1 citations · 1 across the 6 of their papers we have counts for
7 papers
Spectral Consistent Flow for One-step 3D Medical Image Translation
Haoqing Li, Jun Shi, Mingchao Li +4
We present Spectral Consistent Flow (SC-Flow), a 3D medical image translation framework with a single function evaluation (1-NFE) in the latent space. This approach reformulates me…
Automated Safety Benchmarking: A Multi-agent Pipeline for LVLMs
Xiangyang Zhu, Yuan Tian, Zicheng Zhang +6
Large vision-language models (LVLMs) exhibit remarkable capabilities in cross-modal tasks but face significant safety challenges, which undermine their reliability in real-world ap…
Large Language Model Aided Birt-Hogg-Dube Syndrome Diagnosis with Multimodal Retrieval-Augmented Generation
Haoqing Li, Jun Shi, Xianmeng Chen +5
Deep learning methods face dual challenges of limited clinical samples and low inter-class differentiation among Diffuse Cystic Lung Diseases (DCLDs) in advancing Birt-Hogg-Dube sy…
Info-Coevolution: An Efficient Framework for Data Model Coevolution
Ziheng Qin, Hailun Xu, Wei Chee Yew +6
Machine learning relies heavily on data, yet the continuous growth of real-world data poses challenges for efficient dataset construction and training. A fundamental yet unsolved q…
Boosting LLM via Learning from Data Iteratively and Selectively
Qi Jia, Siyu Ren, Ziheng Qin +3
Datasets nowadays are generally constructed from multiple sources and using different synthetic techniques, making data de-noising and de-duplication crucial before being used for…
Weaver: Foundation Models for Creative Writing
Tiannan Wang, Jiamin Chen, Qingrui Jia +43
This work introduces Weaver, our first family of large language models (LLMs) dedicated to content creation. Weaver is pre-trained on a carefully selected corpus that focuses on im…
Learning from Training Dynamics: Identifying Mislabeled Data Beyond Manually Designed Features
Qingrui Jia, Xuhong Li, Lei Yu +5
While mislabeled or ambiguously-labeled samples in the training set could negatively affect the performance of deep models, diagnosing the dataset and identifying mislabeled sample…