Exploring the Use of Attention for Generation Z Fashion Style Recognition with User Annotations as Labels

Abstract: As e-commerce and online shopping have increased worldwide, the interest and research of intelligent fashion systems have expanded. Given the competitive nature of the fashion market business, digital marketplaces depend on determining customer preferences. The fashion preferences of the next generation of consumers, Generation Z, are highly discovered on social media, where new fashion styles have emerged. For digital marketplaces to gain the attraction of Generation Z consumers, an understanding of their fashion style preferences may be crucial. However, fashion style recognition remains challenging due to the subjective nature of fashion styles. Previous research has approached the task by fine-tuning pre-trained convolutional neural networks (CNNs). The disadvantage of this approach is that a CNN leveraged on its own fails to find subtle visual differences between clothing items. Hence, this thesis seeks to approach the clothing style recognition task as a fine-grained image recognition task by incorporating a component that allows the model to focus on specific parts of the input images, referred to as an attention mechanism, into the network. Specifically, a convolutional block attention module (CBAM) is added to a CNN. Based on the results, it is concluded that the fine-tuned CNN without the attention module achieves superior performance. In contrast, qualitative analysis conducted on GradCAM visualizations shows that the attention mechanism aids the CNN in capturing discriminative features, while the network without the attention module tends to make predictions based on dataset bias. For a fair comparison, future work should involve extending this research by refining the dataset or using an additional dataset.