arXiv:2605. 20247v2 Announce Type: replace-cross Abstract: Catastrophic forgetting remains a major obstacle to continual learning in large language models (LLMs) and vision--language models (VLMs).
By Yang Liu, Toan Nguyen, Flora D. Salim
arXiv:2607. 12048v1 Announce Type: cross Abstract: Deploying medical visual question answering (MedVQA) systems in real-world clinical settings requires models that adapt to new clinical tasks without forgetting previously acquired knowledge.
By Mai A. Shaaban, Tausifa Jan Saleem, Alaa Mohamed, Dilnaz Utemissova, Ufaq Khan, Mohammad Yaqub
arXiv:2605. 20297v2 Announce Type: replace-cross Abstract: Medical image segmentation faces a fundamental challenge in continual learning: data arrives sequentially from heterogeneous sources, yet effective continual learning requires discovering which tasks share sufficient structure to benefit from joint learning.
By Ziyuan Gao
arXiv:2607. 09785v1 Announce Type: cross Abstract: Traditionally, continual learning has assumed access to labeled data, yet many real-world applications -- such as lifelong robotics -- require models to adapt continuously from unlabeled streams.
By Sergi Masip, Alicja Dobrzeniecka, Jonathan Swinnen, Joachim Collin, Bart{\l}omiej Twardowski, Szymon {\L}ukasik, Tinne Tuytelaars
arXiv:2608. 13660v1 Announce Type: cross Abstract: Medical image segmentation models are typically trained under the assumption that all data are available simultaneously.
By Amal Saqib, Tausifa Jan Saleem, Numan Saeed, Mohammad Yaqub
arXiv:2606. 19966v1 Announce Type: cross Abstract: Whole-slide images (WSIs) are widely used for computational cancer prognosis.
By Yucheng Xing, Ling Huang, Pei Liu, Jingying Ma, Jiaqing Xu, Kai He, Mengling Feng