A new benchmark study rigorously compares visual state-space models (SSMs) like VMamba and MambaVision against traditional Vision Transformers for remote-sensing segmentation. The research found that while visual SSMs offer a good balance of accuracy and efficiency, improvements are more likely to stem from robustness-focused designs and boundary-aware decoding rather than solely scaling up the encoder. This work establishes a reproducible standard for evaluating future Mamba-based segmentation backbones. AI
RANK_REASON This is a research paper presenting a controlled benchmark and analysis of existing models.
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