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Found 5 papers, 3 papers with code
A Deep Learning Framework for Traffic Data Imputation Considering Spatiotemporal Dependencies
Spatiotemporal (ST) data collected by sensors can be represented as multi-variate time series, which is a sequence of data points listed in an order of time.
Real-time Human-Centric Segmentation for Complex Video Scenes
To alleviate this problem, we propose a mechanism named Inner Center Sampling to improve the accuracy of instance segmentation.
PoseDet: Fast Multi-Person Pose Estimation Using Pose Embedding
This simple framework achieves an unprecedented speed and a competitive accuracy on the COCO benchmark compared with state-of-the-art methods.
Spatial‐temporal attention wavenet: A deep learning framework for traffic prediction considering spatial‐temporal dependencies
Traffic prediction on road networks is highly challenging due to the complexity of traffic systems and is a crucial task in successful intelligent traffic system applications.
Ranked #7 on
Traffic Prediction
on PEMS-BAY
Learning Large-scale Location Embedding From Human Mobility Trajectories with Graphs
An increasing amount of location-based service (LBS) data is being accumulated and helps to study urban dynamics and human mobility.
