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This paper investigates adapting general-purpose Vision-Language Models (VLMs) to the complexities of e-commerce data, which is characterized by attribute-centric information, multiple images, and inherent noise. The authors demonstrate that targeted adaptation strategies can significantly enhance VLM performance on e-commerce tasks without compromising their general multimodal capabilities. They also introduce a new evaluation suite designed to assess product understanding, instruction following, and attribute extraction in the e-commerce domain.
Adapting general VLMs to e-commerce data can substantially boost performance without sacrificing broad multimodal capabilities, a finding demonstrated through a large-scale study.
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.
