Vision Mamba vs. MambaOut: Token Magnitude and Direction Explain Dense Prediction Advantage
A new preprint investigates how Vision Mamba (VMamba) and MambaOut, two state-space model-based vision architectures, encode visual information differently. The study finds that VMamba distributes discriminative signals across token directions, while MambaOut concentrates them in high-norm foreground tokens. This difference in encoding strategies explains why VMamba outperforms MambaOut in dense prediction tasks such as semantic segmentation, especially at high resolutions.
Why it matters: These findings could inform the design of more effective vision backbones for dense prediction tasks by highlighting the importance of token magnitude and direction in model representations.
Full story at: arXiv Computer Vision ↗