  While lots of people may think branding begins and ends with a logo, fashion
brands communicate their uniqueness through a wide range of visual cues such as
color, patterns and shapes. In this work, we analyze learned visual
representations by deep networks that are trained to recognize fashion brands.
In particular, the activation strength and extent of neurons are studied to
provide interesting insights about visual brand expressions. The proposed
method identifies where a brand stands in the spectrum of branding strategy,
i.e., from trademark-emblazoned goods with bold logos to implicit no logo
marketing. By quantifying attention maps, we are able to interpret the visual
characteristics of a brand present in a single image and model the general
design direction of a brand as a whole. We further investigate versatility of
neurons and discover "specialists" that are highly brand-specific and
"generalists" that detect diverse visual features. A human experiment based on
three main visual scenarios of fashion brands is conducted to verify the
alignment of our quantitative measures with the human perception of brands.
This paper demonstrate how deep networks go beyond logos in order to recognize
clothing brands in an image.
