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+and+state-of-the-art+large+vision-language+models+(Claude+3.5%252C+Gemini+1.5%252C+GPT-4)+are+systematically+brittle+on+unusual+poses%252C+with+the+exception+of+Gemini+showing+excellent+robustness+in+that+condition.+As+we+limit+image+exposure+time%252C+human+performance+degrades+to+the+level+of+deep+networks%252C+suggesting+that+additional+mental+processes+(requiring+additional+time)+are+necessary+to+identify+objects+in+unusual+poses.+An+analysis+of+error+patterns+of+humans+vs.+networks+reveals+that+even+time-limited+humans+are+dissimilar+to+feed-forward+deep+networks.+In+conclusion%252C+our+comparison+reveals+that+humans+and+deep+networks+rely+on+different+mechanisms+for+recognizing+objects+in+unusual+poses.+Understanding+the+nature+of+the+mental+processes+taking+place+during+extra+viewing+time+may+be+key+to+reproduce+the+robustness+of+human+vision+in+silico.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.aca08180cd579f4e984c33b8c1106b15%3frik%3dJcsuVMZdZ6CjDg%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Human+Compared+to+Objects&FORM=IRPRST&ck=41BAA1DC45A59A79AA64AF6F909D6470&selectedIndex=26&itb=0)
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