CL-25: The 2nd CL Workshop
Continual Learning meets Multimodal Foundation Models:
Fundamentals and Advances

10:10-10:50

Keynote: Continual Learning for Multi-modal Human-centric Applications

• [Abstract]

• [Slides]

Continual learning is a fundamental mechanism for enabling long-term adaptation and knowledge accumulation in intelligent systems. However, in the era of large-scale pretraining, it remains a critical challenge to extend continual learning to dynamic, heterogeneous real-world environments. This talk presents our recent efforts on continual learning in pretrained models, with a focus on two key directions. First, we propose a modality-heterogeneous continual pretraining framework for multi-modal physiological signal generation, enabling real-time and robust monitoring of human health conditions. Second, inspired by the spatial cognition mechanisms of the biological brain, we develop embodied agents that construct and refine cognitive maps through continuous collection of spatial knowledge, thus equipping multi-modal language models with strong long-horizon generalization in complex environments. Together, these advances point toward the development of brain-inspired embodied intelligence with lifelong adaptability.

09:30-10:10

Keynote(online): Continual Learning of Visual Representations

• [Abstract]

• [Slides]

Continually learning and acquiring new concepts from a dynamically changing environment is an important requirement for an artificial intelligence system. Existing deep learning methods fail to achieve this goal and suffer from significant performance degeneration when being trained again on a new dataset. We discuss the main approaches in continual learning, trough regularization, expansion architectures and using replay mechanisms. A series of recent approaches to the continual learning of image tasks will be introduced during the plenary lecture and experimental results will be provided. Limitations of the existing continual learning systems will also be discussed together with directions of future research.

08:50-09:30

Keynote: From Context to Parameters: Generalization and Transfer in Modalities and Domains

• [Abstract]

• [Slides]

Multimodal foundation models (MFMs) are increasingly deployed in dynamic, open-world environments where they must generalize to new tasks and modalities and transfer knowledge across diverse domains. Achieving this requires tackling two complementary challenges: adapting in context for immediate, task-specific generalization, and evolving parameters for long-term retention and scalable transfer. In this keynote, I will present effective strategies through the lens of context and parameter adaptations. On the context side, I will discuss how multimodal models can leverage demonstrations and structured reasoning to generalize on the fly, adapting to new tasks without additional training. On the parameter side, I will examine how models can evolve to retain prior knowledge and expand to new modalities and domains, enabling continual learning over time.

10:50-11:30

Keynote: Generalizing vision-language models to novel domains

• [Abstract]

• [Slides]

Vision-language pretraining has enabled powerful vision-language models (VLMs) with strong zero-shot capabilities. Yet, their performance drops in domain-specific tasks, motivating research on transferring and generalizing VLM knowledge to downstream applications. This talk briefly reviews generalization settings, methodologies, and benchmarks, categorizing approaches into prompt-based, parameter-based, and feature-based methods. We also discuss our recent research on generalizing VLMs to novel domains.