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

TL;DR

MC-LLaVA introduces a multi-concept personalization approach for vision-language models, enabling them to understand and respond to multiple user-defined concepts simultaneously, with new training strategies, prompts, and a specialized dataset.

Contribution

It presents a novel multi-concept instruction tuning method, personalized prompts, and a high-quality dataset for improved VLM personalization.

Findings

Achieves impressive multi-concept personalized responses

Reduces training costs with visual token-based prompts

Enhances recognition and grounding with visual prompts

Abstract

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. Specifically, MC-LLaVA employs a multi-concept instruction tuning strategy, effectively integrating multiple concepts in a single training step. To reduce the training costs, we propose a personalized textual prompt that uses visual token information to initialize concept tokens. Additionally, we introduce a personalized visual prompt during inference, aggregating location maps for enhanced recognition and grounding capabilities. To further push the performance upper bound, we incorporate an optional auxiliary loss, better enhancing the proposed personalized prompts. To decorate the VLM personalization research, we contribute a high-quality dataset. We carefully collect images with multiple characters and objects from movies and manually create question-answer samples for multi-concept scenarios, featuring superior diversity. Comprehensive experiments demonstrate that MC-LLaVA achieves impressive multi-concept personalized responses, paving the way for VLMs to become better user assistants. The code and dataset will be released at \href{https://github.com/arctanxarc/MC-LLaVA}{https://github.com/arctanxarc/MC-LLaVA}.