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

2026-05-28 · Source: Machine Learning · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, quick

Summary

OVA-IB (One-vs-All Information Bottleneck) is a novel framework designed for arbitrary-modality alignment, addressing limitations of pairwise contrastive learning methods like CLIP in handling more than two modalities. It reinterprets multi-modal alignment through the Information Bottleneck principle, where sufficiency means preserving information predictable from other modalities, and minimality involves compressing modality-specific data not supported by them. This approach establishes a "One-vs-All" perspective for characterizing each modality. OVA-IB optimizes a tractable One-vs-All contrastive lower bound, which connects to a Dual Total Correlation-style objective. It also incorporates a parameter-free geometry-aware projection score and derives a tractable upper-bound regularizer for minimality. The framework demonstrates robust performance across classification, regression, modality-agnostic evaluation, and cross-modal retrieval benchmarks.

Key takeaway

For research scientists developing multi-modal AI systems, OVA-IB offers a principled approach to align arbitrary modalities beyond traditional pairwise methods. You should consider this Information Bottleneck framework to explicitly model higher-order dependencies, potentially improving performance in complex tasks like cross-modal retrieval or classification. Its One-vs-All view provides a robust criterion for information preservation and compression.

Key insights

OVA-IB aligns arbitrary modalities by applying the Information Bottleneck principle through a One-vs-All view, optimizing for shared and compressing unique information.

Principles

• Sufficiency preserves information predictable from other modalities.
• Minimality compresses modality-specific, unsupported information.
• Each modality is characterized relative to all others.

Method

OVA-IB optimizes a tractable One-vs-All contrastive lower bound, uses a parameter-free geometry-aware projection score, and derives an upper-bound regularizer for minimality.

In practice

• Improve multi-modal classification tasks.
• Enhance cross-modal retrieval systems.
• Support modality-agnostic evaluation.

Topics

• Multi-Modal Alignment
• Information Bottleneck
• Contrastive Learning
• One-vs-All
• Machine Learning
• Information Theory

Best for: AI Scientist, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Machine Learning.