FACULTY & RESEARCH

​Bing Su
Tenure-track Associate Professor
​Bing Su received the BS degree in information engineering from the Beijing Institute of Technology, Beijing, China, in 2010, and the PhD degree in electronic engineering from Tsinghua University, Beijing, China, in 2016. From 2016 to 2020, he worked with the Institute of Software, Chinese Academy of Sciences, Beijing. His research interests include pattern recognition, computer vision, and machine learning.

Personal Homepage： https://gsai.ruc.edu.cn/addons/teacher/index/info.html?user_id=0&ruccode=20200026&ln=en

Detailed Information

Education

- PhD: Dept. of Electronic Engineering, Tsinghua University, August 2010 to January 2016

-Visiting student: Dept. of Electrical Engineering and Computer Science, Northwestern University, November 2014 to June 2015

-Bachelor: School of Information and Electronics, Beijing Institute of Technology, September 2006 to July 2010

Professional Experience

-Tenure-track Associate Professor: Gaoling School of Artificial Intelligence, Renmin University of China, July 2020 to present

-Associate Professor: Institute of Software, Chinese Academy of Sciences, October 2018 to June 2020

- Assistant Professor: Institute of Software, Chinese Academy of Sciences, April 2016 to September 2018

Research Interests

-Sequence distance learning: temporal alignment, metric learning for sequence data, dimensionality reduction for sequence data, optimal transport, temporal structure analysis

-Machine learning under restricted conditions: self-supervised learning, few-shot learning, zero-shot learning, transfer learning, federated learning, causal reasoning

-Computer vision: video classification, action recognition, visual and semantic correlation analysis, sequence prediction

Publications

（一）Journal

1、ing Su and Ying Wu. “Learning Low-Dimensional Temporal Representations with Latent Alignments”, IEEE Trans. on Pattern Analysis and Machine Intelligence (TPAMI), 2019, accepted (CCF A)

2、Bing Su and Gang Hua. “Order-preserving Optimal Transport for Distances between Sequences”, IEEE Trans. on Pattern Analysis and Machine Intelligence (TPAMI), 2019, 41(12), pp. 2961-2974. (CCF A)

3、Bing Su, Xiaoqing Ding, Changsong Liu, and Ying Wu, "Heteroscedastic Max–Min Distance Analysis for Dimensionality Reduction", IEEE Trans. on Image Processing (TIP), 2018, 27(8), pp. 4052-4065. (CCF A)

4、Bing Su, Xiaoqing Ding, Hao Wang, and Ying Wu, "Discriminative Dimensionality Reduction for Multi-Dimensional Sequences", IEEE Trans. on Pattern Analysis and Machine Intelligence (TPAMI), 2018, 40(1), pp. 77-91. (CCF A)

5、Bing Su, Jiahuan Zhou, Xiaoqing Ding, and Ying Wu, "Unsupervised Hierarchical Dynamic Parsing and Encoding for Action Recognition ", IEEE Trans. on Image Processing (TIP), 2017, 26(12), pp. 5784-5799. (CCF A)

6、Bing Su, Xiaoqing Ding, Changsong Liu, Hao Wang, and Ying Wu, "Discriminative Transformation for Multi-dimensional Temporal Sequences", IEEE Trans. on Image Processing (TIP), 2017, 26(7), pp. 3579-3593. (CCF A)

（二）Conference

1、Jiahuan Zhou, Bing Su, and Ying Wu, "Online Joint Multi-Metric Adaptation from Frequent Sharing-Subset Mining for Person Re-Identification", IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), 2020. (CCF A)

2、Bing Su, Jiahuan Zhou, and Ying Wu, "Order-Preserving Wasserstein Discriminant Analysis", IEEE/CVF International Conference on Computer Vision (ICCV), 2019, pp. 9885-9894.(CCF A)

3、Bing Su and Ying Wu. “Learning Distance for Sequences by Learning a Ground Metric”, International Conference on Machine Learning (ICML), 2019, pp. 6015-6025.(CCF A)

4、Bing Su and Ying Wu. “Learning Low-Dimensional Temporal Representations”, International Conference on Machine Learning (ICML), 2018, pp. 4761-4770.(CCF A)

5、Jiahuan Zhou, Bing Su, and Ying Wu, " Easy Identification from Better Constraints: Multi-Shot Person Re-Identification from Reference Constraints", IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), 2018, pp. 5373-5381. (CCF A)

6、Chenqin Cai, Pin Lv, and Bing Su, " Feature Fusion Network for Scene Text Detection", IEEE International Conference on Image Processing (ICIP), 2018.

7、Cheng Cheng, Pin Lv, and Bing Su, " Spatiotemporal Pyramid Pooling in 3D Convolutional Neural Networks for Action Recognition", IEEE International Conference on Image Processing (ICIP), 2018.

8、Bing Su and Gang Hua. “Order-preserving Wasserstein Distance for Sequence Matching”, IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 1049-1057.(CCF A)

9、Bing Su, Jiahuan Zhou, Xiaoqing Ding, Hao Wang, and Ying Wu, "Hierarchical Dynamic Parsing and Encoding for Action Recognition", Proc. European Conf. on Computer Vision (ECCV), 2016, pp. 202-217.(CCF B)

10、Bing Su, Xiaoqing Ding, Changsong Liu, and Ying Wu. “Heteroscedastic Max-Min Distance Analysis”, IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), 2015, pp. 4539-4547.(CCF A)

11、Bing Su and Xiaoqing Ding. “Linear Sequence Discriminant Analysis: A Model-Based Dimensionality Reduction Method for Vector Sequences”, IEEE International Conference on Computer Vision (ICCV), 2013, pp. 889–896.(CCF A)

12、Bing Su, Xiaoqing Ding, Liangrui Peng, and Changsong Liu. “Cross-language Sensitive Words Distribution Map: A Novel Recognition-based Document Understanding Method for Uighur and Tibetan”, International Conference on Document Analysis and Recognition (ICDAR), 2013, pp. 255-259.

13、Bing Su, Xiaoqing Ding, Liangrui Peng, and Changsong Liu. “A Novel Baseline-independent Feature Set for Arabic Handwriting Recognition”, International Conference on Document Analysis and Recognition (ICDAR), 2013, pp. 1282-1286.

14、Bing Su, Liangrui Peng, and Xiaoqing Ding. SemiBoost-based Arabic character recognition method. Proc. SPIE 7874, Document Recognition and Retrieval XVIII (SPIE DRR), 2011.

Services

Reviewer for IEEE TPAMI, IEEE TIP, ICML 2020, NeurIPS 2020, ICME 2020


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