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arXiv · 2509.16011

Towards Robust Visual Continual Learning with Multi-Prototype Supervision

Xiwei Liu·Yulong Li·Yichen Li·Xinlin Zhuang·Haolin Yang·Huifa Li·Imran Razzak
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Abstract

Language-guided supervision, which utilizes a frozen semantic target from a Pretrained Language Model (PLM), has emerged as a promising paradigm for visual Continual Learning (CL). However, relying on a single target introduces two critical limitations: 1) semantic ambiguity, where a polysemous category name results in conflicting visual representations, and 2) intra-class visual diversity, where a single prototype fails to capture the rich variety of visual appearances within a class. To this end, we propose MuproCL, a novel framework that replaces the single target with multiple, context-aware prototypes. Specifically, we employ a lightweight LLM agent to perform category disambiguation and visual-modal expansion to generate a robust set of semantic prototypes. A LogSumExp aggregation mechanism allows the vision model to adaptively align with the most relevant prototype for a given image. Extensive experiments across various CL baselines demonstrate that MuproCL consistently enhances performance and robustness, establishing a more effective path for language-guided continual learning.

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Xiwei Liu, Yulong Li, Yichen Li, Xinlin Zhuang, Haolin Yang, Huifa Li, Imran Razzak. 2025-09-19. Towards Robust Visual Continual Learning with Multi-Prototype Supervision. https://arxiv.org/abs/2509.16011

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