Adapter Merging with Centroid Prototype Mapping for Scalable Class-Incremental Learning

We propose Adapter Merging with Centroid Prototype Mapping (ACMap), an exemplar-free framework for classincremental learning (CIL) that addresses both catastrophic forgetting and scalability. While existing methods involve a trade-off between inference time and accuracy, ACMap consolidates task-specific adapters into a single adapter, thus achieving constant inference time across tasks without sacrificing accuracy. The framework employs adapter merging to build a shared subspace that aligns task representations and mitigates forgetting, while centroid prototype mapping maintains high accuracy by consistently adapting representations within the shared subspace. To further improve scalability, an early stopping strategy limits adapter merging as tasks increase. Extensive experiments on five benchmark datasets demonstrate that ACMap matches state-ofthe-art accuracy while maintaining inference time comparable to the fastest existing methods. The code is available at https://github.com/tf63/ACMap . To address the dual challenges of catastrophic forgetting and scalability in CIL, we propose Adapter Merging with Centroid Prototype Mapping (ACMap), a framework that consolidates task-specific adapters into a single adapter. ACMap enables scalability by maintaining con-This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.