Exploring CNNs and Attention Mechanisms for Brand Identification in Fashion Runway Shows

Abstract

In today's fashion landscape, characterized by an abundance of competing brands, establishing a unique and captivating visual identity has emerged as an essential pillar of effective branding strategies.The core challenge that drives our research is the extraction of brand-specific information from a diverse array of runway fashion presentations and the subsequent classification of these images into six distinct fashion brands. To this end, we developed a sophisticated sophisticated deep learning model, specifically a Convolutional Neural Network (CNN)-based classification model enriched with attention mechanisms. Accurate brand classification could signifies the presence of a highly recognizable brand, one that boasts a robust and distinctive visual identity. Conversely, when our model yields lower accuracy in brand classification, it hints at the possibility of a weaker or less distinctive visual identity for the brand in question. The versatility and the applicability of this model in the fashion industry is evident in its multifaceted utility across various domains. Fashion brands can leverage this tool to gain insights into their brand identity, thereby enhancing their ability to resonate with their target audiences effectively. It could be transformed into a tool aimed at amplifying fashion houses' ability to resonate deeply with their target audiences, creating stronger connections and achieving greater engagement. To accomplish this, our training process heavily relies on a meticulously annotated dataset of fashion images, where each image is accompanied by detailed brand information, forming the bedrock of our model's training and learning.