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%2C a foundation model for general mask segmentation. However%2C SAM is largely tailored for single-modal RGB images%2C limiting its applicability to multi-modal data captured with widely-adopted sensor suites%2C such as LiDAR plus RGB%2C depth plus RGB%2C thermal plus RGB%2C etc. We develop MM-SAM%2C an extension and expansion of SAM that supports cross-modal and multi-modal processing for robust and enhanced segmentation with different sensor suites. MM-SAM features two key designs%2C namely%2C unsupervised cross-modal transfer and weakly-supervised multi-modal fusion%2C enabling label-efficient and parameter-efficient adaptation toward various sensor modalities. It addresses three main challenges: 1) adaptation toward diverse non-RGB sensors for single-modal processing%2C 2) synergistic processing of multi-modal data via sensor fusion%2C and 3) mask-free training for different downstream tasks. Extensive experiments show that MM-SAM consistently outperforms SAM by large margins%2C demonstrating its effectiveness and robustness across various sensors and data modalities.?quality=80&w=800)
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