3 papers
cs.CV2026
Lifelong Representations: A Survey on Continual Self-Supervised Learning for Vision Models
Sergi Masip, Alicja Dobrzeniecka, Jonathan Swinnen +4
Traditionally, continual learning has assumed access to labeled data, yet many real-world applications -- such as lifelong robotics -- require models to adapt continuously from unl…
cs.AI2026
FF-JEPA: Long-Horizon Planning in World Models with Latent Planners
Sergi Masip, Jonathan Swinnen, Yutong Hu +2
Joint Embedding Predictive Architectures (JEPAs) have shown promising world modeling capabilities, enabling planning in latent space by optimizing action trajectories using methods…
cs.CV2026
Remembering by Reconstructing: Domain Incremental Learning With Test-Time Training on Video Streams
Jonathan Swinnen, Tinne Tuytelaars
In this work we introduce a novel approach to domain incremental learning, adapting models over time to evolving, non-stationary data. In contrast to other works, we do not attempt…