1 citations · 1 across the 4 of their papers we have counts for
5 papers
Beyond Raw Context Transfer: Representation-based Federated Retrieval-Augmented Generation
Can Peng, Yu Liu, Yingyu Yang +4
Retrieval-augmented generation (RAG) improves the factuality of large language models (LLMs) and vision-language models (VLMs) by grounding generation in external knowledge. Howeve…
Orientation-Robust Latent Motion Trajectory Learning for Annotation-free Cardiac Phase Detection in Fetal Echocardiography
Yingyu Yang, Qianye Yang, Can Peng +4
Fetal echocardiography is essential for detecting congenital heart disease (CHD), facilitating pregnancy management, optimized delivery planning, and timely postnatal interventions…
Neural Collapse-Inspired Multi-Label Federated Learning under Label-Distribution Skew
Can Peng, Yuyuan Liu, Yingyu Yang +3
Federated Learning (FL) enables collaborative model training across distributed clients while preserving data privacy, but remains challenging when client data are highly heterogen…
Latent Motion Profiling for Annotation-free Cardiac Phase Detection in Adult and Fetal Echocardiography Videos
Yingyu Yang, Qianye Yang, Kangning Cui +6
The identification of cardiac phase is an essential step for analysis and diagnosis of cardiac function. Automatic methods, especially data-driven methods for cardiac phase detecti…
Asynchronous Federated Continual Segmentation with Evolving Clients and Label Spaces
Can Peng, Qianhui Men, Pramit Saha +5
Federated learning seeks to foster collaboration among distributed clients while preserving the privacy of their local data. Traditional federated learning methods typically assume…