DGSSM formulates multimodal salient object detection as a progressive denoising process using diffusion-guided Mamba models, achieving better boundary accuracy and outperforming prior methods on 13 benchmarks.
arXiv preprint arXiv:2404.02668 (2024)
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Controlled tests on LoveDA and ISPRS Potsdam show visual SSM encoders deliver favorable speed-accuracy trade-offs but suffer most from boundary errors under domain shift, indicating that robustness and boundary-aware decoding will matter more than intra-family encoder scaling.
The paper consolidates existing research on Mamba models, their architecture variants, adaptations to different data modalities, and applications across domains.
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DGSSM: Diffusion guided state-space models for multimodal salient object detection
DGSSM formulates multimodal salient object detection as a progressive denoising process using diffusion-guided Mamba models, achieving better boundary accuracy and outperforming prior methods on 13 benchmarks.
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Controlled tests on LoveDA and ISPRS Potsdam show visual SSM encoders deliver favorable speed-accuracy trade-offs but suffer most from boundary errors under domain shift, indicating that robustness and boundary-aware decoding will matter more than intra-family encoder scaling.
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A Survey of Mamba
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