Modality Curation: Building Universal Embeddings for Advanced Multimodal
  Information Retrieval
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.
6836c8391314d4ac39aebebb
Fanheng Kong
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2025-05-28T08:41:20.041Z
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Kong
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friedrichor
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Jingyuan Zhang
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Yahui Liu
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Hongzhi Zhang
claimed_verified
2025-08-19T10:51:08.448Z
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Hongzhi Zhang
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hongzhizhang
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Shi Feng
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Xiaocui Yang
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2025-05-28T12:29:53.979Z
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Yang Xiaocui
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YangXiaocui
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Daling Wang
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Yu Tian
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2025-09-12T16:25:21.770Z
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Yu Tian
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Rainmaker
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Victoria W.
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Fuzheng Zhang
admin_assigned
2025-05-28T12:30:12.107Z
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Fuzheng Zhang
user
Edrex
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Guorui Zhou
admin_assigned
2025-05-28T12:30:18.800Z
67c6c570cf87e2d2ebfc81aa
https://cdn-avatars.huggingface.co/v1/production/uploads/67c6c570cf87e2d2ebfc81aa/7qAstZtIT86Uwrz3u_anv.jpeg
Guorui Zhou
user
GuoruiZhou
Modality Curation: Building Universal Embeddings for Advanced Multimodal
  Information Retrieval
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.
UNITE addresses challenges in multimodal information retrieval through data curation and modality-aware training, achieving state-of-the-art results across benchmarks with Modal-Aware Masked Contrastive Learning.
MAMCL Modal-Aware Masked Contrastive Learning multimodal information retrieval modality-specific data properties cross-modal alignment cross-modal representation learning