Title: Modality Curation: Building Universal Embeddings for Advanced Multimodal Information Retrieval
Primary area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
Keywords: Universal Multimodal Retrieval, Information Retrieval, Multimodal Large Language Model

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.

TL;DR: We propose UNITE, a universal multimodal embedder that achieves SOTA performance through strategic modality-aware data curation and training configurations, providing comprehensive insights for multimodal retrieval.