Dr. Yan Huang received the BSc degree from University of Electronic Science and Technology of China (UESTC) in 2012, and the PhD degree from University of Chinese Academy of Sciences (UCAS) in 2017. Since July 2017, He has joined the National Laboratory of Pattern Recognition (NLPR), Institute of Automation, Chinese Academy of Sciences (CASIA) as an assistant professor. His research interests include deep learning and computer vision. He has published papers in the leading international conferences such as NIPS, ICCV and CVPR.
Deep learning, computer vision
CAS President Scholarship Excellence Award, 2017
Baidu Scholarship, 2016
Doctoral National Scholarship, 2016
Best Poster Award, RACV, 2016
Best Student Paper Award, ICPR, 2014
Best Paper Award, CVPR-Deep Vision Workshop, 2014
1. Yan Huang, Wei Wang, and Liang Wang, Video Super-resolution via Bidirectional Recurrent Convolutional Networks, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2017, Accepted.
2. Yan Huang, Wei Wang, Liang Wang, and Tieniu Tan, Conditional High-order Boltzmann Machines for Supervised Relation Learning, IEEE Transactions on Image Processing (TIP), 26(9), pp. 4297-4310, 2017.
3. Yan Huang, Wei Wang, and Liang Wang, Unconstrained Multimodal Multi-Label Learning, IEEE Transactions on Multimedia (TMM), 17(11), pp. 1923-1935, 2015.
4. Yan Huang, Wei Wang, and Liang Wang, Instance-aware Image and Sentence Matching with Selective Multimodal LSTM. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2310-2318, 2017.
5. Yan Huang, Wei Wang, and Liang Wang, Bidirectional Recurrent Convolutional Networks for Multi-frame Super-resolution. Advances in Neural Information Processing Systems (NIPS), pp. 235-243, 2015.
6. Yan Huang, Wei Wang, and Liang Wang, Conditional High-order Boltzmann Machine: A Supervised Learning Model for Relation Learning, IEEE International Conference on Computer Vision (ICCV), pp. 4265-4273, 2015.
7. Yan Huang, Wei Wang, Liang Wang, and Tieniu Tan, A General Nonlinear Embedding Framework Based on Deep Neural Network, International Conference on Pattern Recognition (ICPR), pp. 732-737, 2014.
8. Yan Huang, Wei Wang, Liang Wang, and Tieniu Tan, Multi-task Deep Neural Network for Multi-label Learning, IEEE International Conference on Image Processing (ICIP), pp. 2897-2900, 2013.
9. Yan Huang, Wei Wang, Liang Wang, and Tieniu Tan, An Effective Regional Saliency Model Based on Extended Site Entropy Rate, International Conference on Pattern Recognition (ICPR), pp. 1407-1410, 2012.