OVA-IB: One vs All Information Bottleneck for Multi-Modal Alignment

• URL: http://arxiv.org/abs/2605.29900v1
• Date: Thu, 28 May 2026 13:23:07 GMT
• Title: OVA-IB: One vs All Information Bottleneck for Multi-Modal Alignment
• Abstract summary: Contrastive learning is effective for aligning paired views or modalities, but alignment beyond two modalities remains non-trivial.<n>We propose OVA-IB, an Information Bottleneck framework for arbitrary-modality alignment.
• Score: 31.43351753062205
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Contrastive learning is effective for aligning paired views or modalities, but alignment beyond two modalities remains non-trivial and comparatively underexplored. Pairwise CLIP-style losses decompose multi-modal alignment into independent two-way comparisons and therefore do not explicitly model higher-order dependencies among multiple modalities. Recent beyond-pairwise objectives approach this problem from statistical or geometric perspectives, but arbitrary-modality alignment still lacks a principled criterion for defining what each modality should preserve and compress relative to the others. We revisit arbitrary-modality alignment through the Information Bottleneck principle. In multi-modal learning, sufficiency should preserve information predictable from the remaining modalities, while minimality should compress modality-specific information not supported by them. This naturally leads to a One-vs-All view, where each modality is characterized with respect to the remaining modalities. We propose OVA-IB, an Information Bottleneck framework for arbitrary-modality alignment. OVA-IB optimizes a tractable One-vs-All contrastive lower bound for sufficiency connected to a Dual Total Correlation-style objective, uses a parameter-free geometry-aware projection score, and derives a tractable upper-bound regularizer for minimality by bounding each representation's dependence on its own input with representation distributions induced by the remaining modalities. Experiments on classification, regression, modality-agnostic evaluation, and cross-modal retrieval benchmarks demonstrate strong and robust performance.

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