Recent Advances of Multimodal Continual Learning: A Comprehensive Survey (2410.05352v2)

Abstract: Continual learning (CL) aims to empower machine learning models to learn continually from new data, while building upon previously acquired knowledge without forgetting. As machine learning models have evolved from small to large pre-trained architectures, and from supporting unimodal to multimodal data, multimodal continual learning (MMCL) methods have recently emerged. The primary challenge of MMCL is that it goes beyond a simple stacking of unimodal CL methods, as such straightforward approaches often yield unsatisfactory performance. In this work, we present the first comprehensive survey on MMCL. We provide essential background knowledge and MMCL settings, as well as a structured taxonomy of MMCL methods. We categorize existing MMCL methods into four categories, i.e., regularization-based, architecture-based, replay-based, and prompt-based methods, explaining their methodologies and highlighting their key innovations. Additionally, to prompt further research in this field, we summarize open MMCL datasets and benchmarks, and discuss several promising future directions for investigation and development. We have also created a GitHub repository for indexing relevant MMCL papers and open resources available at https://github.com/LucyDYu/Awesome-Multimodal-Continual-Learning.

• The paper categorizes MMCL into four core approaches—regularization, architecture, replay, and prompt—to mitigate catastrophic forgetting.
• It demonstrates the use of techniques like knowledge distillation and dynamic architectures, such as MoE-Adapters4CL, to enhance multimodal integration.
• It identifies challenges like modality imbalance and advocates for research into parameter-efficient and federated MMCL to build resilient AI systems.

Insights into Multimodal Continual Learning: A Survey

The paper, entitled "Recent Advances of Multimodal Continual Learning: A Comprehensive Survey," provides a detailed examination of the emerging field of Multimodal Continual Learning (MMCL). As deep learning models evolve to accommodate diverse data modalities, the demand for continual learning models that can effectively incorporate and retain knowledge from multiple sources has become imperative. This survey serves as a foundational reference, capturing the landscape of current methodologies, challenges, and future directions within MMCL.

Key Findings

The authors define MMCL as an extension of traditional continual learning (CL), aimed at processing and synthesizing information from multiple modalities without succumbing to catastrophic forgetting. They categorize MMCL solutions into four core methodologies:

1. Regularization-based Approaches: These methods impose constraints on model parameters to mitigate forgetting, leveraging techniques like knowledge distillation (KD) to regularize outputs implicitly. Notable methods include Mod-X and MSPT, which utilize feature-based and relation-based KD tailored for multimodal data.
2. Architecture-based Approaches: Divided into fixed and dynamic architectures, this category expands model capacity to accommodate new tasks or modalities. The dynamic architecture subcategory is particularly well-represented, with methods such as MoE-Adapters4CL and EProj, which introduce task-specific components adaptively.
3. Replay-based Approaches: These methods utilize episodic memory for replaying past data, either directly storing samples or generating pseudo samples, ensuring retention of previously acquired knowledge. IncCLIP serves as an illustrative example of pseudo-replay, where past experiences are synthesized to reinforce learning.
4. Prompt-based Approaches: Emerging alongside large pre-trained models, these methods fine-tune models using minimal modifications through prompts, maintaining continuity and efficiency. This area is noted as promising yet underexplored compared to others.

Implications and Future Directions

The paper identifies challenges unique to MMCL, such as modality imbalance and complex modality interactions. These issues underscore the necessity for strategies that effectively balance and integrate diverse data types to prevent interference and forgetting. The survey also emphasizes parameter-efficient fine-tuning methods and prompts as promising solutions for resource optimization while retaining pre-trained knowledge.

Future developments hint at expanding MMCL strategies to encompass a broader range of modalities and improve the quality of modality integration. There is a clear call for methods that address parameter level and data imbalance, refined modality interaction strategies, and a deeper understanding of inter-modality influences. Furthermore, with advancements in pre-trained models, greater emphasis on maintaining the foundational capabilities of these models throughout the learning process is seen as a crucial research pathway.

The survey also hints at leveraging federated learning techniques to improve trustworthiness, advocating for research into federated multimodal continual learning to enhance privacy and robustness. This direction aligns with broader trends in AI toward distributed, secure learning environments.

Conclusion

This comprehensive survey sets the stage for intensive research in the field of multimodal continual learning. By categorizing existing methods and outlining predominant challenges, the authors provide a clear roadmap for researchers aiming to advance this domain. The integration of several data modalities into continual learning frameworks not only reflects the complexities of real-world problem-solving but also accelerates progress toward more adaptable and resilient AI systems. Through this survey, the authors invite the community to explore and innovate further, expanding the boundaries of what is possible with MMCL.