  Developing deep networks that analyze fashion garments has many real-world
applications. Among all fashion attributes, color is one of the most important
yet challenging to detect. Existing approaches are classification-based and
thus cannot go beyond the list of discrete predefined color names. In this
paper, we handle color detection as a regression problem to predict the exact
RGB values. That's why in addition to a first color classifier, we include a
second regression stage for refinement in our newly proposed architecture. This
second step combines two attention models: the first depends on the type of
clothing, the second depends on the color previously detected by the
classifier. Our final prediction is the weighted spatial pooling over the image
pixels RGB values, where the illumination has been corrected. This architecture
is modular and easily expanded to detect the RGBs of all colors in a multicolor
garment. In our experiments, we show the benefits of each component of our
architecture.
