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

• URL: http://arxiv.org/abs/2511.12449v1
• Date: Sun, 16 Nov 2025 04:29:35 GMT
• Title: MOON2.0: Dynamic Modality-balanced Multimodal Representation Learning for E-commerce Product Understanding
• Authors: Zhanheng Nie, Chenghan Fu, Daoze Zhang, Junxian Wu, Wanxian Guan, Pengjie Wang, Jian Xu, Bo Zheng,
• Abstract summary: MOON2.0 is a dynamic modality-balanced representation learning framework for e-commerce product understanding.<n>MoE module adaptively processes input samples by their modality composition, enabling Multimodal Joint Learning.<n> MBE2.0 is a co-augmented multimodal representation benchmark for e-commerce representation learning and evaluation.
• Score: 11.989986738179427
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The rapid growth of e-commerce calls for multimodal models that comprehend rich visual and textual product information. Although recent multimodal large language models (MLLMs) for product understanding exhibit strong capability in representation learning for e-commerce, 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. MOON2.0 comprises: (1) a Modality-driven Mixture-of-Experts (MoE) module 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 introduce MBE2.0, a co-augmented multimodal representation benchmark for e-commerce representation learning and evaluation. 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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