Baocai Yin
Beijing University of Technology
321 Papers
742 Citations
Baocai Yin is an academic researcher from Beijing University of Technology. The author has contributed to research in topics: Computer science & Sparse approximation. The author has an hindex of 20, co-authored 308 publications. Previous affiliations of Baocai Yin include Dalian University of Technology.
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Papers
Learning to Restore Low-Light Images via Decomposition-and-Enhancement
Ke Xu,Xin Yang,Baocai Yin,Rynson W. H. Lau +3 more
- 14 Jun 2020
TL;DR: A frequency-based decompositionand- enhancement model that first learns to recover image objects in the low-frequency layer and then enhances high-frequency details based on the recovered image objects and outperforms state-of-the-art approaches in enhancing practical noisy low-light images.
Deep Learning on Traffic Prediction: Methods, Analysis and Future Directions
TL;DR: A comprehensive survey on deep learning-based approaches in traffic prediction from multiple perspectives is provided, and the state-of-the-art approaches in different traffic prediction applications are listed.
352
Optimized Graph Convolution Recurrent Neural Network for Traffic Prediction
TL;DR: A graph network is introduced and an optimized graph convolution recurrent neural network is proposed for traffic prediction, in which the spatial information of the road network is represented as a graph, which outperforms state-of-the-art traffic prediction methods.
329
A Spatial–Temporal Attention Approach for Traffic Prediction
TL;DR: This paper proposes a novel Attention-based Periodic-Temporal neural Network (APTN), an end-to-end solution for traffic foresting that captures spatial, short-term, and long-term periodical dependencies.
170
Dynamic Graph Convolution Network for Traffic Forecasting Based on Latent Network of Laplace Matrix Estimation
TL;DR: A novel dynamic graph convolution network for traffic forecasting, in which a latent network is introduced to extract spatial-temporal features for constructing the dynamic road network graph matrices adaptively.
161