MC-LLaVA: Multi-Concept Personalized Vision-Language Model

Understand

Current vision-language models (VLMs) show exceptional abilities across diverse tasks, such as visual question answering.

• To enhance user experience, recent studies have investigated VLM personalization to understand user-provided concepts.
• However, they mainly focus on single concepts, neglecting the existence and interplay of multiple concepts, which limits real-world applicability.
• This paper proposes MC-LLaVA, a multi-concept personalization paradigm.