Online-PVLM: Advancing Personalized VLMs with Online Concept Learning

• URL: http://arxiv.org/abs/2511.20056v1
• Date: Tue, 25 Nov 2025 08:25:30 GMT
• Title: Online-PVLM: Advancing Personalized VLMs with Online Concept Learning
• Authors: Huiyu Bai, Runze Wang, Zhuoyun Du, Yiyang Zhao, Fengji Zhang, Haoyu Chen, Xiaoyong Zhu, Bo Zheng, Xuejiao Zhao,
• Abstract summary: Online-PVLM is a framework for online concept learning by leveraging hyperbolic representations.<n>We develop OP-Eval, a benchmark comprising 1,292 concepts and over 30K high-quality instances with diverse question types.
• Score: 19.46716778297505
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Personalized Visual Language Models (VLMs) are gaining increasing attention for their formidable ability in user-specific concepts aligned interactions (e.g., identifying a user's bike). Existing methods typically require the learning of separate embeddings for each new concept, which fails to support real-time adaptation during testing. This limitation becomes particularly pronounced in large-scale scenarios, where efficient retrieval of concept embeddings is not achievable. To alleviate this gap, we propose Online-PVLM, a framework for online concept learning by leveraging hyperbolic representations. Our approach makes a train-free paradigm for concept embeddings generation at test time, making the use of personalized VLMs both scalable and efficient. In addition, we develop OP-Eval, a comprehensive and large-scale benchmark comprising 1,292 concepts and over 30K high-quality instances with diverse question types, designed to rigorously assess online concept learning in realistic scenarios. Extensive experiments demonstrate the state-of-the-art performance of our proposed framework. Our source code and dataset will be made available.

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