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

• URL: http://arxiv.org/abs/2411.11706v1
• Date: Mon, 18 Nov 2024 16:33:52 GMT
• Title: MC-LLaVA: Multi-Concept Personalized Vision-Language Model
• Authors: Ruichuan An, Sihan Yang, Ming Lu, Kai Zeng, Yulin Luo, Ying Chen, Jiajun Cao, Hao Liang, Qi She, Shanghang Zhang, Wentao Zhang,
• Abstract summary: Current vision-language models (VLMs) show exceptional abilities across diverse tasks including visual question answering. We propose the first multi-concept personalization method named MC-LLaVA along with a high-quality multi-concept personalization dataset. We conduct comprehensive qualitative and quantitative experiments to demonstrate that MC-LLaVA can achieve impressive multi-concept personalized responses.
• Score: 44.325777035345695
• License:
• Abstract: Current vision-language models (VLMs) show exceptional abilities across diverse tasks including visual question answering. To enhance user experience in practical applications, recent studies investigate VLM personalization to understand user-provided concepts. However, existing studies mainly focus on single-concept personalization, neglecting the existence and interplay of multiple concepts, which limits the real-world applicability of personalized VLMs. In this paper, we propose the first multi-concept personalization method named MC-LLaVA along with a high-quality multi-concept personalization dataset. Specifically, MC-LLaVA uses a joint training strategy incorporating multiple concepts in a single training step, allowing VLMs to perform accurately in multi-concept personalization. To reduce the cost of joint training, MC-LLaVA leverages visual token information for concept token initialization, yielding improved concept representation and accelerating joint training. To advance multi-concept personalization research, we further contribute a high-quality dataset. We carefully collect images from various movies that contain multiple characters and manually generate the multi-concept question-answer samples. Our dataset features diverse movie types and question-answer types. We conduct comprehensive qualitative and quantitative experiments to demonstrate that MC-LLaVA can achieve impressive multi-concept personalized responses, paving the way for VLMs to become better user-specific assistants. The code and dataset will be publicly available at https://github.com/arctanxarc/MC-LLaVA.

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