The streamlined, replay-free approach proves highly effective on a new continual learning benchmark and a practical path toward building prehensive, continually-learning medical VLMs and advancing the development of medical AI.
It is widely assumed that continued pretraining (CPT) on a narrow, out-of-distribution corpus such as raw biological sequence must trade away a general-purpose model’s broad competence — the “alignment tax” or catastrophic-forgetting intuition. We test this directly, without any new training, by re-analyzing three chec...
A systematic evaluation of CL for MedVQA across diverse clinical objectives, including classification, multi-label classification, detection, cell counting, and report generation suggests that existing CL methods struggle to maintain stability-plasticity balance when tasks with different objectives and supervision form...
Mai A. Shaaban, Tausifa Jan Saleem, Alaa Mohamed et al.· arXiv.org· 0 citations
The results suggest that the combination of sparse representations, local learning, and persistent memory is a promising direction for continual learning, while motivating further investigation into the respective roles of learning rules, representations, and architectural design in mitigating catastrophic forgetting.
TCLA corrects inference logits based on a small set of support samples, boosting pretrained VLMs performance by improving inter-class deconfusion and reducing domain shift, and consistently improves OOD performance of Medical VLMs and outperforms existing training-based adaptation methods.
We introduce CMP (Cognitive Memory Primitive), an architecture that represents inputs as sparse relational codes, stores them in a two-tier competitive memory, and learns entirely through local, gradient-free updates, with no backpropagation anywhere in the network. We use this architecture to test a specific hypothesi...
Experiments show that FiUni can effectively infer latent batch-level task affiliations and achieve competitive performance against advanced task-aware CL methods with fewer trainable parameters.
Dezheng Han, Anlan Zhang, Zhiwu Zhu et al.· 0 citations
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