EcomMMMU: Strategic Utilization of Visuals for Robust Multimodal E-Commerce Models

Computer Vision & MultiModal AI

Published: arXiv: 2508.15721v1

Authors

Xinyi Ling Hanwen Du Zhihui Zhu Xia Ning

Abstract

E-commerce platforms are rich in multimodal data, featuring a variety of images that depict product details. However, this raises an important question: do these images always enhance product understanding, or can they sometimes introduce redundancy or degrade performance? Existing datasets are limited in both scale and design, making it difficult to systematically examine this question. To this end, we introduce EcomMMMU, an e-commerce multimodal multitask understanding dataset with 406,190 samples and 8,989,510 images. EcomMMMU is comprised of multi-image visual-language data designed with 8 essential tasks and a specialized VSS subset to benchmark the capability of multimodal large language models (MLLMs) to effectively utilize visual content. Analysis on EcomMMMU reveals that product images do not consistently improve performance and can, in some cases, degrade it. This indicates that MLLMs may struggle to effectively leverage rich visual content for e-commerce tasks. Building on these insights, we propose SUMEI, a data-driven method that strategically utilizes multiple images via predicting visual utilities before using them for downstream tasks. Comprehensive experiments demonstrate the effectiveness and robustness of SUMEI. The data and code are available through https://anonymous.4open.science/r/submission25.

Paper Summary

Problem

E-commerce platforms have become essential for consumer activities, generating a vast amount of multimodal data, including product images. However, the value of these images is unclear: do they enhance product understanding, or can they introduce redundancy or degrade performance?

Key Innovation

Researchers have introduced EcomMMMU, a large-scale e-commerce multimodal multitask understanding dataset, designed to evaluate and benchmark visual utilities for e-commerce tasks. They also proposed SUMEI, a data-driven method that strategically utilizes multiple images by predicting visual utilities before using them for downstream tasks.

Practical Impact

This research has significant implications for e-commerce applications, where models can now effectively utilize visual content to improve performance and robustness. The EcomMMMU dataset and SUMEI method can be applied to various e-commerce tasks, such as question answering, query search, recommendation, product classification, and sentiment analysis.

Analogy / Intuitive Explanation

Imagine you're shopping online and want to find a product that matches your search query. Traditional models might rely solely on text information, but with SUMEI, they can strategically use multiple images to better understand the product and provide more accurate suggestions. This is like having a personal shopping assistant that can analyze multiple visual cues to give you the best results.

Paper Information

Categories:

cs.CL cs.AI

Published Date:

arXiv ID:

2508.15721v1

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