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.+For+fine-grained+classification%252C+we+propose+PEEB+-+an+explainable+and+editable+classifier+to+(1)+express+the+class+name+into+a+set+of+text+descriptors+that+describe+the+visual+parts+of+that+class%253B+and+(2)+match+the+embeddings+of+the+detected+parts+to+their+textual+descriptors+in+each+class+to+compute+a+logit+score+for+classification.+In+a+zero-shot+setting+where+the+class+names+are+unknown%252C+PEEB+outperforms+CLIP+by+a+huge+margin+(%7e10x+in+top-1+accuracy).+Compared+to+part-based+classifiers%252C+PEEB+is+not+only+the+state-of-the-art+(SOTA)+on+the+supervised-learning+setting+(88.80%2525+and+92.20%2525+accuracy+on+CUB-200+and+Dogs-120%252C+respectively)+but+also+the+first+to+enable+users+to+edit+the+text+descriptors+to+form+a+new+classifier+without+any+re-training.+Compared+to+concept+bottleneck+models%252C+PEEB+is+also+the+SOTA+in+both+zero-shot+and+supervised-learning+settings.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.7d616e5201022a0668438a39f1b0ed61%3frik%3dgyQwvGTE%252f%252b0iPA%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Peeb+Arduino+Board&FORM=IRPRST&ck=748DC771A50E7DD8CA1B8DA42D52E836&selectedIndex=9&itb=0)
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