Dr Lingqiao Liu

 Position Senior Lecturer
 Org Unit School of Computer Science
 Email lingqiao.liu@adelaide.edu.au
 Telephone +61 8 8313 2133
 Location Floor/Room 5 48b ,  Ingkarni Wardli ,   North Terrace
  • Biography/ Background


    Dr. Lingqiao Liu is a Lecturer and ARC DECRA Fellow in School of Computer Science, University of Adelaide, Australia. He obtained his P.h.D. from the Australian National University in 2014. He is a recipient of ARC DECRA (Discovery Early Career Researcher Award) award in 2016 and the University of Adelaide Research Fellowship award in 2016.  He has a broad research interest in machine learning, computer vision, natural language processing and music information processing.
  • Awards & Achievements

    ARC DECRA (Discovery Early Career Researcher Award) award  2016

    The University of Adelaide Research Fellowship award  2016

  • Publications

     2019

    •         Lei Zhang, Peng Wang, Chunhua Shen, Lingqiao Liu, Wei Wei, Yanning Zhang, Anton van den Hengel. Adaptive importance learning for improving lightweight image super-resolution network. International Journal of Computer Vision (IJCV), 2019.  [J17]
    •         Yinjie Lei, Ziqin Zhou, Pingping Zhang, Yulan Guo, ZijunMa, Lingqiao Liu. Deep point-to-subspace metric learning for sketch-based 3D shape retrieval. Pattern Recognition,  2019. [J16]
    •         Shengqin Jiang, Xiaobo Lu, Lingqiao Liu. Mask-aware networks for crowd counting.  IEEE Transactions on Circuits and Systems for Video Technology. 2019. [J15]
    •         Dong Gong, Lingqiao Liu, Vuong Le, Budhaditya Saha, Moussa Reda Mansour, Svetha Venkatesh, Anton van den Hengel. Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection. IEEE International Conference on Computer Vision (ICCV)  2019. [C33]
    •         Xiu-Shen Wei, Peng Wang, Lingqiao Liu, Chunhua Shen, Jianxin Wu. Piecewise classifier mappings:Learning fine-grained learners for novel categories with few examples. IEEE Transactions on Image Processing, Accepted in May 2019. [J14]
    •         Ehsan Abbasnejad, Javen Shi, Anton van den Hengel, Lingqiao Liu. A Generative Adversarial Density Estimator. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR ), 2019.   [C32]
    •         Bohan Zhuang, Chunhua Shen, Mingkui Tan, Lingqiao Liu, Ian Reid. Structured Binary Neural Networks for Accurate Image Classification and Semantic Segmentation, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019.   [C31]
    •         Peng Wang, Lingqiao Liu, Chunhua Shen, Heng Tao Shen. Order-Aware Convolutional Pooling for Video Based Action Recognition, Pattern Recognition (PR) 2019.   [J14]
    •         Yu Chen, Chunhua Shen, Hao Chen, Xiu-Shen Wei, Lingqiao Liu, Jian Yang. Adversarial Learning of Structure-Aware Fully Convolutional Networks for Landmark Localization, IEEE Transcation Pattern Analysis and Machine Intelligence. Accepted in 2019 .   [J13]


             2018

    •         Jie Yang, Dong Gong, Lingqiao Liu, Qinfeng Shi. Seeing Deeply and Bidirectionally: A Deep Learning Approach for Single Image Reflection Removal. In European Conference on Computer Vision (ECCV), 2018.   [C30]
    •         Zetao Chen, Lingqiao Liu, Inkyu Sa, Zongyuan Ge, Margarita Chli, Learning Context Flexible Attention Model for Long-term Visual Place Recognition. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2018 .         [C29]
    •         Bohan Zhuang, Chunhua Shen, Mingkui Tan, Lingqiao Liu, Ian Reid, Towards effective low-bitwidth convolutional neural networks. Computer Vision and Pattern Recognition (CVPR) 2018 .          [C28]



    2017

    •         Bohan Zhuang*, Lingqiao Liu*, Chunhua Shen, Ian Reid. Towards Context-aware Interaction Recognition for Visual Relationship Detection. IEEE International Conference on Computer Vision (ICCV)  2017.  (* Indicates equal contribution.)          [C27]
    •         Tong Shen, Guosheng Lin, Lingqiao Liu, Chunhua Shen, Ian Reid. Weakly Supervised Semantic Segmentation Based on Co-segmentation . British Machine Vision Conference (BMVC)  2017.         [C26]
    •         Ruizhi Qiao, Lingqiao Liu, Chunhua Shen, Anton van den Hengel. Visually Aligned Word Embeddings for Improving Zero-shot Learning. (ORAL) British Machine Vision Conference (BMVC)  2017.         [C25]
    •         Yao Li, Guosheng Lin, Bohan Zhuang, Lingqiao Liu, Chunhua Shen, Anton van den Hengel. Sequential Person Recognition in Photo Albums with a Recurrent Network. Computer Vision and Pattern Recognition (CVPR) 2017.          [C24]
    •         Damien Teney, Lingqiao Liu, Anton van den Hengel. Graph-Structured Representations for Visual Question Answering. Computer Vision and Pattern Recognition (CVPR) 2017.          [C23]
    •         Bohan Zhuang*, Lingqiao Liu*, Chunhua Shen, Ian Reid. Attend in groups: a weakly-supervised deep learning framework for learning from web data. Computer Vision and Pattern Recognition (CVPR) 2017.  (* Indicates equal contribution.)       [C22]
    •         Peng Wang*, Lingqiao Liu*, Chunhua Shen, Zi Huang, Anton van den Hengel, Heng Tao Shen, Multi-attention Network for One Shot Learning. Computer Vision and Pattern Recognition (CVPR) 2017.  (* Indicates equal contribution.)         [C21]
    •         Dong Gong, Jie Yang, Lingqiao Liu, Yanning Zhang, Ian Reid, Chunhua Shen, Anton van den Hengel, Qinfeng Shi. From Motion Blur to Motion Flow: a Deep Learning Solution for Removing Heterogeneous Motion Blur.  Computer Vision and Pattern Recognition (CVPR) 2017.         [C20]
    •         Zetao Chen, Adam Jacobson, Niko Sünderhauf, Ben Upcroft, Lingqiao Liu, Chunhua Shen, Ian Reid, Michael Milford, Deep Learning Features at Scale for Visual Place Recognition.  International Conference on Robotics and Automation (ICRA). 2017        [C19]


    2016

    •     Ruizhi Qiao, Lingqiao Liu, Chunhua Shen, Anton van den Hengel. Learning discriminative trajectorylet detector sets for accurate skeleton-based action recognition. Pattern Recognition. Accepted in 2016        [J12]
    •     ZongYuan Ge, Chris McCool, Conrad Sanderson, Peng Wang, Lingqiao Liu, Ian D. Reid, Peter I. Corke, Exploiting Temporal Information for DCNN-Based Fine-Grained Object Classification.  DICTA 2016.         [C18]
    •     Lingqiao Liu*, Peng Wang*, Chunhua Shen, Lei Wang, Anton van den Hengel, Chao Wang, Hengtao Shen. Compositional Model based Fisher Vector Coding for Image Classification. IEEE Transcation Pattern Analysis and Machine Intelligence. Accepted in 2016  (* Indicates equal contribution.)          [J11]
    •     Lingqiao Liu, Chunhua Shen, Anton van den Hengel. Cross-convolutional-layer Pooling for Image Recognitions. IEEE Transcation Pattern Analysis and Machine Intelligence. Accepted in 2016       [J10]
    •     Yao Li*, Lingqiao Liu*, Chunhua Shen, Anton van den Hengel. Mining Mid-level Visual Patterns with Deep CNN Activations. International Journal on Computer Vision (IJCV), 2016 (* Indicates equal contribution.)        [J9]
    •     Yao Li*, Lingqiao Liu*, Chunhua Shen, Anton van den Hengel. Image Co-localization by Mimicking a Good Detector’s Confidence Score Distribution. European Conference on Computer Vision (ECCV), 2016. (* Indicates equal contribution.)         [c17]
    •     Peng Wang*, Lingqiao Liu*, Chunhua Shen, Zi Huang, Anton van den Hengel, Heng Tao Shen.What’s Wrong with that Object? Identifying Images of Unusual Objects by Modelling the Detection Score Distribution. Computer Vision and Pattern Recognition (CVPR) 2016. (* Indicates equal contribution.)          [c16]
    •     Ruizhi Qiao*, Lingqiao Liu*, Chunhua Shen, Anton van den Hengel. Less is More: Zero-shot Learning from Online Textual Documents with Noise Suppression Mechanism. Computer Vision and Pattern Recognition (CVPR) 2016. (* Indicates equal contribution.)       [c15]
    •     Qi Wu, Chunhua Shen, Anton van den Hengel, Lingqiao Liu, Anthony Dick. What value high level concepts in vision to language problems? Computer Vision and Pattern Recognition (CVPR) 2016.        [c14]
    •     Peng Wang, Yuanzhouhan Cao, Chunhua Shen, Lingqiao Liu, HengTao Shen. Temporal Pyramid Pooling Based Convolutional Neural Network for Action Recognition. IEEE Transactions on Circuits and Systems for Video Technology. Accepted in 2016     [J8]



    2015

    •     Lei Wang, Lingqiao Liu, and Luping Zhou, A Graph-embedding Approach to Hierarchical Visual Word Mergence, IEEE Transactions on Neural Networks and Learning Systems, Accepted in Dec 2015.      [J7]
    •      Luping Zhou, Lei Wang, Lingqiao Liu, Philip Ogunbona, and Dinggang Shen, Learning Discriminative Bayesian Networks from High-dimensional Continuous Neuroimaging Data, IEEE Transactions on Pattern Analysis and Machine Intelligence, Accepted in Nov 2015.      [J6]
    •          Lingqiao Liu,  Lei Wang,  Chunhua Shen,  A generalized probabilistic framework for compact codebook creation, IEEE Transcation Pattern Analysis and Machine Intelligence. Accepted in April 2015      [J5]
    •          Chao Wang, Lei Wang, Lingqiao Liu, Density Maximization for Improving Graph Matching with Its Applications, IEEE Transactions on Image Processing, Accepted in March 2015.      [J4]
    •         Lingqiao Liu, Chunhua Shen, Anton van den Hengel. The Treasure beneath Convolutional Layers: Cross-convolutional-layer Pooling for Image Classification. Computer Vision and Pattern Recognition (CVPR) 2015.      [c13]
    •         Yao Li*, Lingqiao Liu*, Chunhua Shen, Anton van den Hengel. Mid-level Deep Pattern Mining, Computer Vision and Pattern Recognition (CVPR) 2015. (* Indicates equal contribution.)     [c12]


    2014

    •     Lei Wang, Lingqiao Liu, Luping Zhou, KL Chan. Application of SVMs to the Bag-of-Features Model: A Kernel Perspective, In Support Vector Machines  Applications, pp 155-189. Published by Springer in January 2014.     [B1]
    •          Lingqiao Liu, Lei Wang. HEp-2 cell image classification with multiple linear descriptors, Pattern Recognition, Vol 47(7): 2400-2408, 2014      [J3]
    •          Lei Wang, Luping Zhou, Chunhua Shen, Lingqiao Liu, Huan Liu. A Hierarchical Word-Merging Algorithm with Class Separability Measure, IEEE Transcation Pattern Analysis and Machine Intelligence. Vol.36(3): 417-435, 2014         [J2]
    •         Lingqiao Liu, Chunhua Shen, Lei Wang, Anton van den Hengel, Chao Wang. Encoding high-dimensional local features by sparse coding based fisher vectors,  Advances in Neural Information Processing Systems (NIPS) 2014, pp 1143-1151, June 2014.       [c11]
    •         Chao Wang, Lei Wang, Lingqiao Liu, Progressive Mode-Seeking on Graphs for Sparse Feature Matching,  European Conference on Computer Vision (ECCV), pp 788-802, 2014      [c10]
    •         Luping Zhou, Lei Wang, Lingqiao Liu, Philip Ogunbona, Dinggang Shen. Max-Margin Based Learning for Discriminative Bayesian Network from Neuroimaging Data,  International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), pp: 321-328, 2014          [c9]


    2013

    •         Luping Zhou, Lei Wang, Lingqiao Liu, Philip Ogunbona, Dinggang Shen. Discriminative Brain Effective Connectivity Analysis for Alzheimer’s Disease: A Kernel Learning Approach upon Sparse Gaussian Bayesian Network, Computer Vision and Pattern Recognition (CVPR) 2013, pp 2243-2250.        [c8]
    •         Jianjia Zhang, Lei Wang, Lingqiao Liu, Luping Zhou, Wanqing Li. Accelerating the Divisive Information-Theoretic Clustering of Visual Words, The International Conference on Digital Image Computing: Techniques and Applications (DICTA), 2013, pp 1-8.     [c7]
    •         Lingqiao Liu, Lei Wang. A Scalable Unsupervised Feature Merging Approach to Efficient Dimensionality Reduction of High Dimensional Visual Data, IEEE International Conference on Computer Vision (ICCV), 2013. pp 3008-3015      [c6]
    •         Chao Wang, Lei Wang, Lingqiao Liu. Improving Graph Matching via Density Maximization, 2013 IEEE International Conference on Computer Vision (ICCV), 2013. pp 3424-3431   [c5]


    2012

    •      Xinwang Liu, Lei Wang, Jianping Yin, Lingqiao Liu. Incorporation of radius-info can be simple with SimpleMKL,  Neurocomputing, Vol 89: 30-38, 2012    [J1]
    •     Lingqiao Liu, Lei Wang. What has my classifier learned? Visualizing the classification rules of bag-of-feature model by support region detection,  Computer Vision and Pattern Recognition (CVPR) 2012, pp 3586-3593      [c4]



    2011

    •         Lingqiao Liu, Lei Wang, Chunhua Shen. A generalized probabilistic framework for compact codebook creation, Computer Vision and Pattern Recognition (CVPR) 2011, pp 1537-1544.    [c3]
    •         Lingqiao Liu, Lei Wang, Xinwang Liu. In defense of soft-assignment coding, 2011 IEEE International Conference on Computer Vision (ICCV), pp 2486-2493.     [c2]
    •         Lingqiao Liu, Lei Wang. Exploring latent class information for image retrieval using the bag-of-feature model, ACM Multimedia 2011, pp 1405-1408.     [c1]



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Entry last updated: Saturday, 26 Oct 2019

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