CL-25: The 2nd CL Workshop
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

Keynote Speakers

Wenya Wang

Prof. Wenya Wang is an Assistant Professor with the school of Computer Science and Engineering at Nanyang Technological University. Prior to joining NTU, she worked with Noah Smith and Hanna Hajishirzi as a Postdoc in Paul G. Allen School of Computer Science and Engineering at the University of Washington. She completed her PhD under the supervision of Sinno Jialin Pan at Nanyang Technological University.

Liyuan Wang

Prof. Liyuan Wang is currently an Assistant Professor at the Department of Psychological and Cognitive Sciences, Tsinghua University. Before that, he received both the BS degree (Aug 2013 - Jul 2017, advised by Prof. Yi Zhong) and PhD degree (Aug 2019 - Jul 2023, co-advised by Prof. Yi Zhong and Prof. Jun Zhu) from Tsinghua University, where he also conducted his postdoc research (Jul 2023 - Jul 2025, working with Prof. Jun Zhu). He has an interdisciplinary background in neuroscience and machine learning. His primary research interest lies in the development of bio-inspired machine learning methodologies and generic computational models for neuroscience. The current focus includes continual / incremental / lifelong learning and transfer learning, by exploring “natural algorithms” in biological learning and memory. He's also exploring AI algorithms that serve other scientific domains.

Jingjing Li

Dr. Jingjing Li is currently a professor at University of Electronic Science and Technology of China (UESTC). His research interest includes Computer Vision, Machine Learning and Multimedia Analysis. Specifically, he focuses on Domain Adaptation, Zero-shot Learning and Recommender Systems.

Submission

• The CL-25 will be held together with ACM MM 2025.
• Accepted papers will be selected to be presented at the workshop, and authors retain the right to submit them to other journals.
• We invite submissions of original research papers addressing but not limited to the topics as listed above. Submissions should adhere to the ACM Multimedia 2025 formatting guidelines and will undergo a rigorous peer-review process. The template can be found via: Template
• Submissions may vary in length from 4 to 8 pages, with additional pages permitted for the reference section (up to 2 pages). There is no distinction between long and short papers, but authors are free to determine the appropriate length for their paper.
• Papers have to be submitted via: Submit a Paper

Organizers

Program Committee

Student Organizer