Zobrazeno 1 - 10
of 155
pro vyhledávání: '"Tan, Yusong"'
Training in unsupervised time series anomaly detection is constantly plagued by the discrimination between harmful `anomaly contaminations' and beneficial `hard normal samples'. These two samples exhibit analogous loss behavior that conventional loss
Externí odkaz:
http://arxiv.org/abs/2410.21322
Occupancy prediction plays a pivotal role in autonomous driving (AD) due to the fine-grained geometric perception and general object recognition capabilities. However, existing methods often incur high computational costs, which contradicts the real-
Externí odkaz:
http://arxiv.org/abs/2407.13155
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 generating pseudo mappings for unlabeled entities. However, they either su
Externí odkaz:
http://arxiv.org/abs/2311.04441
Depth completion is a popular research direction in the field of depth estimation. The fusion of color and depth features is the current critical challenge in this task, mainly due to the asymmetry between the rich scene details in color images and t
Externí odkaz:
http://arxiv.org/abs/2309.16301
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 typically estimate the object likelihood with an additional objectness branc
Externí odkaz:
http://arxiv.org/abs/2306.02275
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) have been successfully applied in many embedding-based EA methods. H
Externí odkaz:
http://arxiv.org/abs/2304.14585
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 degrades the performance of SSOD methods. Recent advances effectively al
Externí odkaz:
http://arxiv.org/abs/2303.02998
Autor:
Xie, Feng, Zhang, Zhong, Zhao, Xuechen, Wang, Haiyang, Zou, Jiaying, Tian, Lei, Zhou, Bin, Tan, Yusong
The ongoing COVID-19 pandemic has caused immeasurable losses for people worldwide. To contain the spread of the virus and further alleviate the crisis, various health policies (e.g., stay-at-home orders) have been issued which spark heated discussion
Externí odkaz:
http://arxiv.org/abs/2209.04631
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 transmission and evolution of epidemics, we propose EpiGNN, a graph neural ne
Externí odkaz:
http://arxiv.org/abs/2208.11517
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 dependencies between regions or the importance of temporal dependencies and
Externí odkaz:
http://arxiv.org/abs/2208.11515