Boosting Vision State Space Model with Fractal Scanning

Recently, foundational models have significantly advanced in different tasks, accompanied by Transformer as the general backbone. However, Transformer's quadratic complexity poses challenges for handling longer sequences and higher resolution images, which may limit foundational models further development. To alleviate this issue, various efficient State Space Models (SSMs) like Mamba have emerged, initially matching Transformer performance and gradually surpassing it. To improve the performance of SSMs in computer vision tasks, one crucial viewpoint is effective serialization of images. Existing vision Mambas, which rely on a linear scanning mechanism, often struggle to capture complex spatial relationships in 2D images. This results in feature loss during serialization and negatively impacts model performance. To overcome this limitation, we propose the use of fractal scanning curves for image serialization to enhance the Mambas’ ability to accurately model complex spatial dependencies. Additionally, unlike existing vision Mambas, which are designed with various curve scanning directions that increase the complexity, contradicting the original intent of Mamba to enhance model performance. We novelty introduce the Fractal Fusion Pathway (FFP) for our FractalMamba, which can enhance its performance efficiently. Extensive experiments underscore the superiority of our proposed FractalMamba.