Adapting Vision-Language Models for E-commerce Understanding at Scale

TL;DR

This paper demonstrates how to adapt general vision-language models to e-commerce data, improving product understanding and attribute extraction while maintaining their broad multimodal capabilities through a large-scale experimental study.

Contribution

It introduces a targeted adaptation strategy for VLMs tailored to e-commerce, along with a comprehensive evaluation suite for deep product understanding.

Findings

Significant performance improvements in e-commerce tasks

Preservation of general multimodal capabilities

Effective attribute extraction and instruction following

Abstract

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.