个人简介

Han-Jia Ye is an Associate Researcher in the School of Artificial Intelligence at the Nanjing University (NJU). His major research focuses on machine learning and its applications to data mining and computer vision. Han-Jia received his B.Sc. degree from Nanjing University of Posts and Telecommunications, China in June 2013. After that, he became an M.Sc. student in the LAMDA Group led by professor Zhi-Hua Zhou in Nanjing University. From Sept. 2015, Han-Jia started his Ph.D. degree in machine learning under the supervision of Prof. Yuan Jiang and Prof. De-Chuan Zhan. During 2017-2018, he visited Prof. Fei Sha's group in University of Southern California, LA. He received his PhD degree at May 2019. Latest News 09/2021: 2 papers on meta-learning and fine-grained classification have been accepted by NeurIPS 2021. 08/2021: 1 paper on imbalanced deep learning accepted by ICCV 2021. 07/2021: 1 paper on incremental learning accepted by ACM MM 2021. 07/2021: 1 arXiv paper on meta-learning. 06/2021: 1 arXiv paper on model-agnostic meta-learning. 06/2021: 1 arXiv paper on contextualizing meta-learning. 03/2021: 1 (oral) paper accepted by CVPR 2021 on openset recognition. 12/2020: 3 papers accepted by AAAI 2021. 11/2020: 1 arXiv paper on unspervised meta-learning. 09/2020: 1 paper accepted by IJCV on generalized few-shot learning. 04/2020: 1 paper accepted by TPAMI on heterogeneous few-shot model reuse. 03/2020: 2 papers (1 oral and 1 poster) accepted by CVPR 2020. 02/2020: 1 arXiv paper on a novel perspective of meta-learning. 01/2020: 1 arXiv paper on imbalanced deep learning. 10/2019: 1 paper accepted by TKDE on multiple instance learning w/ novel class. 09/2019: Invited talk at a CCF-Big Data workshop (Wuhan, China) on "Multi-Metric Learning for Heterogeneous Data". 09/2019: One manuscript with Xiang-Rong Sheng and De-Chuan Zhan is accepted by Machine Learning. 07/2019: Joining the Nanjing University (School of Artificial Intelligence) as an Assistant Researcher. 05/2019: Successfully defending thesis on "Metric Learning for Open Environment".