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deerta0103's Projects

gan icon gan

运用GAN来进行交通流预测和数据修复

geotorchai icon geotorchai

GeoTorchAI: A Spatiotemporal Deep Learning Framework (https://dl.acm.org/doi/abs/10.1145/3557915.3561036)

hgcn icon hgcn

The code of AAAI2021 paper of HGCN for Traffic Forecasting

hstgnn icon hstgnn

A pytorch version implement of DEXA 2021 conference paper "Traffic Prediciton Through the Fusion of Spatial Temporal Data and Point of interest."

hstgnn-1 icon hstgnn-1

This repository is a pytorch version implementation of DEXA 2021 conference paper "Traffic Flow Prediciton through the Fusion of Spatial Temporal Data and Points of Interest".

kstdiff-urban-flow-generation icon kstdiff-urban-flow-generation

Official implementation of "Towards Generative Modeling of Urban Flow through Knowledge-enhanced Denoising Diffusion"(SIGSPATIAL'23)

lmst3dresnet icon lmst3dresnet

Multiple local 3D CNNs for region-based prediction in smart cities

localegn icon localegn

few-shot models for short-term traffic prediction

maed icon maed

[ICCV 2021] Encoder-decoder with Multi-level Attention for 3D Human Shape and Pose Estimation

mdstn icon mdstn

Multidirectional Short-Term Traffic Volume Prediction Based on Spatiotemporal Networks, Applied Intelligence, to appear.

mf-stn icon mf-stn

The codes and data of paper "Matrix Factorization for Spatio-Temporal Neural Networks with Applications to Urban Flow Prediction"

mg-tar icon mg-tar

[IEEE T-ITS] MG-TAR: Multi-view Graph Convolutional Networks for Traffic Accident Risk Prediction

mim icon mim

Code release for "Memory In Memory: A Predictive Neural Network for Learning Higher-Order Non-Stationarity from Spatiotemporal Dynamics" (CVPR 2019)

moganet icon moganet

[arXiv 2022] MogaNet: Efficient Multi-order Gated Aggregation Network

pdformer icon pdformer

[AAAI2023] A PyTorch implementation of PDFormer: Propagation Delay-aware Dynamic Long-range Transformer for Traffic Flow Prediction.

poseformer icon poseformer

The project is an official implementation of our paper "3D Human Pose Estimation with Spatial and Temporal Transformers".

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