A picture is worth a thousand words. Albeit a clich\'e, 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.

本研究通过一种端到端的架构，将层次化注释的概念直接嵌入到模型中，以实现对时尚图像的视觉识别，并针对大约 350k 时尚产品图像的数据集进行了多级结构分类的性能优化和演示。