arXiv:2503.12999v4 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) have demonstrated exceptional performance in various multi-modal tasks. Recently, there has been an increasing interest in improving the personalization capabilities of VLMs. To better integrate user-provided concepts into VLMs, many methods use positive and negative samples to fine-tune these models. However, the scarcity of user-provided positive samples and the low quality of retrieved negative samples pose challenges for existing techniques. To reveal the relationship between sample and model performanc
The continuous drive to improve AI model performance and application specificity is leading to innovations in data generation for complex tasks like personalized VLMs.
Improving personalized Vision-Language Models through better synthetic data directly impacts the scalability and efficacy of AI applications, especially in areas requiring nuanced user understanding.
The ability to generate high-quality synthetic data for personalization can circumvent the traditional bottlenecks of real-world data scarcity and quality, accelerating VLM development and deployment.
- · AI model developers
- · Companies offering personalized AI services
- · Users of personalized AI
- · Traditional data collection services
- · Unoptimized VLM personalization methods
Enhanced personalization capabilities in various AI-powered products, from assistants to enterprise tools.
Increased adoption of VLMs in sectors requiring deep user context and interactive understanding.
The development of entire ecosystems built around synthetic data generation and curation for specialized AI applications, potentially reducing reliance on extensive real-world data acquisition.
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Read at arXiv cs.AI