Do VLMs Need Vision Transformers? Evaluating State Space Models as Vision Encoders
Large vision--language models (VLMs) often use a frozen vision backbone, whose image features are mapped into a large language model through a lightweight connector. While transformer-based encoders are the standard visual backbone, we ask whether state space model (SSM) vision backbones can be a strong alternative. We systematically evaluate SSM vision backbones for VLMs in a controlled setting. Under matched ImageNet-1K initialization, the SSM backbone achieves the strongest overall performance across both VQA and grounding/localization. We further adapt both SSM and ViT-family backbones with detection or segmentation training and find that dense-task tuning generally improves performan...
| Authors | Shang-Jui Ray Kuo, Paola Cascante-Bonilla |
|---|---|
| Primary category | cs.CV |
| Published | 19 March 2026 |
| Last updated at source | 19 March 2026 |
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