A Unified Model with Structured Output for Fashion Images Classification
This addresses the need for efficient and scalable image description in the dynamic fashion industry, though it is incremental as it modifies an existing architecture.
The paper tackled the problem of automatically classifying fashion images into hierarchical categories by proposing a unified end-to-end architecture that embeds hierarchical annotations directly into the model, achieving performance advantages over state-of-the-art models on a dataset of about 350k images.
A picture is worth a thousand words. Albeit a cliché, for the fashion industry, an image of a clothing piece allows one to perceive its category (e.g., dress), sub-category (e.g., day dress) and properties (e.g., white colour with floral patterns). The seasonal nature of the fashion industry creates a highly dynamic and creative domain with evermore data, making it unpractical to manually describe a large set of images (of products). In this paper, we explore the concept of visual recognition for fashion images through an end-to-end architecture embedding the hierarchical nature of the annotations directly into the model. Towards that goal, and inspired by the work of [7], we have modified and adapted the original architecture proposal. Namely, we have removed the message passing layer symmetry to cope with Farfetch category tree, added extra layers for hierarchy level specificity, and moved the message passing layer into an enriched latent space. We compare the proposed unified architecture against state-of-the-art models and demonstrate the performance advantage of our model for structured multi-level categorization on a dataset of about 350k fashion product images.