Addressing Missing and Noisy Modalities in One Solution: Unified Modality-Quality Framework for Low-quality Multimodal Data

2026-03-03

AI Summary

This work addresses the common yet often separately treated challenges of missing modalities and noisy data in real-world multimodal scenarios. To unify these issues under a single framework, the authors propose Unified Modality Quality (UMQ), which models both missingness and noise as manifestations of low-quality modalities. UMQ introduces a ranking-guided quality estimation strategy, modality baseline representations, sample-specific cross-modal fusion, and a quality-aware mixture-of-experts routing mechanism to jointly optimize learning from degraded modalities. Extensive experiments on multiple multimodal sentiment analysis benchmarks demonstrate that UMQ consistently outperforms state-of-the-art methods across various settings—including complete, missing, and noisy modalities—highlighting its robustness and effectiveness in handling real-world multimodal imperfections.

Technology Category

• Machine Learning: Multimodal Learning
• Computer Vision: Multi-modal Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Multimodal data encountered in real-world scenarios are typically of low quality, with noisy modalities and missing modalities being typical forms that severely hinder model performance and robustness. However, prior works often handle noisy and missing modalities separately. In contrast, we jointly address missing and noisy modalities to enhance model robustness in low-quality data scenarios. We regard both noisy and missing modalities as a unified low-quality modality problem, and propose a unified modality-quality (UMQ) framework to enhance low-quality representations for multimodal affective computing. Firstly, we train a quality estimator with explicit supervised signals via a rank-guided training strategy that compares the relative quality of different representations by adding a ranking constraint, avoiding training noise caused by inaccurate absolute quality labels. Then, a quality enhancer for each modality is constructed, which uses the sample-specific information provided by other modalities and the modality-specific information provided by the defined modality baseline representation to enhance the quality of unimodal representations. Finally, we propose a quality-aware mixture-of-experts module with particular routing mechanism to enable multiple modality-quality problems to be addressed more specifically. UMQ consistently outperforms state-of-the-art baselines on multiple datasets under the settings of complete, missing, and noisy modalities.

Problem

• missing modalities
• noisy modalities
• low-quality multimodal data
• multimodal robustness
• modality quality

Innovation

• Unified Modality-Quality Framework
• Low-quality Multimodal Data
• Quality Estimation
• Modality Enhancement
• Quality-aware Mixture-of-Experts