Search Results for author: Kun Xie

Found 6 papers, 4 papers with code

MCL: Multi-view Enhanced Contrastive Learning for Chest X-ray Report Generation

1 code implementation15 Nov 2024 Kang Liu, Zhuoqi Ma, Kun Xie, Zhicheng Jiao, Qiguang Miao

Radiology reports are crucial for planning treatment strategies and enhancing doctor-patient communication, yet manually writing these reports is burdensome for radiologists.

Contrastive Learning

Drone Data Analytics for Measuring Traffic Metrics at Intersections in High-Density Areas

1 code implementation4 Nov 2024 Qingwen Pu, Yuan Zhu, Junqing Wang, Hong Yang, Kun Xie, Shunlai Cui

This study employed over 100 hours of high-altitude drone video data from eight intersections in Hohhot to generate a unique and extensive dataset encompassing high-density urban road intersections in China.

KPG: Key Propagation Graph Generator for Rumor Detection based on Reinforcement Learning

no code implementations21 May 2024 Yusong Zhang, Kun Xie, Xingyi Zhang, Xiangyu Dong, Sibo Wang

In this paper, we propose Key Propagation Graph Generator (KPG), a novel reinforcement learning-based rumor detection framework that generates contextually coherent and informative propagation patterns for events with insufficient topology information, while also identifies indicative substructures for events with redundant and noisy propagation structures.

Data Augmentation Graph Neural Network

Factual Serialization Enhancement: A Key Innovation for Chest X-ray Report Generation

1 code implementation15 May 2024 Kang Liu, Zhuoqi Ma, Mengmeng Liu, Zhicheng Jiao, Xiaolu Kang, Qiguang Miao, Kun Xie

In Stage 1, we introduce factuality-guided contrastive learning for visual representation by maximizing the semantic correspondence between radiographs and corresponding factual descriptions.

Contrastive Learning cross-modal alignment +6

Learning Based Proximity Matrix Factorization for Node Embedding

1 code implementation10 Jun 2021 Xingyi Zhang, Kun Xie, Sibo Wang, Zengfeng Huang

Recent progress on node embedding shows that proximity matrix factorization methods gain superb performance and scale to large graphs with millions of nodes.

Link Prediction Node Classification

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