Modality Curation: Building Universal Embeddings for Advanced Multimodal
  Information Retrieval
Authors: name: Fanheng Kong; name: Jingyuan Zhang; name: Yahui Liu; name: Hongzhi Zhang; name: Shi Feng; name: Xiaocui Yang; name: Daling Wang; name: Yu Tian; name: Victoria W.; name: Fuzheng Zhang; name: Guorui Zhou
Published: 2025-05-26 08:09:44

Abstract: Multimodal information retrieval (MIR) faces inherent challenges due to the
heterogeneity of data sources and the complexity of cross-modal alignment.
While previous studies have identified modal gaps in feature spaces, a
systematic approach to address these challenges remains unexplored. In this
work, we introduce UNITE, a universal framework that tackles these challenges
through two critical yet underexplored aspects: data curation and
modality-aware training configurations. Our work provides the first
comprehensive analysis of how modality-specific data properties influence
downstream task performance across diverse scenarios. Moreover, we propose
Modal-Aware Masked Contrastive Learning (MAMCL) to mitigate the competitive
relationships among the instances of different modalities. Our framework
achieves state-of-the-art results on multiple multimodal retrieval benchmarks,
outperforming existing methods by notable margins. Through extensive
experiments, we demonstrate that strategic modality curation and tailored
training protocols are pivotal for robust cross-modal representation learning.
This work not only advances MIR performance but also provides a foundational
blueprint for future research in multimodal systems. Our project is available
at https://friedrichor.github.io/projects/UNITE.