activity
20192021
most citedSpectral Temporal Graph Neural Network for Multivariate Time-series Forecasting

116 citations · 261 across the 13 of their papers we have counts for

collaborators

21 papers

cs.CV202120 cited

Global Aggregation then Local Distribution for Scene Parsing

Xiangtai Li, Li Zhang, Guangliang Cheng +4

Modelling long-range contextual relationships is critical for pixel-wise prediction tasks such as semantic segmentation. However, convolutional neural networks (CNNs) are inherentl…

cs.CV2021

Dynamic Dual Sampling Module for Fine-Grained Semantic Segmentation

Chen Shi, Xiangtai Li, Yanran Wu +2

Representation of semantic context and local details is the essential issue for building modern semantic segmentation models. However, the interrelationship between semantic contex…

cs.CV20211 cited

Fast and Accurate Scene Parsing via Bi-direction Alignment Networks

Yanran Wu, Xiangtai Li, Chen Shi +5

In this paper, we propose an effective method for fast and accurate scene parsing called Bidirectional Alignment Network (BiAlignNet). Previously, one representative work BiSeNet~\…

cs.LG2021116 cited

Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting

Defu Cao, Yujing Wang, Juanyong Duan +8

Multivariate time-series forecasting plays a crucial role in many real-world applications. It is a challenging problem as one needs to consider both intra-series temporal correlati…

cs.CV20218 cited

PointFlow: Flowing Semantics Through Points for Aerial Image Segmentation

Xiangtai Li, Hao He, Xia Li +6

Aerial Image Segmentation is a particular semantic segmentation problem and has several challenging characteristics that general semantic segmentation does not have. There are two…

cs.CL20214 cited

Syntax-BERT: Improving Pre-trained Transformers with Syntax Trees

Jiangang Bai, Yujing Wang, Yiren Chen +4

Pre-trained language models like BERT achieve superior performances in various NLP tasks without explicit consideration of syntactic information. Meanwhile, syntactic information h…