Brand > Logo: Visual Analysis of Fashion Brands
This work addresses the challenge of interpreting visual brand expressions in fashion for researchers and marketers, though it is incremental as it applies existing deep learning methods to a new domain.
The paper tackled the problem of understanding how fashion brands communicate identity beyond logos by analyzing deep network activations trained for brand recognition, revealing insights into visual brand strategies and verifying alignment with human perception through experiments.
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.