CountMamba: Exploring Multi-directional Selective State-Space Models for Plant Counting
<|reference_start|>CountMamba: Exploring Multi-directional Selective State-Space Models for Plant Counting: 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.<|reference_end|>
@article{he2024countmamba:,
    title={CountMamba: Exploring Multi-directional Selective State-Space Models for
  Plant Counting},
    author={Hulingxiao He, Yaqi Zhang, Jinglin Xu, Yuxin Peng},
    journal={arXiv preprint arXiv:2410.07528},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.07528},
    primaryClass={cs.CV}
}