通过视觉语言校准提升分析性类增量学习

使用预训练模型（PTMs）的类增量学习（Class-Incremental Learning, CIL）面临着在高效适应性和长期稳定性之间的关键权衡。虽然分析性学习能够实现快速、递归的闭式更新，但其有效性常被积累的误差和特征不兼容所削弱。本文首先进行系统性研究，剖析了基于PTM的分析性CIL的失效模式，识别出特征刚性作为主要瓶颈。ederated this insight, we propose VILA, a novel dual-branch framework that advances analytic CIL via a two-level vision-language calibration strategy. Specifically, we coherently fuse plastic, task-adapted features with a frozen, universal visual anchor at the feature level through geometric calibration, and leverage cross-modal semantic priors at the decision level to rectify prediction bias. This confluence maintains analytic-learning's extreme efficiency while overcoming its inherent brittleness. Extensive experiments across eight benchmarks demonstrate that VILA consistently yields superior performance, particularly in fine-grained and long-sequence scenarios. Our framework harmonizes high-fidelity prediction with the simplicity of analytic learning. Our code is available at https://github.com/byzhaoAI/VILA.