Adapting Vision-Language Models for E-commerce Understanding at Scale
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.
698eef93cace060ff123af77
Matteo Nulli
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2026-02-17T15:51:19.518Z
661d4e74b8f13412f6d48a50
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Matteo Nulli
MatteoNulli
user
MatteoNulli
698eef93cace060ff123af78
Vladimir Orshulevich
698eef93cace060ff123af79
Tala Bazazo
698eef93cace060ff123af7a
Christian Herold
698eef93cace060ff123af7b
Michael Kozielski
698eef93cace060ff123af7c
Marcin Mazur
698eef93cace060ff123af7d
Szymon Tuzel
698eef93cace060ff123af7e
Cees G. M. Snoek
698eef93cace060ff123af7f
Seyyed Hadi Hashemi
698eef93cace060ff123af80
Omar Javed
698eef93cace060ff123af81
Yannick Versley
698eef93cace060ff123af82
Shahram Khadivi
Adapting Vision-Language Models for E-commerce Understanding at Scale
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.
General-purpose Vision-Language Models can be effectively adapted for e-commerce applications through targeted techniques that enhance product understanding while maintaining broad multimodal capabilities.
Vision-Language Models multimodal comprehension e-commerce data attribute-centric multi-image noisy data generalizable multimodal latent modelling targeted adaptation deep product understanding strict instruction following dynamic attribute extraction