Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2410.07528 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866929535444844544 |
|---|---|
| author | He, Hulingxiao Zhang, Yaqi Xu, Jinglin Peng, Yuxin |
| author_facet | He, Hulingxiao Zhang, Yaqi Xu, Jinglin Peng, Yuxin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_07528 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | CountMamba: Exploring Multi-directional Selective State-Space Models for Plant Counting He, Hulingxiao Zhang, Yaqi Xu, Jinglin Peng, Yuxin Computer Vision and Pattern Recognition 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. |
| title | CountMamba: Exploring Multi-directional Selective State-Space Models for Plant Counting |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2410.07528 |