Beyond Unimodal Learning: The Importance of Integrating Multiple Modalities for Lifelong Learning
This work addresses the problem of catastrophic forgetting in AI systems for lifelong learning, offering an incremental approach by applying multimodal integration to an existing challenge.
The paper tackles catastrophic forgetting in deep neural networks by studying multimodal continual learning, finding that leveraging multiple modalities improves representation robustness and reduces forgetting. It introduces a benchmark and a method for integrating modalities, setting a strong baseline for future research.
While humans excel at continual learning (CL), deep neural networks (DNNs) exhibit catastrophic forgetting. A salient feature of the brain that allows effective CL is that it utilizes multiple modalities for learning and inference, which is underexplored in DNNs. Therefore, we study the role and interactions of multiple modalities in mitigating forgetting and introduce a benchmark for multimodal continual learning. Our findings demonstrate that leveraging multiple views and complementary information from multiple modalities enables the model to learn more accurate and robust representations. This makes the model less vulnerable to modality-specific regularities and considerably mitigates forgetting. Furthermore, we observe that individual modalities exhibit varying degrees of robustness to distribution shift. Finally, we propose a method for integrating and aligning the information from different modalities by utilizing the relational structural similarities between the data points in each modality. Our method sets a strong baseline that enables both single- and multimodal inference. Our study provides a promising case for further exploring the role of multiple modalities in enabling CL and provides a standard benchmark for future research.