A Comprehensive Survey on Deep Learning Methods for Multimodal Learning with Missing Modalities

Kernkonzepte

Multimodal learning systems often face the challenge of missing or incomplete data in real-world applications. This survey provides a comprehensive overview of recent deep learning techniques that address the problem of Multimodal Learning with Missing Modality (MLMM), including modality augmentation, feature space engineering, architecture engineering, and model selection approaches.

Zusammenfassung

This survey provides a comprehensive overview of deep learning techniques for Multimodal Learning with Missing Modality (MLMM). It covers the historical background, distinguishes MLMM from standard multimodal learning, and presents a detailed taxonomy of current MLMM methods.

The key highlights and insights are:

1. Modality Augmentation Methods:

   • Modality Composition: Filling in missing modalities with zero/random values, retrieved samples, or generated data.
   • Modality Generation: Using generative models like AEs, GANs, and Diffusion Models to synthesize missing modalities.

2. Feature Space Engineering Methods:

   • Regularization-based: Introducing tensor rank minimization or correlation-based regularization to learn robust representations.
   • Representation Composition: Retrieving or arithmetically combining available modality representations.
   • Representation Generation: Indirectly or directly generating representations of missing modalities.

3. Architecture Engineering Methods:

   • Attention-based: Leveraging intra- and inter-modality attention mechanisms to handle missing modalities.
   • Distillation-based: Transferring knowledge from teacher models with full modalities to student models.
   • Graph Learning-based: Exploiting graph structures to capture relationships between modalities and samples.
   • Multimodal Large Language Models (MLLMs): Utilizing the flexibility of transformer-based LLMs to process any number of modalities.

4. Model Selection Methods:

   • Ensemble: Combining predictions from multiple models to improve robustness and performance.
   • Dedicated: Allocating specialized models for different missing modality cases.
   • Discrete Scheduler: Enabling LLMs to autonomously select appropriate models based on modalities and tasks.

The survey also discusses the current application scenarios, corresponding datasets, unresolved challenges, and future research directions in the field of deep MLMM.

Statistiken

"Multimodal learning has become a crucial field in Artificial Intelligence (AI). It focuses on integrating and analyzing various data types, including visual, textual, auditory, and sensory information."
"Modern multimodal models leverage the robust generalization capabilities of deep learning to uncover complex patterns and relationships that uni-modality systems might not detect."
"Real-world examples of the missing modality problem are prevalent across various domains, such as affective computing, space exploration, and medical AI."

Zitate

"During multimodal model training and reasoning, data samples may miss certain modalities and lead to compromised model performance due to sensor limitations, cost constraints, privacy concerns, data loss, and temporal and spatial factors."
"The primary challenge in MLMM lies in dynamically and robustly handling and fusing information from any number of available modalities during training and testing, while maintaining performance comparable to that achieved with full-modality samples."
"Developing robust multimodal systems that can perform effectively with missing modalities has become a crucial focus in the field."

Wichtige Erkenntnisse aus

A Comprehensive Survey on Deep Multimodal Learning with Missing Modality

by Renjie Wu, H... um arxiv.org 09-13-2024

https://arxiv.org/pdf/2409.07825.pdf

Tiefere Fragen

How can deep MLMM methods be extended to handle dynamic changes in the availability of modalities during deployment?

Deep Multimodal Learning with Missing Modality (MLMM) methods can be extended to accommodate dynamic changes in the availability of modalities during deployment through several strategies. First, adaptive architecture design can be employed, where models are built with modular components that can be activated or deactivated based on the available modalities. This allows the model to dynamically adjust its structure and processing pathways, ensuring that it can still function effectively even when certain modalities are missing.
Second, real-time modality detection and selection mechanisms can be integrated into the MLMM framework. By continuously monitoring the availability of modalities, the system can select the most relevant models or components that correspond to the current input data. This can involve using ensemble methods that combine predictions from multiple models trained on different modality combinations, thereby enhancing robustness and performance in real-time scenarios.
Third, online learning techniques can be utilized, allowing the model to update its parameters and improve its performance based on new data as it becomes available. This is particularly useful in environments where the availability of modalities may change frequently, such as in autonomous vehicles or wearable health monitoring systems.
Lastly, attention mechanisms can be adapted to focus on the available modalities dynamically. By employing inter-modality attention methods, the model can learn to ignore missing modalities while still leveraging the information from the available ones, thus maintaining performance even in the face of incomplete data.

What are the potential ethical and privacy implications of using generative models to synthesize missing modalities, and how can these be addressed?

The use of generative models to synthesize missing modalities raises several ethical and privacy implications. One major concern is the potential for data misrepresentation. Generative models, while powerful, can produce synthetic data that may not accurately reflect the real-world distributions of the missing modalities. This can lead to biased or erroneous conclusions in applications such as medical diagnosis or sentiment analysis, where the integrity of the data is crucial.
Another significant issue is privacy concerns. In many cases, the data used to train generative models may contain sensitive information. If these models are used to generate synthetic modalities, there is a risk that they could inadvertently reveal private information or be used to reconstruct sensitive data, violating individuals' privacy rights.
To address these concerns, several strategies can be implemented. First, robust data governance frameworks should be established to ensure that data used for training generative models is anonymized and complies with privacy regulations such as GDPR. This includes implementing techniques like differential privacy, which adds noise to the data to protect individual identities while still allowing for meaningful analysis.
Second, transparency and accountability in the use of generative models should be prioritized. This can involve documenting the data sources, model architectures, and training processes, as well as providing clear guidelines on how the generated data will be used. Engaging stakeholders in discussions about the ethical implications of using generative models can also foster trust and ensure that diverse perspectives are considered.
Lastly, validation and verification processes should be put in place to assess the quality and reliability of the synthetic data generated. This includes rigorous testing to ensure that the generated modalities do not introduce biases or inaccuracies that could compromise the outcomes of downstream tasks.

Given the rapid advancements in multimodal large language models, how can their capabilities be leveraged to further enhance deep MLMM approaches across diverse application domains?

The capabilities of multimodal large language models (MLLMs) can significantly enhance deep MLMM approaches across various application domains by leveraging their advanced feature extraction, representation learning, and contextual understanding. First, MLLMs can serve as powerful feature processors that integrate information from multiple modalities, allowing for more effective handling of missing modalities. By utilizing their ability to understand and generate contextual representations, MLLMs can fill in gaps left by missing modalities, improving the robustness of predictions.
Second, MLLMs can facilitate cross-modal learning by enabling the transfer of knowledge between modalities. For instance, insights gained from textual data can inform the interpretation of visual or auditory data, and vice versa. This can be particularly beneficial in domains such as healthcare, where integrating patient history (text) with imaging data (visual) can lead to more accurate diagnoses and treatment plans.
Third, the scalability and adaptability of MLLMs can be harnessed to create more flexible MLMM systems. By fine-tuning pre-trained MLLMs on specific tasks or datasets, researchers can develop models that are not only capable of handling missing modalities but also adaptable to new modalities as they become available. This is crucial in dynamic environments such as autonomous driving or real-time surveillance, where the types of data collected may vary.
Additionally, MLLMs can enhance user interaction in applications such as virtual assistants or customer service bots by providing a more natural and intuitive interface. By understanding and generating responses based on multimodal inputs (e.g., text, voice, and images), MLLMs can improve user experience and engagement.
Finally, the integration of MLLMs into deep MLMM approaches can lead to the development of novel applications in fields such as education, entertainment, and social media. For example, MLLMs can be used to create interactive learning environments that adapt to students' needs by synthesizing information from various modalities, thereby enhancing the learning experience.
In summary, the advancements in MLLMs present a unique opportunity to enhance deep MLMM approaches, making them more robust, adaptable, and capable of addressing complex challenges across diverse application domains.