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

📰 ArXiv cs.AI

MOON2.0 addresses modality imbalance, underutilization of intrinsic alignment, and noise in e-commerce multimodal data for product understanding

advanced Published 25 Mar 2026

Action Steps

Identify modality imbalance in multimodal data
Utilize intrinsic alignment relationships among visual and textual information
Implement noise handling mechanisms in e-commerce multimodal data

Who Needs to Know This

AI engineers and researchers on e-commerce product understanding teams can benefit from MOON2.0 to improve multimodal representation learning, and product managers can utilize the insights for better product recommendations

Key Insight

💡 Dynamic modality-balanced multimodal representation learning can improve e-commerce product understanding

Key Takeaways

MOON2.0 addresses modality imbalance, underutilization of intrinsic alignment, and noise in e-commerce multimodal data for product understanding

Full Article

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

Abstract:
arXiv:2511.12449v2 Announce Type: replace-cross 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 d

Read full paper → ← Back to Reads