Brand > Logo: Visual Analysis of Fashion Brands

TL;DR

This paper explores how deep neural networks recognize fashion brands by analyzing visual cues beyond logos, revealing insights into branding strategies and visual features through neuron activation and human validation.

Contribution

It introduces a method to interpret deep network representations of fashion brands, identifying brand-specific and general visual features and linking neural activations to human perception.

Findings

Neural activations correlate with branding strategies from logo to no-logo.

Identified 'specialist' and 'generalist' neurons for visual features.

Quantitative measures align with human perception of brands.

Abstract

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.