Continual Learning meets Multimodal Foundation Models:
Fundamentals and Advances

Call For Papers

In recent years, with the advancement of multimodal foundation models (MMFMs), there has been a growing interest in enhancing their generalization abilities through continual learning (CL) to process diverse data types, from text to visuals, and continuously update their capabilities based on real-time inputs. Despite significant advancements in theoretical research and applications of continual learning, the community remains confronted with serious challenges. Our workshop aims to provide a venue where academic researchers and industry practitioners can come together to discuss the principles, limitations, and applications of multimodal foundation models in continual learning for multimedia applications and promote the understanding of multimodal foundation models in continual learning, innovative algorithms, and research on new multimodal technologies and applications.

Scope and Topics
Interested topics will include, but not be limited to:

• Lifelong / Continual / Incremental / Online Learning
• Few-shot & Transfer Learning related to Continual Learning
• Applications and use-cases of Continual Learning
• Meta-learning & Curriculum Learning & Active Learning
• Reinforcement Learning and Robotics in Continual Learning
• Ethical and Safety considerations for machines that can learn continuously
• Continuous domain adaptation / Test-time adaptation
• Vision / Sound / Speech / Language Foundation Models in any possible combination
• Self / Semi / Weakly supervised training of MMFMs
• Multi-task and Continual Learning for MMFMs
• Efficient training and inference of MMFMs
• Parameter-efficient fine-tuning, prompting, and adapters for MMFMs
• Generative MMFMs (e.g. text-to-image / video /3D generation)
• Ethics, risks, and fairness of MMFMs
• Benchmarks, scenarios, evaluation protocols, and metrics for the above topics