Contrastive language and vision learning of general fashion concepts

Autor:	Chia, Patrick John, Attanasio, Giuseppe, Bianchi, Federico, Terragni, Silvia, Magalhães, Ana Rita, Goncalves, Diogo, Greco, Ciro, Tagliabue, Jacopo
Rok vydání:	2022
Předmět:	Computer Science - Information Retrieval Computer Science - Computation and Language
Druh dokumentu:	Working Paper
Popis:	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. Comment: Latest version available at https://www.nature.com/articles/s41598-022-23052-9; model available at https://huggingface.co/patrickjohncyh/fashion-clip
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2204.03972 Zobrazit plný text záznamu View this record from Arxiv