Dr Lingqiao Liu
Senior Lecturer
School of Computer Science
Faculty of Sciences, Engineering and Technology
Eligible to supervise Masters and PhD, but is currently at capacity - email supervisor to discuss availability.
Dr. Lingqiao Liu is a Senior 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.
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Journals
Year Citation 2021 Lei, Y., Liu, Y., Zhang, P., & Liu, L. (2021). Towards using count-level weak supervision for crowd counting. Pattern Recognition, 109, 1-13.
Scopus6 WoS42021 Su, H., Wang, P., Liu, L., Li, H., Li, Z., & Zhang, Y. (2021). Where to Look and How to Describe: Fashion Image Retrieval with an Attentional Heterogeneous Bilinear Network. IEEE Transactions on Circuits and Systems for Video Technology, 31(8), 3254-3265.
Scopus2 WoS22021 Peng, D., Lei, Y., Liu, L., Zhang, P., & Liu, J. (2021). Global and Local Texture Randomization for Synthetic-to-Real Semantic Segmentation. IEEE Transactions on Image Processing, 30, 6594-6608.
Scopus22021 Zhou, Y., Song, X., Zhang, Y., Liu, F., Zhu, C., & Liu, L. (2021). Feature Encoding With Autoencoders for Weakly Supervised Anomaly Detection. IEEE Transactions on Neural Networks and Learning Systems, 12 pages.
Scopus62021 Kang, L., Liu, J., Liu, L., Zhou, Z., & Ye, D. (2021). Semi-supervised emotion recognition in textual conversation via a context-augmented auxiliary training task. Information Processing and Management, 58(6), 1-13.
2021 Shu, Y., Li, Q., Liu, L., & Xu, G. (2021). Semi-supervised Adversarial Learning for Attribute-Aware Photo Aesthetic Assessment. IEEE Transactions on Multimedia, 1-11.
2021 Lu, W., Gong, D., Fu, K., Sun, X., Diao, W., & Liu, L. (2021). Boundarymix: Generating pseudo-training images for improving segmentation with scribble annotations. Pattern Recognition, 117, 107924.
Scopus3 WoS22021 Zhang, J., Liu, L., Wang, P., & Zhang, J. (2021). Exploring the auxiliary learning for long-tailed visual recognition. Neurocomputing, 449, 303-314.
Scopus1 WoS12021 Zhuang, B., Tan, M., Liu, J., Liu, L., Reid, I., & Shen, C. (2021). Effective Training of Convolutional Neural Networks with Low-bitwidth Weights and Activations. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1.
Scopus32020 Zhuang, B., Liu, L., Tan, M., Shen, C., & Reid, I. (2020). Training quantized neural networks with a full-precision auxiliary module. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1485-1494.
Scopus19 WoS112020 Abbasnejad, M. E., Shi, Q., van den Hengel, A., & Liu, L. (2020). GADE: A Generative Adversarial Approach to Density Estimation and its Applications. International Journal of Computer Vision, 128(10-11), 2731-2743.
2020 Zhang, L., Wang, P., Liu, L., Shen, C., Wei, W., Zhang, Y., & van den Hengel, A. (2020). Towards Effective Deep Embedding for Zero-Shot Learning. IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, 30(9), 2843-2852.
Scopus10 WoS92020 Jiang, S., Lu, X., Lei, Y., & Liu, L. (2020). Mask-Aware Networks for Crowd Counting. IEEE Transactions on Circuits and Systems for Video Technology, 30(9), 3119-3129.
Scopus92020 Chen, Y., Shen, C., Chen, H., Wei, X., Liu, L., & Yang, J. (2020). Adversarial learning of structure-aware fully convolutional networks for landmark localization. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42(7), 1654-1669.
Scopus12 WoS7 Europe PMC12020 Zhang, L., Wang, P., Shen, C., Liu, L., Wei, W., Zhang, Y., & van den Hengel, A. (2020). Adaptive importance learning for improving lightweight image super-resolution network. International Journal of Computer Vision, 128(2), 479-499.
Scopus16 WoS162020 Gou, Y., Lei, Y., Liu, L., Zhang, P., & Peng, X. (2020). A Dynamic Parameter Enhanced Network for distant supervised relation extraction. Knowledge-Based Systems, 197, 1-12.
Scopus42019 Lei, Y., Zhou, Z., Zhang, P., Guo, Y., Ma, Z., & Liu, L. (2019). Deep point-to-subspace metric learning for sketch-based 3D shape retrieval. Pattern Recognition, 96, 106981-1-106981-13.
Scopus15 WoS162019 Wei, X., Wang, P., Liu, L., Shen, C., & Wu, J. (2019). Piecewise Classifier Mappings: Learning Fine-Grained Learners for Novel Categories with Few Examples. IEEE Transactions on Image Processing, 28(12), 6116-6125.
Scopus43 WoS272019 Wang, P., Liu, L., Shen, C., & Shen, H. (2019). Order-aware convolutional pooling for video based action recognition. Pattern Recognition, 91, 357-365.
Scopus13 WoS132019 Teney, D., Wang, P., Cao, J., Liu, L., Shen, C., & Hengel, A. V. D. (2019). V-PROM: A Benchmark for Visual Reasoning Using Visual Progressive Matrices.. CoRR, abs/1907.12271, 12071-12078.
WoS12018 Chen, Z., Liu, L., Sa, I., Ge, Z., & Chli, M. (2018). Learning Context Flexible Attention Model for Long-Term Visual Place Recognition. IEEE Robotics and Automation Letters, 3(4), 4015-4022.
Scopus442017 Li, Y., Liu, L., Shen, C., & Hengel, A. (2017). Mining Mid-level Visual Patterns with Deep CNN Activations. International Journal of Computer Vision, 121(3), 344-364.
Scopus25 WoS202017 Wang, L., Liu, L., & Zhou, L. (2017). A graph-embedding approach to hierarchical visual word mergence. IEEE Transactions on Neural Networks and Learning Systems, 28(2), 308-320.
Scopus3 WoS42017 Qiao, R., Liu, L., Shen, C., & van den Hengel, A. (2017). Learning discriminative trajectorylet detector sets for accurate skeleton-based action recognition. Pattern Recognition, 66, 202-212.
Scopus41 WoS352017 Liu, L., Wang, P., Shen, C., Wang, L., Van Den Hengel, A., Wang, C., & Shen, H. (2017). Compositional model based Fisher vector coding for image classification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(12), 2335-2348.
Scopus40 WoS27 Europe PMC32016 Liu, L., Shen, C., & van den Hengel, A. (2016). Cross-convolutional-layer pooling for image recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(11), 2305-2313.
Scopus52 WoS43 Europe PMC62016 Zhou, L., Wang, L., Liu, L., Ogunbona, P., & Shen, D. (2016). Learning discriminative Bayesian networks from high-dimensional continuous neuroimaging data. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(11), 2269-2283.
Scopus202016 Liu, L., Wang, L., & Shen, C. (2016). A generalized probabilistic framework for compact codebook creation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(2), 224-237.
Scopus3 WoS22016 Wang, P., Cao, Y., Shen, C., Liu, L., & Shen, H. (2016). Temporal pyramid pooling based convolutional neural network for action recognition. IEEE Transactions on Circuits and Systems for Video Technology, 27(99), 1-8.
Scopus73 WoS732015 Wang, C., Wang, L., & Liu, L. (2015). Density maximization for improving graph matching with its applications. IEEE Transactions on Image Processing, 24(7), 2110-2123.
Scopus10 WoS8 Europe PMC12014 Wang, L., Zhou, L., Shen, C., Liu, L., & Liu, H. (2014). A hierarchical word-merging algorithm with class separability measure. IEEE Transactions on Pattern Analysis and Machine Intelligence, 36(3), 417-435.
Scopus13 WoS9 Europe PMC12013 Liu, X., Yin, J., Wang, L., Liu, L., Liu, J., Hou, C., & Zhang, J. (2013). An adaptive approach to learning optimal neighborhood kernels. IEEE Transactions on Cybernetics, 43(1), 371-384.
Scopus222012 Liu, X., Wang, L., Yin, J., & Liu, L. (2012). Incorporation of radius-info can be simple with SimpleMKL. Neurocomputing, 89, 30-38.
Scopus20Qiao, R., Liu, L., Shen, C., & Hengel, A. V. D. (n.d.). Visually Aligned Word Embeddings for Improving Zero-shot Learning. Zhuang, B., Shen, C., Tan, M., Chen, P., Liu, L., & Reid, I. (n.d.). Structured Binary Neural Networks for Image Recognition. -
Book Chapters
Year Citation 2014 Zhou, L., Wang, L., Liu, L., Ogunbona, P., & Shen, D. (2014). Support vector machines for neuroimage analysis: Interpretation from discrimination. In Support Vector Machines Applications (Vol. 9783319023007, pp. 191-220). Springer International Publishing.
Scopus82014 Wang, L., Liu, L., Zhou, L., & Chan, K. L. (2014). Application of SVMs to the bag-of-features model: A kernel perspective. In Support Vector Machines Applications (Vol. 9783319023007, pp. 155-189). Springer International Publishing.
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Conference Papers
DE170101259 (ARC Discovery Early Career Researcher Award 17): Zero-shot
and few-shot learning with deep knowledge transfer
DP160103710 (ARC Discovery Project 16) : Whole image understanding by
convolutions on graphs
Introduction to Statistic Machine Learning, Puzzle based Learning, Big Data Analysis and Project, Artificial Intelligence
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Current Higher Degree by Research Supervision (University of Adelaide)
Date Role Research Topic Program Degree Type Student Load Student Name 2022 Co-Supervisor Improving the Generalization of Machine Learning Systems Doctor of Philosophy Doctorate Full Time Mr Xuan Ren 2021 Principal Supervisor Computer Vision, Computational Photography, Machine Learning Doctor of Philosophy Doctorate Full Time Mr Liang Chen 2021 Principal Supervisor Deep Learning, 3D Shape Analysis, Few-shot Learning, Low-supervised Learning, Natural Language Processing Doctor of Philosophy Doctorate Full Time Miss Ziqin Zhou 2021 Co-Supervisor Next Generation Digital Sensing for the Food Industry Doctor of Philosophy under a Jointly-awarded Degree Agreement with Doctorate Full Time Ms Eveline van Honk 2021 Co-Supervisor Data efficient learning Doctor of Philosophy Doctorate Full Time Mr Yuhao Lin 2020 Principal Supervisor An Investigation into Unsupervised and Semi-Unsupervised Approaches to Crowd Counting Master of Philosophy Master Part Time Mr Avraham Nisel Chapman 2020 Co-Supervisor Markov Logic Networks. Generalisations and Applications Doctor of Philosophy Doctorate Part Time Mr Xian Wang 2019 Co-Supervisor Machine Learning and Natural Language Processing in Stock Prediction Doctor of Philosophy Doctorate Full Time Mr Jinan Zou 2019 Principal Supervisor Unsupervised learning in Natural Language Processing Doctor of Philosophy Doctorate Full Time Mr Qiaoyang Luo 2018 Principal Supervisor Semi-Supervised Learning in Deep Learning Doctor of Philosophy Doctorate Full Time Mr Hai-Ming Xu -
Past Higher Degree by Research Supervision (University of Adelaide)
Date Role Research Topic Program Degree Type Student Load Student Name 2018 - 2019 Co-Supervisor High-performance Object Detection and Tracking using Deep Learning Master of Philosophy Master Full Time Mr Xinyu Wang 2018 - 2021 Co-Supervisor Deep Learning for 2D and 3D Scene Understanding Doctor of Philosophy Doctorate Full Time Mr Yu Liu 2017 - 2019 Co-Supervisor Context Learning and Weakly Supervised Learning for Semantic Segmentation Doctor of Philosophy Doctorate Full Time Mr Tong Shen 2017 - 2020 Co-Supervisor Efficient Scene Parsing with Imagery and Point Cloud Data Doctor of Philosophy Doctorate Full Time Mr Tong He 2017 - 2021 Co-Supervisor Deep Learning for Image Deblurring and Reflection Removal Doctor of Philosophy Doctorate Full Time Mr Jie Yang 2014 - 2018 Co-Supervisor Mid-level Representations for Action Recognition and Zero-shot Learning Doctor of Philosophy Doctorate Full Time Mr Ruizhi Qiao 2014 - 2018 Co-Supervisor Deep Learning Based RGB-D Vision Tasks Doctor of Philosophy Doctorate Full Time Mr Yuanzhouhan Cao
Connect With Me
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