Graph-based Vision-Language Model for Multi-Modal Retrieval of Fashion Products

Artificial Intelligence is reshaping the field of fashion industry in different ways. E-commerce retailers exploit their data through AI to enhance their search engines, make outfit suggestions and forecast the success of a specific fashion product. However, it is a challenging endeavour as the data they possess is huge, complex and multi-modal. The most common way to search for fashion products online is by matching keywords with phrases in the product's description which are often cluttered, inadequate and differ across collections and sellers. A customer may also browse an online store's taxonomy, although this is time-consuming and doesn't guarantee relevant items. With the advent of Deep Learning architectures, particularly Vision-Language models, ad-hoc solutions have been proposed to model both the product image and description to solve this problems. However, the suggested solutions do not exploit effectively the semantic or syntactic information of these modalities, and the unique qualities and relations of clothing items. In this work of thesis, a novel approach is proposed to address this issues, which aims to model and process images and text descriptions as graphs in order to exploit the relations inside and between each modality and employs specific techniques to extract syntactic and semantic information. The results obtained show promising performances on different tasks when compared to the present state-of-the-art deep learning architectures.

Lingua della moda: Proposta di traduzione tecnico-scientifica dall'italiano al russo

Fashion is a mighty social phenomenon, therefore its linguistic representation, like many other aspects, serves as an opportunity to study the bonds between two languages. The first chapter of this thesis examines both the technical jargon of dress makers and the richer, more creative language used in glossy magazines, in an attempt to determine whether the language of fashion should be considered a language for specific purpose (LSP) or not. The second chapter provides a theoretical framework for translating a technical text and contributions of different scholars are taken into account. The third chapter delves deeper into the core of the work, the technical translation itself. It analyses the different strategies, the translation transformations, that proved to be necessary in the transition from Italian into Russian. Since the lexicon of special languages is their most obvious distinguishing characteristic, the commentary focuses on what are considered the most effective ways of enriching the Russian vocabulary of fashion, international words, calques and loanwords, especially form English and French. Overall, this influence has greatly accelerated in the last past decades because of dramatic changes in society and economy.