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.

翻译：随着在线购物的不断增长，越来越复杂的机器学习和自然语言处理模型也随之发展。虽然大多数用例被视为专门的监督学习问题，但我们认为从更可传递的产品表示中获益的实践者将大大受益。在这项工作中，我们借鉴了最近在对比学习中的发展成果，使用FashionCLIP进行训练，这是一种基于对比学习的时尚行业CLIP模型。我们展示了它在检索、分类和引导方面的能力，并向社区发布了我们的模型和代码。