Search Results for author:
Found 2 papers, 1 papers with code
Deep-AIR: A Hybrid CNN-LSTM Framework for Air Quality Modeling in Metropolitan Cities
Our proposed framework creates 1x1 convolution layers to strengthen the learning of cross-feature spatial interaction between air pollution and important urban dynamic features, particularly road density, building density/height, and street canyon effect.
Facial Action Unit Intensity Estimation via Semantic Correspondence Learning with Dynamic Graph Convolution
The intensity estimation of facial action units (AUs) is challenging due to subtle changes in the person's facial appearance.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
