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%252C+a+foundation+model+for+general+mask+segmentation.+However%252C+SAM+is+largely+tailored+for+single-modal+RGB+images%252C+limiting+its+applicability+to+multi-modal+data+captured+with+widely-adopted+sensor+suites%252C+such+as+LiDAR+plus+RGB%252C+depth+plus+RGB%252C+thermal+plus+RGB%252C+etc.+We+develop+MM-SAM%252C+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%252C+namely%252C+unsupervised+cross-modal+transfer+and+weakly-supervised+multi-modal+fusion%252C+enabling+label-efficient+and+parameter-efficient+adaptation+toward+various+sensor+modalities.+It+addresses+three+main+challenges%3a+1)+adaptation+toward+diverse+non-RGB+sensors+for+single-modal+processing%252C+2)+synergistic+processing+of+multi-modal+data+via+sensor+fusion%252C+and+3)+mask-free+training+for+different+downstream+tasks.+Extensive+experiments+show+that+MM-SAM+consistently+outperforms+SAM+by+large+margins%252C+demonstrating+its+effectiveness+and+robustness+across+various+sensors+and+data+modalities.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.194939eed4c24c4ba3300c1fc735d2f6%3frik%3dH3XhS%252bQ6p8t%252bdA%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Segment+Anything+2+Grow+Mask+with+Blur&FORM=IRPRST&ck=75781ADE24B146835029BCCBB59EAF5B&selectedIndex=21&itb=0)
