The steady rise of online shopping goes hand in hand with the development of increasingly complex ML and NLP models. While most use cases are cast as specialized supervised learning problems, we argue that practitioners would greatly benefit from more transferable representations of products. In this work, we build on recent developments in contrastive learning to train FashionCLIP, a CLIP-like model for the fashion industry. We showcase its capabilities for retrieval, classification and grounding, and release our model and code to the community.

FashionCLIP: Connecting Language and Images for Product Representations / John Chia, Patrick; Attanasio, Giuseppe; Bianchi, Federico; Terragni, Silvia; Rita Magalhães, Ana; Goncalves, Diogo; Greco, Ciro; Tagliabue, Jacopo. - (2022).

FashionCLIP: Connecting Language and Images for Product Representations

Giuseppe Attanasio;
2022

Abstract

The steady rise of online shopping goes hand in hand with the development of increasingly complex ML and NLP models. While most use cases are cast as specialized supervised learning problems, we argue that practitioners would greatly benefit from more transferable representations of products. In this work, we build on recent developments in contrastive learning to train FashionCLIP, a CLIP-like model for the fashion industry. We showcase its capabilities for retrieval, classification and grounding, and release our model and code to the community.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11583/2968275