# Hybrid Mamba-Transformer

The first hybrid computer vision model combining the strengths of Mamba and Transformer, enhancing visual feature modeling efficiency by reconstructing the Mamba formula, and introducing self-attention modules in the final layers of the Mamba architecture to improve long-range spatial dependency modeling.

The first hybrid computer vision model combining the advantages of Mamba and Transformer, enhancing visual feature modeling capability by reconstructing the Mamba formula

MambaVision is a hybrid Mamba-Transformer visual backbone network designed for vision applications, combining the strengths of the Mamba formula and vision Transformers, delivering outstanding performance in image classification and downstream vision tasks.

The first hybrid computer vision model combining the strengths of Mamba and Transformer, enhancing visual feature modeling efficiency through reconstructed Mamba formulas and introducing self-attention modules at the end of the Mamba architecture to improve long-range spatial dependency modeling.

Vamba is a hybrid Mamba-Transformer architecture that achieves efficient long video understanding through cross-attention layers and Mamba-2 modules.

The first hybrid computer vision model combining the strengths of Mamba and Transformer, enhancing visual feature modeling through redesigned Mamba formulations and incorporating self-attention modules in the Mamba architecture to improve long-range spatial dependency modeling.

MambaVision is the first hybrid computer vision model combining the advantages of Mamba and Transformer, significantly enhancing the modeling capability of long-range spatial dependencies through redesigned Mamba formulas and integrated ViT modules.