LWGANet: 通过轻量级分组注意力解决遥感视觉任务中的空间和通道冗余
LWGANet: Addressing Spatial and Channel Redundancy in Remote Sensing Visual Tasks with Light-Weight Grouped Attention
AI总结：
LWGANet通过轻量级分组注意力模块有效解决遥感任务中的空间和通道冗余问题，提升模型效率与准确率。
摘要：
轻量级神经网络用于遥感（RS）视觉分析必须克服两个固有冗余：来自广阔、同质背景的空间冗余，以及通道冗余，其中极端的尺度变化使得单一特征空间效率低下。现有模型通常针对自然图像设计，未能在遥感场景中解决这一双重挑战。为填补这一空白，我们提出了LWGANet，一种针对遥感特定属性设计的轻量级主干网络。LWGANet引入了两个核心创新：Top-K全局特征交互（TGFI）模块，通过聚焦显著区域来缓解空间冗余；以及轻量级分组注意力（LWGA）模块，通过将通道分成专门、尺度特定的路径来解决通道冗余。通过协同解决这些核心低效问题，LWGANet在特征表示质量和计算成本之间实现了更优的权衡。在十二个不同且四个主要遥感任务——场景分类、定向目标检测、语义分割和变化检测——上的大量实验表明，LWGANet在准确性和效率上均优于最先进的轻量级主干网络。我们的工作为高效遥感图像视觉分析建立了新的、稳健的基准。
英文摘要：
Light-weight neural networks for remote sensing (RS) visual analysis must overcome two inherent redundancies: spatial redundancy from vast, homogeneous backgrounds, and channel redundancy, where extreme scale variations render a single feature space inefficient. Existing models, often designed for natural images, fail to address this dual challenge in RS scenarios. To bridge this gap, we propose LWGANet, a light-weight backbone engineered for RS-specific properties. LWGANet introduces two core innovations: a Top-K Global Feature Interaction (TGFI) module that mitigates spatial redundancy by focusing computation on salient regions, and a Light-Weight Grouped Attention (LWGA) module that resolves channel redundancy by partitioning channels into specialized, scale-specific pathways. By synergistically resolving these core inefficiencies, LWGANet achieves a superior trade-off between feature representation quality and computational cost. Extensive experiments on twelve diverse datasets across four major RS tasks--scene classification, oriented object detection, semantic segmentation, and change detection--demonstrate that LWGANet consistently outperforms state-of-the-art light-weight backbones in both accuracy and efficiency. Our work establishes a new, robust baseline for efficient visual analysis in RS images.
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