Do Vision Models Really Need Mamba? A Deep Dive into MambaOut

Background

Mamba uses a state‑space model (SSM) as its token mixer, offering linear‑time complexity compared to the quadratic cost of self‑attention. While attractive for long‑sequence tasks, prior visual Mamba variants (Vision Mamba, VMamba, etc.) often underperform convolution‑ or attention‑based models on standard vision benchmarks.

Key Question

Is the Mamba architecture truly required for visual recognition, or does its causal, long‑sequence bias make it unsuitable for many vision tasks?

Assumptions

Assumption 1: ImageNet classification does not need SSM because the task lacks both long‑sequence and causal characteristics.

Assumption 2: Object detection, instance segmentation, and semantic segmentation involve long sequences and may benefit from SSM despite not being causal.

Methodology

The authors build a family of models called MambaOut by removing the SSM token mixer and stacking Gated CNN blocks (a 7×7 depth‑wise convolution with selective channel usage). The overall architecture follows a ConvNeXt‑style four‑stage design, illustrated in Figure 5 and Figure 6.

Experiments

ImageNet Classification: MambaOut‑Small achieves 84.1% top‑1 accuracy, surpassing LocalVMamba‑S by 0.4% while using only 79% of the MACs, confirming that SSM is unnecessary for this task (Figure 7).

COCO Detection & Instance Segmentation: Using Mask R‑CNN, MambaOut outperforms some visual Mamba models but remains behind the best‑performing VMamba and LocalVMamba, indicating that SSM can still provide advantages for long‑sequence detection tasks (Figure 8).

ADE20K Semantic Segmentation: With UperNet, MambaOut‑Tiny lags behind LocalVMamba‑T by 0.5 mIoU, while still beating several visual Mamba variants, supporting the hypothesis that SSM may help long‑sequence segmentation (Figure 9).

Findings

The experiments validate Assumption 1: removing SSM improves ImageNet classification, showing that Mamba is unnecessary for non‑long‑sequence, non‑causal tasks. They also partially support Assumption 2: for detection and segmentation, which are long‑sequence tasks, Mamba‑based models still have potential, though current MambaOut designs do not yet match the strongest state‑of‑the‑art hybrids.

Conclusion

Mamba’s SSM token mixer excels when a task requires processing long token sequences with causal mixing, but it offers no benefit for standard image classification where the entire image is visible at once. Future work should explore richer hybrid architectures that retain SSM advantages for long‑sequence vision tasks while closing the performance gap on detection and segmentation.