What Makes a Style: Experimental Analysis of Fashion Prediction

Moeko Takagi, Edgar Simo-Serra, Satoshi Iizuka, Hiroshi Ishikawa; Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2017, pp. 2247-2253

In this work, we perform an experimental analysis of the differences of both how humans and machines see and distinguish fashion styles. For this purpose, we propose an expert-curated new dataset for fashion style prediction, which consists of 14 different fashion styles each with roughly 1,000 images of worn outfits. The dataset, with a total of 13,126 images, captures the diversity and complexity of modern fashion styles. We perform an extensive analysis of the dataset by benchmarking a wide variety of modern classification networks, and also perform an in-depth user study with both fashion-savvy and fashion-naive users. Our results indicate that, although classification networks are able to outperform naive users, they are still far from the performance of savvy users, for which it is important to not only consider texture and color, but subtle differences in the combination of garments.