection framework, ELGC-Net, which leverages rich contextual information to precisely estimate change regions. Our proposed ELGC-Net sets a new state-of-the-art performance in remote sensing change detection benchmarks. We also introduce ELGC-Net-LW, a lighter variant with significantly reduced computational complexity, suitable for resource-constrained settings.
  arXiv  Detail & Related papers  (2024-03-26T17:46:25Z)
• Object Segmentation by Mining Cross-Modal Semantics [68.88086621181628]
  We propose a novel approach by mining the Cross-Modal Semantics to guide the fusion and decoding of multimodal features. Specifically, we propose a novel network, termed XMSNet, consisting of (1) all-round attentive fusion (AF), (2) coarse-to-fine decoder (CFD), and (3) cross-layer self-supervision.
  arXiv  Detail & Related papers  (2023-05-17T14:30:11Z)
• Task-Oriented Sensing, Computation, and Communication Integration for Multi-Device Edge AI [108.08079323459822]
  This paper studies a new multi-intelligent edge artificial-latency (AI) system, which jointly exploits the AI model split inference and integrated sensing and communication (ISAC) We measure the inference accuracy by adopting an approximate but tractable metric, namely discriminant gain.
  arXiv  Detail & Related papers  (2022-07-03T06:57:07Z)
• CSRNet: Cascaded Selective Resolution Network for Real-time Semantic Segmentation [18.63596070055678]
  We propose a light Cascaded Selective Resolution Network (CSRNet) to improve the performance of real-time segmentation. The proposed network builds a three-stage segmentation system, which integrates feature information from low resolution to high resolution. Experiments on two well-known datasets demonstrate that the proposed CSRNet effectively improves the performance for real-time segmentation.
  arXiv  Detail & Related papers  (2021-06-08T14:22:09Z)
• Spatial and spectral deep attention fusion for multi-channel speech separation using deep embedding features [60.20150317299749]
  Multi-channel deep clustering (MDC) has acquired a good performance for speech separation. We propose a deep attention fusion method to dynamically control the weights of the spectral and spatial features and combine them deeply. Experimental results show that the proposed method outperforms MDC baseline and even better than the ideal binary mask (IBM)
  arXiv  Detail & Related papers  (2020-02-05T03:49:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.