The encapsulated 3D convolution is developed to fully exploit the short-term spatiotemporal information, and the channel correlation is modeled by self-attention mechanism to further improve representations in the long-term interaction, the effectiveness of which is validated in the ablation study. ...
SAF-Net论文代码复现:台风强度变化预测《A spatio-temporal deep learning method for typhoon inten》827 0 2023-11-15 23:18:30 未经作者授权,禁止转载 您当前的浏览器不支持 HTML5 播放器 请更换浏览器再试试哦~5 投币 37 6 程序定制、二次开发、论文辅导、详情+V whbwqq123 或者 wqqpython 人工...
To the best knowledge of the authors, this paper is one of the first attempts to employ spatio-temporal DL approaches in short-term passenger demand forecasting under the on-demand ride service platform. The main contributions of this paper are within three folds: The rest of the paper is or...
GeoTorchAI is a spatiotemporal deep learning framework on top of PyTorch and Apache Sedona. It enable spatiotemporal machine learning practitioners to easily and efficiently implement deep learning models targeting the applications of raster imagery datasets and spatiotemporal non-imagery datasets. Deep ...
Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting 巴拉巴拉 积极向上的懒人 1.创新点 第一篇将图卷积用于提取空间和时间信息的文章,没有使用正则卷积和递归单元,使用了完整的卷积结构,在更少的参数下,可以得到更快的训练速度 2.问题描述 通过前[t-m+1,t] 的交通流...
Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting 论文笔记---GCN在交通领域的应用(二) 一、论文翻译: 1、摘要: 及时准确地交通预测对于城市交通控制和指导具有至关重要的意义。由于交通流量的非线... 查看...
Spatial: graph, Temporal: Conv Spatio-temporal convolutional networks 2 Preliminary 2.1 Traffic Prediction on Road Graphs Traffic forecast: v^t+1,…,v^t+H=argmaxvt+1,…,vt+HlogP(vt+1,…,vt+H∣vt−M+1,…,vt) Gt=(Vt,E,W) ...
SpatiotemporalDeep learningVideo understandingComputer visionSurveyVideo understanding requires abundant semantic information. Substantial progress has been made on deep learning models in the image, text, and audio domains, and notable efforts have been recently dedicated to the design of deep networks in...
spatio-temporal fields measured on a set of irregular points in space is still under-investigated. To fill this gap, we introduce here a framework for spatio-temporal prediction of climate and environmental data using deep learning. Specifically, we show how spatio-temporal processes can be ...
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