In this paper, we formulate crowd flow forecasting in irregular regions as a spatio-temporal graph (STG) prediction problem in which each node represents a region with time-varying flows. (Yu, Yin, and Zhu 2017) proposed a trafﬁc forecasting framework that uses GCN to learn spatio-temporal features of trafﬁc data applicable only to undirected graph. (2020) recently presented a Hybrid Spatio-Temporal Graph Convolutional Network (H-STGCN). Wang X, Gupta A. GraphSleepNet: Adaptive Spatial-Temporal Graph Convolutional Networks for Sleep Stage Classification. We propose novel Stacked Spatio-Temporal Graph Convolutional Networks (Stacked-STGCN) for action segmentation, i.e., predicting and localizing a sequence of actions over long videos. Spatial temporal graph convolutional networks for skeleton-based action recognition. Accurate real-time traffic forecasting is a core technological problem against the implementation of the intelligent transportation system. Related work The recognition of sign language is a challenging task with an important role in society to facilitate the communication of deaf persons. 06/18/2020 ∙ by Emanuele Rossi, et al. In Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI). [32] proposed the 2s-AGCN model, which constructs an adaptive graph to give adaptive attention to each joint. In Proceedings of the 2017 SIAM International Conference on Data Mining. STGCN-PyTorch. First Online: 29 September 2020. PyTorch implementation of the spatio-temporal graph convolutional network proposed in Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting by Bing Yu, Haoteng Yin, Zhanxing Zhu. Accordingly, we propose a novel end-to-end deep learning framework named Graph Attention Temporal Convolutional Network (GATCN). Deep learning: A generic approach for extreme condition traffic forecasting. Google Scholar; Junping Zhang, Fei-Yue Wang, Kunfeng Wang, Wei-Hua Lin, Xin Xu, and Cheng Chen. Meanwhile, multiple modules for different time periods … Data-driven intelligent transportation systems: A survey. 01/23/2018 ∙ by Sijie Yan, et al. [31] ﬁrst proposed a spatial and temporal graph convolutional network ST-GCN, which uses spatial graph convolution and temporal convolution for spatial-temporal modeling. The general idea is to take the advantages of the piecewise-liner-flow-density relationship and convert the upcoming traffic volume in its equivalent in travel time. 2018. arXiv preprint arXiv:1811.12013. However, … Temporal Graph Convolutional Networks placed on a patient’s scalp, collected over hours to days. First, we construct an action proposal graph, where each proposal is represented as a node and their relations between two proposals as an edge. 2018) also An example for traffic forecasting is included in this repository. 82. Then we design a novel dynamic graph recurrent convolutional neural network, namely Dynamic-GRCNN, to learn the spatial-temporal features representation for urban transportation network topological structures and transportation hubs. July 2020; DOI: 10.24963/ijcai.2020/184. 20 Jun 2020 • Jiawei Zhu • Yujiao Song • Ling Zhao • Haifeng Li. We propose a novel deep learning framework, STGCN, to tackle time series prediction problem in traffic domain.Instead of applying regular convolutional and recurrent units, we formulate the problem on graphs and build the model with complete convolutional structures. SOTA for Temporal Action Localization on THUMOS’14 (mAP IOU@0.5 metric) [2] LONG SHORT-TERM MEMORY Sepp Hochreiter Fakult at f … Spatial Temporal Graph Convolutional Networks for Skeleton-Based ActionRecognition; Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting; Structural-RNN: Deep Learning on Spatio-Temporal Graphs; Hero image; PinSage; Peer Review Contributions by: Lalithnarayan C. About the author Willies Ogola. To improve the prediction accuracy and achieve a timely performance, the capture of the intrinsically spatio-temporal dependencies and the creation of a parallel model architecture are required. 83. Abstract We present a novel classifier network called STEP, to classify perceived human emotion from gaits, based on a Spatial Temporal Graph Convolutional Network (ST-GCN) architecture. Authors; Authors and affiliations; Dongren Yao; Jing Sui; Erkun Yang; Pew-Thian Yap; Dinggang Shen; Mingxia Liu; Conference paper. In traffic forecasting, graph convolutional networks (GCNs), which model traffic flows as spatio-temporal graphs, have achieved remarkable performance. (Li et al. graph convolutional network architecture for skeleton-based action recognition. For this reason, Dai et al. 2018. The model is able to effectively capture the complex localized spatial-temporal correlations through an elaborately designed spatial-temporal synchronous modeling mechanism. In traffic forecasting, graph convolutional networks (GCNs), which model traffic flows as spatio-temporal graphs, have achieved remarkable performance. 2011. A Graph Neural Network, also known as a Graph Convolutional Networks (GCN), performs a convolution on a graph, instead of on an image composed of pixels. Just like a CNN aims to extract the most important information from the image to classify the image, a GCN passes a filter over the graph, looking for essential vertices and edges that can help classify nodes within the graph.