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We propose a data-centric pipeline able to generate exogenous observationdata for the New Fashion Product Performance Forecasting (NFPPF) problem, i.e.,predicting the performance of a brand-new clothing probe with no available pastobservations. Our pipeline manufactures the missing past starting from asingle, available image of the clothing probe. It starts by expanding textualtags associated with the image, querying related fashionable or unfashionableimages uploaded on the web at a specific time in the past. A binary classifieris robustly trained on these web images by confident learning, to learn whatwas fashionable in the past and how much the probe image conforms to thisnotion of fashionability. This compliance produces the POtential Performance(POP) time series, indicating how performing the probe could have been if itwere available earlier. POP proves to be highly predictive for the probe'sfuture performance, ameliorating the sales forecasts of all state-of-the-artmodels on the recent VISUELLE fast-fashion dataset. We also show that POPreflects the ground-truth popularity of new styles (ensembles of clothingitems) on the Fashion Forward benchmark, demonstrating that our webly-learnedsignal is a truthful expression of popularity, accessible by everyone andgeneralizable to any time of analysis. Forecasting code, data and the POP timeseries are available at:https://github.com/HumaticsLAB/POP-Mining-POtential-Performance

POP: Mining POtential Performance of new fashion products via webly cross-modal query expansion