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：在线购物的稳步增长伴随着机器学习和自然语言处理模型日益复杂的发展。尽管大多数应用场景被设定为专门的监督学习问题，但我们认为，从业者将从更具迁移性的产品表征中受益匪浅。在本工作中，我们基于对比学习的最新进展，训练了FashionCLIP——一个面向时尚产业的类CLIP模型。我们展示了其在检索、分类和定位方面的能力，并将我们的模型和代码发布给社区。