MOON2.0: Dynamic Modality-balanced Multimodal Representation Learning for E-commerce Product Understanding

Zhanheng Nie; Chenghan Fu; Daoze Zhang; Junxian Wu; Wanxian Guan; Pengjie Wang; Jian Xu; Bo Zheng

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arXiv · 2511.12449

MOON2.0: Dynamic Modality-balanced Multimodal Representation Learning for E-commerce Product Understanding

Zhanheng Nie·Chenghan Fu·Daoze Zhang·Junxian Wu·Wanxian Guan·Pengjie Wang·Jian Xu·Bo Zheng

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Abstract

Recent Multimodal Large Language Models (MLLMs) have significantly advanced e-commerce product understanding. However, they still face three challenges: (i) the modality imbalance induced by modality mixed training; (ii) underutilization of the intrinsic alignment relationships among visual and textual information within a product; and (iii) limited handling of noise in e-commerce multimodal data. To address these, we propose MOON2.0, a dynamic modality-balanced MultimOdal representation learning framework for e-commerce prOduct uNderstanding. It comprises: (1) a Modality-driven Mixture-of-Experts (MoE) that adaptively processes input samples by their modality composition, enabling Multimodal Joint Learning to mitigate the modality imbalance; (2) a Dual-level Alignment method to better leverage semantic alignment properties inside individual products; and (3) an MLLM-based Image-text Co-augmentation strategy that integrates textual enrichment with visual expansion, coupled with Dynamic Sample Filtering to improve training data quality. We further release MBE2.0, a co-augmented Multimodal representation Benchmark for E-commerce representation learning and evaluation at https://huggingface.co/datasets/ZHNie/MBE2.0. Experiments show that MOON2.0 delivers state-of-the-art zero-shot performance on MBE2.0 and multiple public datasets. Furthermore, attention-based heatmap visualization provides qualitative evidence of improved multimodal alignment of MOON2.0.

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BibTeX RIS

Zhanheng Nie, Chenghan Fu, Daoze Zhang, Junxian Wu, Wanxian Guan, Pengjie Wang, Jian Xu, Bo Zheng. 2025-11-16. MOON2.0: Dynamic Modality-balanced Multimodal Representation Learning for E-commerce Product Understanding. https://arxiv.org/abs/2511.12449

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