Personalized Subject
Personalized subject research focuses on tailoring technologies and models to individual users, leveraging unique characteristics to improve performance and user experience across diverse applications. Current research emphasizes the use of machine learning, particularly neural networks (including graph neural networks and large language models), and techniques like federated learning and personalized embedding to achieve this customization. This field is significant for advancing areas like healthcare (personalized diagnostics and treatment), education (adaptive learning systems), and entertainment (dynamic difficulty adjustment in games), ultimately leading to more efficient, effective, and user-centric systems.
Papers
Personalized Instance-based Navigation Toward User-Specific Objects in Realistic Environments
Luca Barsellotti, Roberto Bigazzi, Marcella Cornia, Lorenzo Baraldi, Rita Cucchiara
Which Client is Reliable?: A Reliable and Personalized Prompt-based Federated Learning for Medical Image Question Answering
He Zhu, Ren Togo, Takahiro Ogawa, Miki Haseyama
Towards Reliable Verification of Unauthorized Data Usage in Personalized Text-to-Image Diffusion Models
Boheng Li, Yanhao Wei, Yankai Fu, Zhenting Wang, Yiming Li, Jie Zhang, Run Wang, Tianwei Zhang
Mixture of Experts Made Personalized: Federated Prompt Learning for Vision-Language Models
Jun Luo, Chen Chen, Shandong Wu