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+demonstrate+impressive+capabilities+in+visual+question+answering+and+image+captioning%252C+acting+as+a+crucial+link+between+visual+and+language+models.+However%252C+existing+open-source+VLMs+heavily+rely+on+pretrained+and+frozen+vision+encoders+(such+as+CLIP).+Despite+CLIP%27s+robustness+across+diverse+domains%252C+it+still+exhibits+non-negligible+image+understanding+errors.+These+errors+propagate+to+the+VLM+responses%252C+resulting+in+sub-optimal+performance.+In+our+work%252C+we+propose+an+efficient+and+robust+method+for+updating+vision+encoders+within+VLMs.+Our+approach+selectively+and+locally+updates+encoders%252C+leading+to+substantial+performance+improvements+on+data+where+previous+mistakes+occurred%252C+while+maintaining+overall+robustness.+Furthermore%252C+we+demonstrate+the+effectiveness+of+our+method+during+continual+few-shot+updates.+Theoretical+grounding%252C+generality%252C+and+computational+efficiency+characterize+our+approach.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.dfe955962e06ef1b7ca29673f42e2bc9%3frik%3d19ljOaprq2PDJg%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Vision+Encoder+Examples&FORM=IRPRST&ck=3015B1A16D54B4D65B9A80FB27B2D330&selectedIndex=34&itb=0)
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