What to align in multimodal contrastive learning?

Benoit Dufumier, Javiera Castillo-Navarro, Devis Tuia, Jean-Philippe Thiran
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Abstract

Humans perceive the world through multisensory integration, blending the information of different modalities to adapt their behavior. Contrastive learning offers an appealing solution for multimodal self-supervised learning. Indeed, by considering each modality as a different view of the same entity, it learns to align features of different modalities in a shared representation space. However, this approach is intrinsically limited as it only learns shared or redundant information between modalities, while multimodal interactions can arise in other ways. In this work, we introduce CoMM, a Contrastive MultiModal learning strategy that enables the communication between modalities in a single multimodal space. Instead of imposing cross- or intra- modality constraints, we propose to align multimodal representations by maximizing the mutual information between augmented versions of these multimodal features. Our theoretical analysis shows that shared, synergistic and unique terms of information naturally emerge from this formulation, allowing us to estimate multimodal interactions beyond redundancy. We test CoMM both in a controlled and in a series of real-world settings: in the former, we demonstrate that CoMM effectively captures redundant, unique and synergistic information between modalities. In the latter, CoMM learns complex multimodal interactions and achieves state-of-the-art results on the six multimodal benchmarks.
在多模态对比学习中该如何调整?
人类通过多感官整合来感知世界,融合不同模式的信息来调整自己的行为。对比学习为多模态自监督学习提供了一种极具吸引力的解决方案。事实上,通过将每种模态视为同一实体的不同视角,对比学习可以将不同模态的特征整合到一个共享的表征空间中。然而,这种方法有其内在的局限性,因为它只能学习模态之间的共享或冗余信息,而多模态交互可以通过其他方式产生。在这项工作中,我们引入了一种对比多模态学习策略(CoMM),它能在单一多模态空间中实现模态之间的交流。我们并不强加跨模态或模内模态约束,而是建议通过最大化这些多模态特征的增强版本之间的相互信息来调整多模态表征。我们的理论分析表明,共享、协同和独特的信息项会从这一表述中自然产生,从而使我们能够估算冗余之外的多模态交互。我们在受控环境和一系列真实世界环境中测试了 CoMM:在前者中,我们证明 CoMM 能够有效捕捉多模态之间的冗余、独特和协同信息。在后者中,CoMM 学习复杂的多模态交互,并在六个多模态基准测试中取得了最先进的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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