Towards Uniformity and Alignment for Multimodal Representation Learning

• URL: http://arxiv.org/abs/2602.09507v1
• Date: Tue, 10 Feb 2026 08:08:07 GMT
• Title: Towards Uniformity and Alignment for Multimodal Representation Learning
• Authors: Wenzhe Yin, Pan Zhou, Zehao Xiao, Jie Liu, Shujian Yu, Jan-Jakob Sonke, Efstratios Gavves,
• Abstract summary: We identify two conflicts in the multimodal regime, both exacerbated as the number of modalities increases.<n>We propose a principled decoupling of alignment and uniformity for multimodal representations.<n>We then provide a theoretical guarantee that our method acts as an efficient proxy for a global Hlder divergence over multiple modality distributions.
• Score: 66.87764574237532
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Multimodal representation learning aims to construct a shared embedding space in which heterogeneous modalities are semantically aligned. Despite strong empirical results, InfoNCE-based objectives introduce inherent conflicts that yield distribution gaps across modalities. In this work, we identify two conflicts in the multimodal regime, both exacerbated as the number of modalities increases: (i) an alignment-uniformity conflict, whereby the repulsion of uniformity undermines pairwise alignment, and (ii) an intra-alignment conflict, where aligning multiple modalities induces competing alignment directions. To address these issues, we propose a principled decoupling of alignment and uniformity for multimodal representations, providing a conflict-free recipe for multimodal learning that simultaneously supports discriminative and generative use cases without task-specific modules. We then provide a theoretical guarantee that our method acts as an efficient proxy for a global Hölder divergence over multiple modality distributions, and thus reduces the distribution gap among modalities. Extensive experiments on retrieval and UnCLIP-style generation demonstrate consistent gains.

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