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+by+enhancing+various+image+interpretation+tasks.+Pretraining+is+an+active+research+topic%252C+encompassing+supervised+and+self-supervised+learning+methods+to+initialize+model+weights+effectively.+However%252C+transferring+the+pretrained+models+to+downstream+tasks+may+encounter+task+discrepancy+due+to+their+formulation+of+pretraining+as+image+classification+or+object+discrimination+tasks.+In+this+study%252C+we+explore+the+Multi-Task+Pretraining+(MTP)+paradigm+for+RS+foundation+models+to+address+this+issue.+Using+a+shared+encoder+and+task-specific+decoder+architecture%252C+we+conduct+multi-task+supervised+pretraining+on+the+SAMRS+dataset%252C+encompassing+semantic+segmentation%252C+instance+segmentation%252C+and+rotated+object+detection.+MTP+supports+both+convolutional+neural+networks+and+vision+transformer+foundation+models+with+over+300+million+parameters.+The+pretrained+models+are+finetuned+on+various+RS+downstream+tasks%252C+such+as+scene+classification%252C+horizontal+and+rotated+object+detection%252C+semantic+segmentation%252C+and+change+detection.+Extensive+experiments+across+14+datasets+demonstrate+the+superiority+of+our+models+over+existing+ones+of+similar+size+and+their+competitive+performance+compared+to+larger+state-of-the-art+models%252C+thus+validating+the+effectiveness+of+MTP.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.eb6302958ad434bca24cd96067a401e1%3frik%3dhaGjdCPIDjGYFA%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Multimodal+Remote+Sensing+Foundation+Model&FORM=IRPRST&ck=193154034F873E77375B01920DD184B3&selectedIndex=2&itb=0)
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