CountMamba：探索用于植物计数的多方向选择性状态空间模型
CountMamba: Exploring Multi-directional Selective State-Space Models for Plant Counting
AI总结：
本文提出植物计数新方法CountMamba，通过多方向状态空间组模拟不同计数专家扫描图像，并设计全局-局部自适应融合机制聚合特征，在多种植物计数任务上表现优异。
摘要：
植物计数在农业的各个阶段都至关重要，包括种子育种、发芽、栽培、施肥、授粉产量估算和收获。受到人类通过顺序扫描对高分辨率图像中的物体进行计数的启发，我们探索了通过状态空间模型（SSMs）处理植物计数任务以生成计数结果的潜力。在本文中，我们提出了一种名为 CountMamba 的新计数方法，该方法构建多个计数专家以同时从各个方向进行扫描。具体来说，我们设计了一个多方向状态空间组，以多种顺序处理图像块序列，旨在模拟不同的计数专家。我们还设计了全局-局部自适应融合，以逐样本的方式自适应地聚合从多个方向提取的全局特征和从 CNN 分支提取的局部特征。大量实验表明，所提出的 CountMamba 在各种植物计数任务上表现优异，包括玉米果穗、麦穗和高粱穗计数。
英文摘要：
Plant counting is essential in every stage of agriculture, including seed breeding, germination, cultivation, fertilization, pollination yield estimation, and harvesting. Inspired by the fact that humans count objects in high-resolution images by sequential scanning, we explore the potential of handling plant counting tasks via state space models (SSMs) for generating counting results. In this paper, we propose a new counting approach named CountMamba that constructs multiple counting experts to scan from various directions simultaneously. Specifically, we design a Multi-directional State-Space Group to process the image patch sequences in multiple orders and aim to simulate different counting experts. We also design Global-Local Adaptive Fusion to adaptively aggregate global features extracted from multiple directions and local features extracted from the CNN branch in a sample-wise manner. Extensive experiments demonstrate that the proposed CountMamba performs competitively on various plant counting tasks, including maize tassels, wheat ears, and sorghum head counting.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
