3 citations · 4 across the 3 of their papers we have counts for
4 papers
RepSNet: A Nucleus Instance Segmentation model based on Boundary Regression and Structural Re-parameterization
Shengchun Xiong, Xiangru Li, Yunpeng Zhong +1
Pathological diagnosis is the gold standard for tumor diagnosis, and nucleus instance segmentation is a key step in digital pathology analysis and pathological diagnosis. However,…
AMPLE: Emotion-Aware Multimodal Fusion Prompt Learning for Fake News Detection
Xiaoman Xu, Xiangrun Li, Taihang Wang +1
Detecting fake news in large datasets is challenging due to its diversity and complexity, with traditional approaches often focusing on textual features while underutilizing semant…
Team QUST at SemEval-2024 Task 8: A Comprehensive Study of Monolingual and Multilingual Approaches for Detecting AI-generated Text
Xiaoman Xu, Xiangrun Li, Taihang Wang +2
This paper presents the participation of team QUST in Task 8 SemEval 2024. We first performed data augmentation and cleaning on the dataset to enhance model training efficiency and…
Multi-Prompt Fine-Tuning of Foundation Models for Enhanced Medical Image Segmentation
Xiangru Li, Yifei Zhang, Liang Zhao
The Segment Anything Model (SAM) is a powerful foundation model that introduced revolutionary advancements in natural image segmentation. However, its performance remains sub-optim…