Search Results for author:
Found 3 papers, 0 papers with code
Learn2Talk: 3D Talking Face Learns from 2D Talking Face
Speech-driven facial animation methods usually contain two main classes, 3D and 2D talking face, both of which attract considerable research attention in recent years.
Modeling Spatial Nonstationarity via Deformable Convolutions for Deep Traffic Flow Prediction
Furthermore, to better fit with convolutions, we suggest to first aggregate traffic flows according to pre-conceived regions or self-organized regions based on traffic flows, then dispose to sequentially organized raster images for network input.
Revisiting the Modifiable Areal Unit Problem in Deep Traffic Prediction with Visual Analytics
Deep learning methods are being increasingly used for urban traffic prediction where spatiotemporal traffic data is aggregated into sequentially organized matrices that are then fed into convolution-based residual neural networks.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
