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

2026-02-12

AI Summary

This work addresses the challenges faced by general-purpose vision-language models (VLMs) in e-commerce settings, where dense attribute spaces, multi-image inputs, and noisy data hinder simultaneous optimization of domain-specific adaptation and general multimodal capabilities. To tackle this, the authors propose an e-commerce-oriented VLM adaptation strategy that incorporates multi-image fusion and structured attribute modeling through targeted fine-tuning. They further introduce a comprehensive evaluation framework encompassing deep product understanding, instruction following, and dynamic attribute extraction. Experimental results demonstrate that the proposed approach significantly enhances model performance on e-commerce tasks while effectively preserving its general multimodal generalization ability.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Computer Vision: Multi-modal Vision
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Vertical and domain-specific search

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.

Problem

• Vision-Language Models
• E-commerce
• Multimodal Understanding
• Attribute Extraction
• Model Adaptation

Innovation

• Vision-Language Models
• E-commerce Understanding
• Model Adaptation
• Multimodal Evaluation
• Attribute Extraction