Xue-song Tang
426/Building 2 Songjiang Campus
Academic History and Education Background:
2019-now Associated Professor, Donghua University, Shanghai, China
2015-2019 Lecturer, Donghua University, Shanghai, China.
2010-2015 Ph.D., Fudan University, Shanghai, China.
2008-2009 M.S., RMIT University, Melbourne, Australia.
2007-2008 M.S. Melbourne University, Melbourne, Australia.
2002-2006 B.E., Wuhan University, Wuhan, China.
Research Interests:
Computer vision; Deep learning; Brain science; Natural language processing; Evolutionary computation.
Teaching:
Semester A (Autumn), C Programming (Undergraduates).
Semester B (Spring), Python Programming (Undergraduates), Artificial Intelligence (Undergraduates).
Research Projects:
1.National Natural Science Foundation of China, Person in Charge, Grant No. 61806051. (01/2019-12/2021)
2.Shanghai Sailing Program, Person in Charge, Grant No. 17YF1426100. (2017.06-2020.05)
3.The Fundamental Research Funds for the Central Universities of China, Person in Charge, Grant No. 2232017D-08. (2017.01-2019.12)
4.China Postdoctoral Science Foundation Special Grants, Person in Charge, Grant No. 2016M601472. (2016.10-2017.10)
[1]X. Tang, H. Wei, and K. Hao, "Using a Vertical-stream Variational Auto-encoder to Generate Segment-based Images and its Biological Plausibility for Modelling the Visual Pathways", IEEE Access, Vol. 7, 2019, pp. 99-110, DOI: 10.1109/ACCESS.2018.2885006, (ISSN: 2169-3536 2018 IF=4.098 JCR Q2) (SCI: 000455293300001)
[2]X. Tang, Y. Ding*, and K. Hao*, "A Novel Method Based on Line-segment Visualizations for Hyper-parameter Optimization in Deep Networks," International Journal of Pattern Recognition and Artificial Intelligence, vol. 32, no. 3, 2018, pp. 1-15. DOI: 10.1142/S0218001418510023. (ISSN: 0218-0014 2018 IF=1.11 JCR Q4) (SCI: 000416037300005)
[3] X. Tang, K. Hao*, H. Wei, and Y. Ding*, “Using Line Segments to Train Multi-stream Stacked Autoencoders for Image Classification,” Pattern Recognition Letters, vol. 94, 2017, pp. 55-61. DOI: 10.1016/j.patrec.2017.05.025 (ISSN: 0167-8655 2018 IF=2.81 JCR Q2) (SCI: 000404696700008)
[4] X. Tang, and H. Wei*, “A Segment-wise Prediction based on Genetic Algorithm for Object Recognition,” Neural Computing and Applications, Vol. 31, no. 7, 2019, pp. 2295-2309. (ISSN 1433-3058 2018 IF=4.664 JCR Q1) (SCI: 000478687000025)
[5] M. Zhao, M. Lin, B. Chiu, Z. Zhang, and X. Tang*, "Trace ratio Criterion based Discriminative Feature Selection via l2, p-norm Regularization for Supervised Learning," Neurocomputing, vol. 321, 10, pp.1–16. DOI: 10.1016/j.neucom.2018.08.040, (ISSN: 0925-2312 2018 IF=4.072 JCR Q1)（SCI:000447385100001）
[6] D. Li, Y. Cao, X. Tang*, S. Yan, and X, Cai, “Leaf segmentation on plant point clouds with facet region growing,” Sensors, vol 18, no. 11, 2018, pp. 3625-3635, (ISSN 1424-8220 2018 IF=3.031 JCRQ2)（SCI: 000451598900035）
[7] X. Tang, H. Wei, D. Li, M. Zhao, and K. Hao*, “Integrating Pixels and Segments: a Deep-learning Method Inspired by the Informational Diversity of the Visual Pathways,” Neurocomputing,DOI: 10.1016/j.neucom.2018.10.096, 2018 (ISSN: 0925-2312 2018 IF=4.072 JCR Q1) (Online) (EI: 20191906900500)
[8] W. Zhou, K. Hao, C. Jiang, L. Chen, X. Tang and X. Cai, “A New Cross Clustering Algorithm for Improving Performance of Supervised Learning”, IEEE Access, vol. 7, 2019, pp. 55713-55723. (ISSN: 2169-3536 2018 IF=4.098 JCR Q2) (SCI: 000468481600001)
[9] T. Han, K. Hao*, Y. Ding, and X. Tang, “A Sparse Autoencoder Compressed Sensing Method for Acquiring the Pressure Array Information of Clothing,” Neurocomputing, vol. 275, pp. 1500-1510, 2018. DOI: 10.1016/j.neucom.2017.09.093 (ISSN: 0925-2312 2018 IF=4.072 JCR Q1) (SCI: 000418370200141)
[10] D. Li, L. Xu*, X. Tang, S. Sun, X. Cai, and P. Zhang, “3D Imaging of Greenhouse Plants with an Inexpensive Binocular Stereo Vision System,” Remote Sensing, vol. 9, no. 5, Article 508, 2017, pp. 1-27. DOI: 10.3390/rs9050508. (ISSN 2072-4292 2018 IF=4.118 JCR Q1) (SCI:000402573700111)
[11] H. Wei*, and X. Tang, “A Genetic-Algorithm-Based Explicit Description of Object Contour and its Ability to Facilitate Recognition,” IEEE Transactions on Cybernetics, vol. 45, no.11, 2015, pp. 2558-2571. DOI: 10.1109/TCYB.2014.2376939 (ISSN: 2168-2267 2018 IF=10.387 JCR Q1) (SCI: 000363233000016)
[12] B. Wei, K. Hao*, X. Tang, and Y. Ding, “A New Method Using CNN with Compressive Sensing for Fabric Defect Classification based on Small Sample Sizes”, Textile Research Journal. DOI: 10.1177/0040517518813656 (ISSN 0040-5175 2018 IF=1.613 JCR Q2) (SCI: 000483665400011)