Qingshan Liu  PH. D

 

Professor & Dean, School of Computer Science, Nanjing University of Information Science & Technology

 

Phone: +86-25-58235306 (office)

Email: qsliu@nuist.edu.cn

Research Interests: Image and Video Analysis, Remote Sensing Image Analysis, Machine Learning

 

Education

l  Ph.D, Pattern Recognition and Intelligence System, April, 2003

² National Laboratory of Pattern Recognition, Chinese Academy of Sciences, Beijing, P.R. China

² Supervisor: Prof. Songde Ma

l  M.Sc, Auto-Control, March, 2000

² Department of Automation, South-East University, Nanjing, P.R. China

l  B.Sc, Auto-Control, July, 1997

² Department of Automation, Chinese Textile University (Now Dong-Hua University), Shanghai, P.R. China

 

Working Experiences

l  September, 2011 – Present:  Nanjing University of Information Science & Technology

l  April, 2006 – August, 2011:  Computer Science Department, Rutgers University. Cooperate with Prof Dimitirs N. Metaxas 

l  April, 2003 – November, 2008 National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, P.R. China

l  June, 2004 – May, 2005:  The Department of Information Engineering, The Chinese University of Hong Kong. Cooperate with Prof. Xiaoou Tang

 

Professional Activities

l  2020- Editorial Board Member of Journal of Image and Graphics (Chinese Journa)

l  2017- Editorial Board Member of Acta Automatic Sinica (Chinese Journal)

l  2012- Editorial Board Member of Signal Processing

l  PC Chair of ACPR 2021

l  Area Chair of ICCV 2021、CVPR 2021

l  Panel Chair of ChinaMM 2021、2020 and 2019

l  Tutorial Chair of PRCV 2020

l  Panel Chair of PRCV 2019

l  General Chair of VALSE 2018

l  Organization Chair of ChinaMM 2017

l  Program Chair of CCCV 2017

 

Publications (Papers:200+, Google Scholar Citation: 11500+. H-Index: 50)

See details in https://scholar.google.com/citations?hl=zh-CN&user=2Pyf20IAAAAJ

Selected papers from 2016:

[1] Hang R, Liu Q, Li Z. Spectral Super-Resolution Network Guided by Intrinsic Properties of Hyperspectral Imagery. To appear in IEEE Trans. on Image Processing (T-IP), 2021.

[2] Fan J, Liu B, Zhang K, Liu Q. Semi-supervised Video Object Segmentation via Learning Object-aware Global-local Correspondence. To appear in IEEE Trans. on Circuits and Systems for Video Technology (T-CSVT), 2021.

[3] Zhao Z, Liu Q, Wang S. Learning Deep Global Multi-Scale and Local Attention Features for Facial Expression Recognition in the Wild. IEEE Trans on Image Processing (T-IP), 30: 6544-6556, 2021.

[4] Shuai H, Xu X, Liu Q. Backward Attentive Fusing Network With Local Aggregation Classifier for 3D Point Cloud Semantic Segmentation. IEEE Trans. on Image Processing (T-IP), 30: 4973-4984, 2021.

[5] Zhang R, Liu Q, Hang R, et al. Predicting Tropical Cyclogenesis Using a Deep Learning Method From Gridded Satellite and ERA5 Reanalysis Data in the Western North Pacific Basin. To appear in IEEE Trans. on Geoscience and Remote Sensing (T-GRS), 2021.

[6] Ma F, Xia G, Liu Q. Spatial Consistency Constrained GAN for Human Motion Transfer. To appear in IEEE Trans. on Circuits and Systems for Video Technology (T-CSVT), 2021.

[7] Song H, Xu W, Liu D, Liu Q, Metaxas D. Multi-Stage Feature Fusion Network for Video Super-Resolution. IEEE Trans. on Image Processing (T-IP), 30: 2923-2934, 2021.

[8] Li T, Zhang K, Shen S, Liu B, Liu Q, Li Z. Image Co-saliency Detection and Instance Co-segmentation using Attention Graph Clustering based Graph Convolutional Network. To appear in IEEE Trans. on Multimedia (T-MM), 2021.

[9] Chen J, Song H, Zhang K, Liu Q. Video saliency prediction using enhanced spatiotemporal alignment network. Pattern Recognition, 109: 107615, 2021.

[10] Zhao Z, Liu Q, Zhou F. Robust lightweight facial expression recognition network with label distribution training, AAAI Conf. on Artificial Intelligence (AAAI), 2021.

[11] Zhang K, Dong M, Liu B, Yuan X, Liu Q. DeepACG: Co-Saliency Detection via Semantic-Aware Contrast Gromov-Wasserstein Distance. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2021.

[12] Zhao Z, Liu Q. Former-DFER: Dynamic Facial Expression Recognition Transformer, ACM Conf. on Multimedia (ACM MM), 2021.

[13] Zhang K, Zhao Z, Liu D, Liu Q, Liu B. Deep Transport Network for Unsupervised Video Object Segmentation. IEEE Conf. on Computer Vision (ICCV), 2021.

[14] Sun Y, Chen J, Liu Q, et al. Dual-path attention network for compressed sensing image reconstruction. IEEE Trans. on Image Processing (T-IP), 29: 9482-9495, 2020.

[15] Li T, Song H, Zhang K, Liu Q. Learning residual refinement network with semantic context representation for real-time saliency object detection. Pattern Recognition, 105: 107372, 2020.

[17] Wang P, He X, Chen Q, Cheng A, Liu Q, Cheng J. Unsupervised network quantization via fixed-point factorization. IEEE Trans. on Neural Networks and Learning Systems (T-NNLS), 32(6): 2706-2720, 2020.

[18] Hang R, Li Z, Liu Q, et al. Hyperspectral image classification with attention-aided CNNs. IEEE Trans. on Geoscience and Remote Sensing (T-GRS), 59(3): 2281-2293, 2020.

[19] Zhou F, Hang R, Liu Q. Class-guided feature decoupling network for airborne image segmentation. IEEE Trans. on Geoscience and Remote Sensing (T-GRS), 59(3): 2245-2255, 2020.

[20] Wang S, Shuai H, Liu Q. Phase space reconstruction driven spatio-temporal feature learning for dynamic facial expression recognition. IEEE Trans. on Affective Computing (T-AC), 2020.

[21] Hang R, Zhou F, Liu Q, et al. Classification of hyperspectral images via multitask generative adversarial networks. IEEE Trans. on Geoscience and Remote Sensing (T-GRS), 59(2): 1424-1436, 2020.

[22] Li J, Pan Z, Liu Q, et al. Stacked U-shape network with channel-wise attention for salient object detection. IEEE Trans. on Multimedia (T-MM), 23: 1397-1409, 2020.

[23] Li J, Pan Z, Liu Q, et al. Complementarity-aware attention network for salient object detection. IEEE Trans. on Cybernetics (T-CYB), 2020.

[24] Xia G, Chen B, Sun H, Liu Q. Nonconvex low-rank kernel sparse subspace learning for keyframe extraction and motion segmentation. IEEE Trans. on Neural Networks and Learning Systems (T-NNLS), 32(4): 1612-1626, 2020.

[25] Fan J, Song H, Zhang K, Yang K, Liu Q. Feature alignment and aggregation siamese networks for fast visual tracking. IEEE Trans. on Circuits and Systems for Video Technology (T-CSVT), 31(4): 1296-1307, 2020.

[26] Sun Y, Yang Y, Liu Q, et al. Learning non-locally regularized compressed sensing network with half-quadratic splitting. IEEE Trans. on Multimedia (T-MM), 22(12): 3236-3248, 2020.

[27] Hang R, Li Z, Ghamisi P, Hong D, Xia G, Liu Q. Classification of hyperspectral and LiDAR data using coupled CNNs. IEEE Trans. on Geoscience and Remote Sensing (T-GRS), 58(7): 4939-4950, 2020.

[28] Sun Y, Chen J, Liu Q, Liu G. Learning image compressed sensing with sub-pixel convolutional generative adversarial network. Pattern Recognition, 98: 107051, 2020.

[29] Yuan X, Liu B, Wang L, Liu Q, Metaxas N. Dual Iterative Hard Thresholding. Journal of Machine Learning Research (JMLR), 21: 152:1-152:50, 2020.

[30] Zhang K, Wang L, Liu D, Liu Q, Li Z. Dual Temporal Memory Network for Efficient Video Object Segmentation. ACM Conf. on Multimedia (ACM MM), 2020.

[31] Tian H, Liu B, Yuan X T, Liu Q. Meta-learning with network pruning. European Conf. on Computer Vision (ECCV), 2020.

[32] Chen J, Sun Y, Liu Q, et al. Learning memory augmented cascading network for compressed sensing of images. European Conf. on Computer Vision (ECCV), 2020.

[33] Zhang K, Chen J, Liu B, Liu Q. Deep object co-segmentation via spatial-semantic network modulation. AAAI Conf. on Artificial Intelligence (AAAI), 2020.

[34] Zhang K, Li T, Shen S, Liu B, Chen J, Liu Q. Adaptive graph convolutional network with attention graph clustering for co-saliency detection. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2020.

[35] Zhang R, Liu Q, Hang R. Tropical cyclone intensity estimation using two-branch convolutional neural network from infrared and water vapor images. IEEE Trans. on Geoscience and Remote Sensing (T-GRS), 58(1): 586-597, 2019.

[36] Liu G, Liu Q, Yuan X T, et al. Matrix completion with deterministic sampling: Theories and methods[J]. IEEE Trans. on Pattern Analysis and Machine Intelligence (T-PAMI), 2019.

[37] Liu G, Zhang Z, Liu Q, et al. Robust subspace clustering with compressed data. IEEE Trans. on Image Processing (T-IP), 28(10): 5161-5170, 2019.

[38] Hang R, Liu Q, Hong D, et al. Cascaded recurrent neural networks for hyperspectral image classification. IEEE Trans. on Geoscience and Remote Sensing (T-GRS), 57(8): 5384-5394, 2019.

[39] Zhang K, Li T, Liu B, Liu Q. Co-saliency detection via mask-guided fully convolutional networks with multi-scale label smoothing. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2019.

[40] Zhang K, Fan J, Liu Q, et al. Parallel attentive correlation tracking. IEEE Trans. on Image Processing (T-IP), 28(1): 479-491, 2018.

[41] Zhang K, Liu Q, Yang J, et al. Visual tracking via Boolean map representations. Pattern Recognition, 81: 147-160, 2018.

[42] Li C, Wang X, Dong W, Yan J, Liu Q, Zha H. Joint active learning with feature selection via cur matrix decomposition. IEEE Trans. on Pattern Analysis and Machine Intelligence (T-PAMI), 41(6): 1382-1396, 2018.

[43] Xia G, Sun H, Chen B, Liu Q, Hang R. Nonlinear low-rank matrix completion for human motion recovery. IEEE Trans. on Image Processing (T-IP), 27(6): 3011-3024, 2018.

[44] Li C, Wei F, Dong W, Wang X, Liu Q, Zhang X. Dynamic structure embedded online multiple-output regression for streaming data. IEEE Trans. on Pattern Analysis and Machine Intelligence (T-PAMI), 41(2): 323-336, 2018.

[45] Liu G, Liu Q, Yuan X. A new theory for matrix completion. Int’l Conf. on Neural Information Processing Systems (NIPS), 2017.

[46] Liu B, Yuan X T, Wang L, Liu Q, Metaxas N. Dual iterative hard thresholding:  From non-convex sparse minimization to non-smooth concave maximization. Int’l Conf. on Machine Learning (ICML), 2017.

[47] Li C, Yan J, Wei F, Dong W, Liu Q, Zha H. Self-paced multi-task learning. AAAI Conf. on Artificial Intelligence (AAAI), 2017.

[48] Liu Q, Hang R, Song H, et al. Learning multiscale deep features for high-resolution satellite image scene classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2017, 56(1): 117-126.

[49] Liu Q, Yang J, Deng J, et al. Robust facial landmark tracking via cascade regression. Pattern Recognition, 66: 53-62, 2017.

[50] Liu Q, Fan J, Song H, et al. Visual tracking via nonlocal similarity learning. IEEE Trans. on Circuits and Systems for Video Technology (T-CSVT), 28(10): 2826-2835, 2017.

[51] Li C, Wei F, Yan J, Dong W, Liu Q, Zhang X, Zha H. A self-paced regularization framework for multilabel learning. IEEE Trans. on Neural Networks and Learning Systems (T-NNLS), 29(6): 2660-2666, 2017.

[52] Liu Q, Liu G, Li L, et al. Reversed spectral hashing. IEEE Trans. on Neural Networks and Learning Systems (T-NNLS), 29(6): 2441-2449, 2017.

[53] Yuan X T, Liu Q. Newton-Type Greedy Selection Methods for $\ell _0 $-Constrained Minimization. IEEE Trans. on Pattern Analysis and Machine Intelligence (T-PAMI), 39(12): 2437-2450, 2017.

[54] Yuan X, Li P, Zhang T, Liu Q, Liu G. Learning additive exponential family graphical models via ℓ2, 1-norm regularized M-estimation. Int’l Conf. on Neural Information Processing Systems (NIPS), 2016.

[55] Liu B, Yuan X, Yu Y, Liu Q, Metaxas N. Decentralized robust subspace clustering. AAAI Conf. on Artificial Intelligence (AAAI), 2016.

[56] Liu B, Yuan X, Zhang S, Liu Q, Metaxas N. Efficient k-Support-Norm Regularized Minimization via Fully Corrective Frank-Wolfe Method. IJCAI, 2016.

[57] Liu Q, Deng J, Yang J, et al. Adaptive cascade regression model for robust face alignment. IEEE Trans. on Image Processing (T-IP), 26(2): 797-807, 2016.

[58] Liu Q, Sun Y, Wang C, et al. Elastic net hypergraph learning for image clustering and semi-supervised classification. IEEE Trans. on Image Processing (T-IP), 26(1): 452-463, 2016.

[59] Zhang K, Liu Q, Wu Y, et al. Robust visual tracking via convolutional networks without training. IEEE Trans. on Image Processing (T-IP), 25(4): 1779-1792, 2016.

[60] Liu G, Liu Q, Li P. Blessing of dimensionality: Recovering mixture data via dictionary pursuit. IEEE Trans. on Pattern Analysis and Machine Intelligence (T-PAMI), 39(1): 47-60, 2016.