3 citations · 8 across the 9 of their papers we have counts for
9 papers
Next Point-of-interest (POI) Recommendation Model Based on Multi-modal Spatio-temporal Context Feature Embedding
Lingyu Zhang, Pengfei Xu, Rui Ban +4
Predicting the next pickup location of individual users is a fundamental problem in intelligent mobility systems, which requires modeling personalized travel behaviors under comple…
A Knowledge-Guided Cross-Modal Feature Fusion Model for Local Traffic Demand Prediction
Lingyu Zhang, Pengfei Xu, Guobin Wu +4
Traffic demand prediction plays a critical role in intelligent transportation systems. Existing traffic prediction models primarily rely on temporal traffic data, with limited effo…
Unveiling the Inflexibility of Adaptive Embedding in Traffic Forecasting
Hongjun Wang, Jiyuan Chen, Lingyu Zhang +2
Spatiotemporal Graph Neural Networks (ST-GNNs) and Transformers have shown significant promise in traffic forecasting by effectively modeling temporal and spatial correlations. How…
STGformer: Efficient Spatiotemporal Graph Transformer for Traffic Forecasting
Hongjun Wang, Jiyuan Chen, Tong Pan +4
Traffic forecasting is a cornerstone of smart city management, enabling efficient resource allocation and transportation planning. Deep learning, with its ability to capture comple…
Evaluating the Generalization Ability of Spatiotemporal Model in Urban Scenario
Hongjun Wang, Jiyuan Chen, Tong Pan +4
Spatiotemporal neural networks have shown great promise in urban scenarios by effectively capturing temporal and spatial correlations. However, urban environments are constantly ev…
Robust Traffic Forecasting against Spatial Shift over Years
Hongjun Wang, Jiyuan Chen, Tong Pan +4
Recent advancements in Spatiotemporal Graph Neural Networks (ST-GNNs) and Transformers have demonstrated promising potential for traffic forecasting by effectively capturing both t…
Adversarially Robust Video Perception by Seeing Motion
Lingyu Zhang, Chengzhi Mao, Junfeng Yang +1
Despite their excellent performance, state-of-the-art computer vision models often fail when they encounter adversarial examples. Video perception models tend to be more fragile un…
Robust Perception through Equivariance
Chengzhi Mao, Lingyu Zhang, Abhishek Joshi +3
Deep networks for computer vision are not reliable when they encounter adversarial examples. In this paper, we introduce a framework that uses the dense intrinsic constraints in na…
Easy Begun is Half Done: Spatial-Temporal Graph Modeling with ST-Curriculum Dropout
Hongjun Wang, Jiyuan Chen, Tong Pan +7
Spatial-temporal (ST) graph modeling, such as traffic speed forecasting and taxi demand prediction, is an important task in deep learning area. However, for the nodes in graph, the…