Multimodal Fusion on Low-quality Data: Challenges and Recent Advances

Core Concepts

This paper surveys the common challenges and recent advances of multimodal fusion in the wild, focusing on four main challenges faced by multimodal fusion on low-quality data: noisy multimodal data, incomplete multimodal data, imbalanced multimodal data, and quality-varying multimodal data.

Abstract

This paper presents a comprehensive survey on the challenges and recent advances in multimodal fusion, particularly when dealing with low-quality multimodal data.
The key challenges identified are:

Noisy multimodal data: Multimodal data can contain complex noise from various sources, which needs to be mitigated by leveraging the correlations among different modalities.

Incomplete multimodal data: Handling missing modalities is crucial, and methods have been proposed for both imputation-based and imputation-free approaches.

Imbalanced multimodal data: The discrepancy in quality and properties across modalities can lead to the model over-relying on the predominant modality. Strategies to balance the modalities are important.

Quality-varying multimodal data: The quality of each modality can dynamically change across samples, requiring adaptive fusion methods that are aware of the varying quality.

The paper discusses technical advances in each of these areas, covering topics such as modal-specific noise reduction, cross-modal noise reduction, imputation-based and imputation-free incomplete multimodal learning, and property-discrepancy based balanced multimodal learning. It also provides a discussion of open problems and future research directions in this field.

Stats

"Multimodal data tend to contain complex noise."
"Patients may choose different medical examinations producing incomplete multimodal data."
"The visual modality is more effective than the audio modality on the whole, leading the model to take shortcuts and lack exploration of audio."
"The quality of one modality often varies for different samples due to unforeseeable environment factors or sensor issues."

Quotes

"Fusing information from different modalities offers the possibility of exploring cross-modal correlation and gaining better performance."
"There is growing recognition that widely-used AI models are often mislead by spurious correlations and biases within the low-quality data."
"Developing flexible and reliable multimodal learning methods that can handle incomplete multimodal data is a challenging yet promising research direction."

Key Insights Distilled From

Multimodal Fusion on Low-quality Data: A Comprehensive Survey

by Qingyang Zha... at arxiv.org 05-01-2024

https://arxiv.org/pdf/2404.18947.pdf

Deeper Inquiries

How can multimodal fusion leverage the inherent correlations between different modalities to effectively mitigate the impact of noise and missing data

Multimodal fusion can effectively mitigate the impact of noise and missing data by leveraging the inherent correlations between different modalities in several ways.

Correlation-based Fusion: By exploiting the correlations between modalities, multimodal fusion models can identify and reduce noise in the data. For example, in the case of noisy multimodal data, the heterogeneity of the data can be used to identify and mitigate potential noise by exploring the correlations among different modalities. Weighted average fusion, joint variation-based fusion, and joint optimization techniques can be employed to reduce noise and enhance the quality of the fused data.

Imputation Strategies: In the presence of missing data in one or more modalities, multimodal fusion models can impute the missing modalities based on the information available in other modalities. Imputation-based incomplete multimodal learning methods, such as model-agnostic imputation and learning-based imputation, can help fill in the missing data and improve the overall quality of the fused data.

Graph and Kernel Learning: Utilizing graph and kernel learning techniques, multimodal fusion models can explore the structure and relationships within the data to fill in missing information and reduce noise. By constructing graphs or kernels based on available data and leveraging the relationships between modalities, these methods can enhance the quality of the fused data.

Deep Learning Approaches: Deep learning-based methods can also be used to effectively fuse multimodal data by learning representations that capture the correlations and patterns across different modalities. Deep neural networks can extract features from each modality and combine them in a way that minimizes the impact of noise and missing data.

What are the potential drawbacks of existing balanced multimodal learning methods, and how can they be further improved to achieve more robust and generalizable performance

Existing balanced multimodal learning methods may have potential drawbacks that can be further improved to achieve more robust and generalizable performance. Some of these drawbacks include:

Over-reliance on Dominant Modalities: One common drawback is the tendency of multimodal models to rely heavily on dominant modalities with more information, while neglecting or underutilizing less informative modalities. This can lead to biased predictions and suboptimal performance.

Limited Adaptability: Current balanced multimodal learning methods may lack adaptability to dynamically changing data quality. They may not effectively adjust to variations in the quality and properties of different modalities over time, leading to decreased performance in real-world scenarios.

Complexity and Computational Cost: Some methods for balancing multimodal learning may introduce complexity and increase computational costs, making them less practical for large-scale applications or real-time processing.

To address these drawbacks and improve the performance of balanced multimodal learning methods, several strategies can be implemented:

Dynamic Weighting Schemes: Introduce dynamic weighting schemes that adaptively adjust the importance of each modality based on the quality and relevance of the data. This can help prevent over-reliance on dominant modalities and ensure a more balanced fusion of information.

Regularization Techniques: Incorporate regularization techniques that penalize the model for relying too heavily on certain modalities and encourage a more equitable utilization of all modalities. This can help prevent bias and improve the generalizability of the model.

Ensemble Approaches: Explore ensemble approaches that combine multiple balanced multimodal learning models to leverage the strengths of each model and mitigate individual weaknesses. Ensemble methods can enhance robustness and improve overall performance.

Given the dynamic nature of real-world multimodal data quality, how can multimodal fusion models be designed to continuously adapt and improve their performance over time

To design multimodal fusion models that can continuously adapt and improve their performance over time in response to the dynamic nature of real-world data quality, several strategies can be employed:

Online Learning: Implement online learning techniques that allow the model to update and adjust its parameters in real-time as new data becomes available. This enables the model to adapt to changes in data quality and distribution over time.

Transfer Learning: Utilize transfer learning approaches to leverage knowledge from previously seen data to improve performance on new data with varying quality. By transferring learned representations and knowledge from one task or domain to another, the model can adapt more effectively to changing data conditions.

Self-supervised Learning: Incorporate self-supervised learning methods that enable the model to learn from the data itself without requiring explicit labels. By training the model to predict missing data or generate augmented samples, it can improve its ability to handle variations in data quality.

Adaptive Fusion Strategies: Develop adaptive fusion strategies that dynamically adjust the fusion process based on the quality and reliability of each modality. By incorporating feedback mechanisms and quality assessment modules, the model can optimize its fusion process for different data conditions.

By integrating these strategies into multimodal fusion models, it is possible to create more adaptive and robust systems that can continuously improve their performance in response to the evolving nature of real-world data quality.