Personalizing MLLMs via Reinforced Multimodal Reference Game
Personalizing Multimodal Large Language Models (MLLMs) aims to recognize users' unique concepts from visual data and provide personalized responses. Although prior work has shown the benefit of concept descriptions and reasoning for this task, MLLM descriptions often include information, such as state and context, that does not help and may in fact hinder the unique identification of the target concept among other visually similar items. Effective descriptions of personal concepts should instead be accurate, discriminative, and free of distracting details. To achieve such descriptions, we introduce Reinforced Reference Game (RRG), a learning framework that promotes discriminative descript...
| Authors | Deepayan Das, Davide Talon, Yiming Wang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 27 June 2026 |
| Last updated at source | 27 June 2026 |
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