CountMamba: Exploring Multi-directional Selective State-Space Models for Plant Counting

• URL: http://arxiv.org/abs/2410.07528v1
• Date: Thu, 10 Oct 2024 01:52:57 GMT
• Title: CountMamba: Exploring Multi-directional Selective State-Space Models for Plant Counting
• Authors: Hulingxiao He, Yaqi Zhang, Jinglin Xu, Yuxin Peng,
• Abstract summary: 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. We propose a new counting approach named CountMamba that constructs multiple counting experts to scan from various directions simultaneously.
• Score: 33.41299696340091
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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