2 citations · 8 across the 6 of their papers we have counts for
6 papers
MixTEA: Semi-supervised Entity Alignment with Mixture Teaching
Feng Xie, Xin Song, Xiang Zeng +4
Semi-supervised entity alignment (EA) is a practical and challenging task because of the lack of adequate labeled mappings as training data. Most works address this problem by gene…
USD: Unknown Sensitive Detector Empowered by Decoupled Objectness and Segment Anything Model
Yulin He, Wei Chen, Yusong Tan +1
Open World Object Detection (OWOD) is a novel and challenging computer vision task that enables object detection with the ability to detect unknown objects. Existing methods typica…
Improving Knowledge Graph Entity Alignment with Graph Augmentation
Feng Xie, Xiang Zeng, Bin Zhou +1
Entity alignment (EA) which links equivalent entities across different knowledge graphs (KGs) plays a crucial role in knowledge fusion. In recent years, graph neural networks (GNNs…
Pseudo-label Correction and Learning For Semi-Supervised Object Detection
Yulin He, Wei Chen, Ke Liang +3
Pseudo-Labeling has emerged as a simple yet effective technique for semi-supervised object detection (SSOD). However, the inevitable noise problem in pseudo-labels significantly de…
EpiGNN: Exploring Spatial Transmission with Graph Neural Network for Regional Epidemic Forecasting
Feng Xie, Zhong Zhang, Liang Li +2
Epidemic forecasting is the key to effective control of epidemic transmission and helps the world mitigate the crisis that threatens public health. To better understand the transmi…
Inter- and Intra-Series Embeddings Fusion Network for Epidemiological Forecasting
Feng Xie, Zhong Zhang, Xuechen Zhao +2
The accurate forecasting of infectious epidemic diseases is the key to effective control of the epidemic situation in a region. Most existing methods ignore potential dynamic depen…