DecAlign: Hierarchical Cross-Modal Alignment for Decoupled Multimodal Representation Learning

• URL: http://arxiv.org/abs/2503.11892v1
• Date: Fri, 14 Mar 2025 21:47:48 GMT
• Title: DecAlign: Hierarchical Cross-Modal Alignment for Decoupled Multimodal Representation Learning
• Authors: Chengxuan Qian, Shuo Xing, Shawn Li, Yue Zhao, Zhengzhong Tu,
• Abstract summary: Multimodal representation learning aims to capture both shared and complementary semantic information across multiple modalities.<n>We introduce DecAlign, a novel hierarchical cross-modal alignment framework designed to decouple multimodal representations into modality-unique (heterogeneous) and modality-common (homogeneous) features.<n>Our experiments on four widely used multimodal benchmarks demonstrate that DecAlign consistently outperforms existing state-of-the-art methods.
• Score: 7.947217265041953
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multimodal representation learning aims to capture both shared and complementary semantic information across multiple modalities. However, the intrinsic heterogeneity of diverse modalities presents substantial challenges to achieve effective cross-modal collaboration and integration. To address this, we introduce DecAlign, a novel hierarchical cross-modal alignment framework designed to decouple multimodal representations into modality-unique (heterogeneous) and modality-common (homogeneous) features. For handling heterogeneity, we employ a prototype-guided optimal transport alignment strategy leveraging gaussian mixture modeling and multi-marginal transport plans, thus mitigating distribution discrepancies while preserving modality-unique characteristics. To reinforce homogeneity, we ensure semantic consistency across modalities by aligning latent distribution matching with Maximum Mean Discrepancy regularization. Furthermore, we incorporate a multimodal transformer to enhance high-level semantic feature fusion, thereby further reducing cross-modal inconsistencies. Our extensive experiments on four widely used multimodal benchmarks demonstrate that DecAlign consistently outperforms existing state-of-the-art methods across five metrics. These results highlight the efficacy of DecAlign in enhancing superior cross-modal alignment and semantic consistency while preserving modality-unique features, marking a significant advancement in multimodal representation learning scenarios. Our project page is at https://taco-group.github.io/DecAlign and the code is available at https://github.com/taco-group/DecAlign.

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