5 papers
Curing Semantic Drift: A Dynamic Approach to Grounding Generation in Large Vision-Language Models
Jiahe Chen, Jiaying He, Qiyuan Chen +6
Large Vision-Language Models (LVLMs) face a tug-of-war between powerful linguistic priors and visual evidence, often leading to \emph{semantic drift}: a progressive detachment from…
From Misleading Queries to Accurate Answers: A Three-Stage Fine-Tuning Method for LLMs
Guocong Li, Weize Liu, Yihang Wu +4
Large language models (LLMs) exhibit excellent performance in natural language processing (NLP), but remain highly sensitive to the quality of input queries, especially when these…
MM-DADM: Multimodal Drug-Aware Diffusion Model for Virtual Clinical Trials
Qian Shao, Bang Du, Zepeng Li +6
High failure rates in cardiac drug development necessitate virtual clinical trials via electrocardiogram (ECG) generation to reduce risks and costs. However, existing ECG generatio…
Towards Clinical Practice in CT-Based Pulmonary Disease Screening: An Efficient and Reliable Framework
Qian Shao, Bang Du, Yixuan Wu +6
Deep learning models for pulmonary disease screening from Computed Tomography (CT) scans promise to alleviate the immense workload on radiologists. Still, their high computational…
Enhancing Semi-Supervised Learning via Representative and Diverse Sample Selection
Qian Shao, Jiangrui Kang, Qiyuan Chen +5
Semi-Supervised Learning (SSL) has become a preferred paradigm in many deep learning tasks, which reduces the need for human labor. Previous studies primarily focus on effectively…