Adapting Vision-Language Models for E-commerce Understanding at Scale
Authors: name: Matteo Nulli, name: Vladimir Orshulevich, name: Tala Bazazo, name: Christian Herold, name: Michael Kozielski, name: Marcin Mazur, name: Szymon Tuzel, name: Cees G. M. Snoek, name: Seyyed Hadi Hashemi, name: Omar Javed, name: Yannick Versley, name: Shahram Khadivi
Organization: eBay

E-commerce product understanding demands by nature, strong multimodal comprehension from text, images, and structured attributes. General-purpose Vision-Language Models (VLMs) enable generalizable multimodal latent modelling, yet there is no documented, well-known strategy for adapting them to the attribute-centric, multi-image, and noisy nature of e-commerce data, without sacrificing general performance. In this work, we show through a large-scale experimental study, how targeted adaptation of general VLMs can substantially improve e-commerce performance while preserving broad multimodal capabilities. Furthermore, we propose a novel extensive evaluation suite covering deep product understanding, strict instruction following, and dynamic attribute extraction.