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

TL;DR

This paper introduces a unified framework that simultaneously addresses missing and noisy modalities in low-quality multimodal data, significantly improving robustness and performance in affective computing tasks.

Contribution

The paper proposes the UMQ framework that jointly handles missing and noisy modalities, incorporating a quality estimator, quality enhancer, and a quality-aware mixture-of-experts module.

Findings

UMQ outperforms state-of-the-art baselines on multiple datasets.

The quality estimator effectively ranks modality quality without absolute labels.

The framework improves robustness in complete, missing, and noisy modality scenarios.

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.