Vision Mamba
Vision Mamba: Efficient State Space Models for Image Understanding · Also known as: ViM, Mamba for Vision
Vision Mamba is an efficient state space model approach for image understanding introduced in 2024 that adapts Mamba, a linear-complexity sequence model, to computer vision. By reformulating image tokens as sequences and using state space models, Vision Mamba achieves competitive accuracy with transformers while maintaining linear computational complexity.
Read the full method
Sign in with a free account to read this section.
Method map
The neighbourhood of related methods — select a node to explore.
When to use it
Vision Mamba is ideal when computational efficiency and speed are paramount, especially for high-resolution images or resource-constrained settings. It provides a strong alternative to vision transformers for practitioners prioritizing inference speed. Use transformers when maximum accuracy on well-studied benchmarks is required and computational cost is unconstrained. Vision Mamba excels for streaming or online vision applications where processing latency must be minimized.
Strengths & limitations
- Linear computational complexity in sequence length enables efficient processing of high-resolution images
- Significantly faster inference than transformer-based models with comparable accuracy
- State space formulation naturally supports efficient batching and recurrent processing
- Scales better to longer sequences than quadratic-complexity attention mechanisms
- Performance on small-scale benchmarks may lag behind well-tuned transformer baselines due to less mature optimization
- Limited architectural variants and pre-trained models compared to the transformer ecosystem
- Bidirectional scanning adds complexity compared to simpler sequential processing
Frequently asked
How do state space models process images without attention?
State space models maintain a hidden state that evolves as the sequence is processed. Each patch updates the state through learned state matrices. By processing patches in multiple directions (bidirectional scanning), distant patches can influence each other through the accumulated state. This differs from attention which explicitly computes interactions between all pairs.
Why is bidirectional scanning necessary?
If patches were scanned in only one direction, patches near the end of the sequence would have limited ability to influence earlier patches. Bidirectional scanning processes sequences forward and backward, allowing the final state to incorporate information from all directions. This ensures sufficient receptive field for capturing spatial relationships.
How does Vision Mamba achieve linear complexity?
Attention requires O(N²) operations to compute similarities between all token pairs. State space models use recurrent state updates that are O(N) — each patch updates the state in constant time. The total complexity is linear in sequence length, enabling processing of longer sequences and higher-resolution images.
Sources
- Zhu, L., Liao, B., Zhang, Q., Wang, X., Liu, W., & Wang, X. (2024). Vision Mamba: Efficient state space models for image understanding. In International Conference on Machine Learning. link ↗
How to cite this page
ScholarGate. (2026, June 3). Vision Mamba: Efficient State Space Models for Image Understanding. ScholarGate. https://scholargate.app/en/deep-learning/vision-mamba
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
- Mamba (State Space Model)Deep learning↔ compare
- Spatial-Temporal GCNDeep learning↔ compare
- Swin TransformerDeep learning↔ compare
- Vision TransformerDeep learning↔ compare