Mamba-MOC: A Multicategory Remote Object Counting via State Space Model

Understand

Multicategory remote object counting is a fundamental task in computer vision, aimed at accurately estimating the number of objects of various categories in remote images.

• Existing methods rely on CNNs and Transformers, but CNNs struggle to capture global dependencies, and Transformers are computationally expensive, which limits their effectiveness in remote applications.
• Recently, Mamba has emerged as a promising solution in the field of computer vision, offering a linear complexity for modeling global dependencies.
• To this end, we propose Mamba-MOC, a mamba-based network designed for multi-category remote object counting, which represents the first application of Mamba to remote sensing object counting.