We introduce an approach that forecasts the popularity
of visual styles discovered in unlabeled images. Given a
large collection of unlabeled fashion images, we first predict
clothing attributes using a supervised deep convolutional
model. Then, we discover a “vocabulary” of latent styles
using non-negative matrix factorization. The discovered
styles account for the attribute combinations observed in the
individual garments or outfits. They have a mid-level granularity: they are more general than individual attributes (pastel, black boots), but more specific than typical style classes
defined in the literature (preppy, NAME_1, etc.) [21, 38, 34]. We
further show how to augment the visual elements with text
data, when available, to discover fashion styles. We then
train a forecasting model to represent trends in the latent
styles over time and to predict their popularity in the future.
Building on this, we show how to extract style dynamics
(trendy vs. classic vs. outdated), and forecast the key visual
attributes that will play a role in tomorrow’s fashion—all
based on learned visual models. Summarize this paragraph: In your response, capitalize a few words to stress main points. Please limit the number of words with all capital letters to less than 18.
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