Co-Learning for Missing Arbitrary Modalities in Multi-modal Classification

2026-07-27

AI Summary

This work addresses the challenge of arbitrary modality missingness during both training and inference in real-world multimodal scenarios—a setting where existing methods often fail due to their reliance on predefined missing patterns. The paper proposes a novel multimodal collaborative learning framework that abandons conventional fusion strategies and instead introduces, for the first time, a synergistic mechanism combining feature-level knowledge transfer with decision-level consistency constraints, explicitly designed to handle any missing modality configuration. Notably, the approach makes no assumptions about missingness patterns and adaptively processes any subset of input modalities. Experiments on two multimodal classification benchmarks demonstrate substantial robustness gains: the model not only outperforms competitors under single-modality missing conditions but also maintains strong performance even when only a single modality remains available.

Abstract

Multi-modal classification leverages complementary information across diverse data sources to enhance predictive performance. However, real-world scenarios subject to operational constraints, such as sensor failures or privacy restrictions, lead to inconsistent modality availability between training and inference times. To handle missing modalities, prior studies have mainly covered bimodal data setups and focused on designing robust fusion processes. Instead, we adopt a multi-modal co-learning framework that prioritizes inter-modal collaboration rather than multi-modal fusion. Specifically, we consider that any subset of modalities may be absent, without assuming predefined missing-modality patterns, an inference scenario we refer to as missing arbitrary modalities. To address this challenge, we introduce two alternative approaches that leverage information at both feature- and decision-level. Experiments on two multi-modal classification benchmarks demonstrate significant robustness gains in various missing modality conditions. The first method shows more robust behavior under minimal missing conditions, where a single modality is absent, whereas the second performs better under extreme missing conditions, where all-but-one modalities are missing. Our code is available at https://github.com/fmenat/Co4Miss.

Problem

• multi-modal classification
• missing modalities
• arbitrary modality absence
• modality availability
• real-world constraints

Innovation

• co-learning
• arbitrary modality missing
• multi-modal classification
• feature-level collaboration
• decision-level collaboration