We propose a data-centric pipeline able to generate exogenous observation
data for the New Fashion Product Performance Forecasting (NFPPF) problem, i.e.,
predicting the performance of a brand-new clothing probe with no available past
observations. Our pipeline manufactures the missing past starting from a
single, available image of the clothing probe. It starts by expanding textual
tags associated with the image, querying related fashionable or unfashionable
images uploaded on the web at a specific time in the past. A binary classifier
is robustly trained on these web images by confident learning, to learn what
was fashionable in the past and how much the probe image conforms to this
notion of fashionability. This compliance produces the POtential Performance
(POP) time series, indicating how performing the probe could have been if it
were available earlier. POP proves to be highly predictive for the probe's
future performance, ameliorating the sales forecasts of all state-of-the-art
models on the recent VISUELLE fast-fashion dataset. We also show that POP
reflects the ground-truth popularity of new styles (ensembles of clothing
items) on the Fashion Forward benchmark, demonstrating that our webly-learned
signal is a truthful expression of popularity, accessible by everyone and
generalizable to any time of analysis. Forecasting code, data and the POP time
series are available at:
https://github.com/HumaticsLAB/POP-Mining-POtential-PerformanceComment: ECCV 202
