CORE: Color Regression for Multiple Colors Fashion Garments
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Among all fashion attributes, color is challenging to detect due to its subjective perception. Existing classification approaches can not go beyond the predefined list of discrete color names. In this paper, we argue that color detection is a regression problem. Thus, we propose a new architecture, based on attention modules and in two-stages. The first stage corrects the image illumination while detecting the main discrete color name. The second stage combines a colorname-attention (dependent of the detected color) with an object-attention (dependent of the clothing category) and finally weights a spatial pooling over the image pixels' RGB values. We further expand our work for multiple colors garments. We collect a dataset where each fashion item is labeled with a continuous color palette: we empirically show the benefits of our approach.
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