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CDT Cdtot Cdtott
+have+demonstrated+remarkable+capabilities+in+learning+representations%252C+but+their+performance+is+compromised+when+applied+to+unseen+domains.+Previous+methods+either+engage+in+prompt+learning+during+the+training+phase+or+modify+model+parameters+at+test+time+through+entropy+minimization.+The+former+often+overlooks+unlabeled+target+data%252C+while+the+latter+doesn%27t+fully+address+domain+shifts.+In+this+work%252C+our+approach%252C+Optimal+Transport-guided+Test-Time+Visual+Prompting+(OT-VP)%252C+handles+these+problems+by+leveraging+prompt+learning+at+test+time+to+align+the+target+and+source+domains+without+accessing+the+training+process+or+altering+pre-trained+model+parameters.+This+method+involves+learning+a+universal+visual+prompt+for+the+target+domain+by+optimizing+the+Optimal+Transport+distance.OT-VP%252C+with+only+four+learned+prompt+tokens%252C+exceeds+state-of-the-art+performance+across+three+stylistic+datasets-PACS%252C+VLCS%252C+OfficeHome%252C+and+one+corrupted+dataset+ImageNet-C.+Additionally%252C+OT-VP+operates+efficiently%252C+both+in+terms+of+memory+and+computation%252C+and+is+adaptable+for+extension+to+online+settings.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.ed55df0be2e9b401d53e302065b8134e%3frik%3de7l1s%252bCsVHjH7w%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=OpenVSP+CDT+Cdtot+Cdtott&FORM=IRPRST&ck=84A0B9690F21DDEFA2C659789F55090A&selectedIndex=30&itb=0)
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