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PlaceRecognition
+aims+to+estimate+the+location+of+an+image+by+treating+it+as+a+retrieval+problem.+VPR+uses+a+database+of+geo-tagged+images+and+leverages+deep+neural+networks+to+extract+a+global+representation%252C+called+descriptor%252C+from+each+image.+While+the+training+data+for+VPR+models+often+originates+from+diverse%252C+geographically+scattered+sources+(geo-tagged+images)%252C+the+training+process+itself+is+typically+assumed+to+be+centralized.+This+research+revisits+the+task+of+VPR+through+the+lens+of+Federated+Learning+(FL)%252C+addressing+several+key+challenges+associated+with+this+adaptation.+VPR+data+inherently+lacks+well-defined+classes%252C+and+models+are+typically+trained+using+contrastive+learning%252C+which+necessitates+a+data+mining+step+on+a+centralized+database.+Additionally%252C+client+devices+in+federated+systems+can+be+highly+heterogeneous+in+terms+of+their+processing+capabilities.+The+proposed+FedVPR+framework+not+only+presents+a+novel+approach+for+VPR+but+also+introduces+a+new%252C+challenging%252C+and+realistic+task+for+FL+research%252C+paving+the+way+to+other+image+retrieval+tasks+in+FL.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.bec37e96063f3ceb605547743ca492e1%3frik%3dOk090i9Om16uFQ%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Visual+Place+Recognition&FORM=IRPRST&ck=E6722AB558C9689AAE781B0B8BED1471&selectedIndex=31&itb=0)
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