Dr Lingqiao Liu

• 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. DOI Scopus6 WoS4
  2021	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. DOI Scopus2 WoS2
  2021	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. DOI Scopus2
  2021	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. DOI Scopus6
  2021	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. DOI
  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. DOI
  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. DOI Scopus3 WoS2
  2021	Zhang, J., Liu, L., Wang, P., & Zhang, J. (2021). Exploring the auxiliary learning for long-tailed visual recognition. Neurocomputing, 449, 303-314. DOI Scopus1 WoS1
  2021	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. DOI Scopus3
  2020	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. DOI Scopus19 WoS11
  2020	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. DOI
  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. DOI Scopus10 WoS9
  2020	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. DOI Scopus9
  2020	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. DOI Scopus12 WoS7 Europe PMC1
  2020	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. DOI Scopus16 WoS16
  2020	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. DOI Scopus4
  2019	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. DOI Scopus15 WoS16
  2019	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. DOI Scopus43 WoS27
  2019	Wang, P., Liu, L., Shen, C., & Shen, H. (2019). Order-aware convolutional pooling for video based action recognition. Pattern Recognition, 91, 357-365. DOI Scopus13 WoS13
  2019	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. WoS1
  2018	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. DOI Scopus44
  2017	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. DOI Scopus25 WoS20
  2017	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. DOI Scopus3 WoS4
  2017	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. DOI Scopus41 WoS35
  2017	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. DOI Scopus40 WoS27 Europe PMC3
  2016	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. DOI Scopus52 WoS43 Europe PMC6
  2016	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. DOI Scopus20
  2016	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. DOI Scopus3 WoS2
  2016	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. DOI Scopus73 WoS73
  2015	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. DOI Scopus10 WoS8 Europe PMC1
  2014	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. DOI Scopus13 WoS9 Europe PMC1
  2013	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. DOI Scopus22
  2012	Liu, X., Wang, L., Yin, J., & Liu, L. (2012). Incorporation of radius-info can be simple with SimpleMKL. Neurocomputing, 89, 30-38. DOI Scopus20
  	Qiao, 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. DOI Scopus8
  2014	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. DOI Scopus3

• Conference Papers

  Year	Citation
  2021	Xu, H. M., Liu, L., & Gong, D. (2021). Semi-supervised Learning via Conditional Rotation Angle Estimation. In DICTA 2021 - 2021 International Conference on Digital Image Computing: Techniques and Applications (pp. 1-8). online: IEEE. DOI
  2021	Kang, L., Liu, J., Liu, L., & Ye, D. (2021). Label definitions augmented interaction model for legal charge prediction. In Proceedings of the 43rd European Conference on Information Retrieval (ECIR 2021), as published in Lecture Notes in Computer Science Vol. 12656 (pp. 270-283). Cham, Switzerland: Springer. DOI
  2021	Luo, Q., Liu, L., Lin, Y., & Zhang, W. (2021). Don't Miss the Labels: Label-semantic Augmented Meta-Learner for Few-Shot Text Classification. In C. Zong, F. Xia, W. Li, & R. Navigli (Eds.), Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 (pp. 2773-2782). Stroudsburg, PA, USA: Association for Computational Linguistics. DOI
  2021	Gou, Y., Lei, Y., Liu, L., Dai, Y., & Shen, C. (2021). Contextualize Knowledge Bases with Transformer for End-to-end Task-Oriented Dialogue Systems. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (pp. 4300-4310). online: Association for Computational Linguistics.
  2020	Liu, Y., Liu, L., Zhang, H., Rezatofighi, H., Yan, Q., & Reid, I. D. (2020). Meta Learning with Differentiable Closed-form Solver for Fast Video Object Segmentation.. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 8439-8446). Las Vegas, NV, USA (Virtual): IEEE. DOI
  2020	Liu, Y., Liu, L., Wang, P., Zhang, P., & Lei, Y. (2020). Semi-supervised Crowd Counting via Self-training on Surrogate Tasks. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 12360 LNCS (pp. 242-259). Switzerland: Springer International Publishing. DOI Scopus6
  2020	Wang, X., Liu, L., & Shi, Q. (2020). Harmonic Structure-Based Neural Network Model for Music Pitch Detection. In Proceedings - 19th IEEE International Conference on Machine Learning and Applications, ICMLA 2020 (pp. 87-92). online: IEEE. DOI Scopus1
  2020	Wang, X., Liu, L., & Shi, Q. (2020). Enhancing Piano Transcription by Dilated Convolution. In Proceedings - 19th IEEE International Conference on Machine Learning and Applications, ICMLA 2020 (pp. 1446-1453). online: IEEE. DOI Scopus1
  2020	Liao, Z., Liu, L., Wu, Q., Teney, D., Shen, C., Van Den Hengel, A., & Verjans, J. (2020). Medical data inquiry using a question answering model. In Proceedings: 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI 2020) Vol. 2020-April (pp. 1490-1493). online: IEEE. DOI Scopus4 WoS2
  2020	Teney, D., Wang, P., Cao, J., Liu, L., Shen, C., & Van Den Hengel, A. (2020). V-PROM: A benchmark for visual reasoning using visual progressive matrices. In Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20) Vol. 34 (pp. 12071-12078). Palo Alto, CA: Association for the Advancement of Artificial Intelligence. DOI Scopus3
  2019	Abbasnejad, M., Shi, Q., Van Den Hengel, A., & Liu, L. (2019). A generative adversarial density estimator. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Vol. 2019-June (pp. 10774-10783). online: IEEE. DOI Scopus6 WoS2
  2019	Wang, X., Liu, L., & Shi, Q. (2019). Exploiting stereo sound channels to boost performance of neural network-based music transcription. In Proceedings - 18th IEEE International Conference on Machine Learning and Applications, ICMLA 2019 (pp. 1353-1358). online: IEEE. DOI Scopus2
  2019	Gong, D., Liu, L., Le, V., Saha, B., Mansour, M., Venkatesh, S., & Van Den Hengel, A. (2019). Memorizing normality to detect anomaly: Memory-augmented deep autoencoder for unsupervised anomaly detection. In Proceedings of the IEEE International Conference on Computer Vision Vol. 2019-October (pp. 1705-1714). online: IEEE. DOI Scopus233 WoS174
  2019	Zhuang, B., Shen, C., Tan, M., Liu, L., & Reid, I. (2019). Structured binary neural networks for accurate image classification and semantic segmentation. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Vol. 2019-June (pp. 413-422). online: IEEE. DOI Scopus60 WoS51
  2019	Wei, X. S., Zhang, C. L., Liu, L., Shen, C., & Wu, J. (2019). Coarse-to-fine: A RNN-based hierarchical attention model for vehicle re-identification. In Proceedings of the 14th Asian Conference on Computer Vision (ACCV 2018), as published in Lecture Notes in Computer Science Vol. 11362 (pp. 575-591). Switzerland: Springer. DOI Scopus13 WoS12
  2019	Abbasnejad, M. E., Shi, Q., Van Den Hengel, A., & Liu, L. (2019). A Generative Adversarial Density Estimator.. In CVPR (pp. 10782-10791). online: Computer Vision Foundation / IEEE.
  2018	Milan, A., Pham, T., Vijay, K., Morrison, D., Tow, A., Liu, L., . . . Leitner, J. (2018). Semantic segmentation from limited training data. In 2018 IEEE International Conference on Robotics and Automation (ICRA) Vol. abs/1709.07665 (pp. 1908-1915). online: IEEE. DOI Scopus22 WoS15
  2018	Yang, J., Gong, D., Liu, L., & Shi, Q. (2018). Seeing Deeply and Bidirectionally: A Deep Learning Approach for Single Image Reflection Removal. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 11207 LNCS (pp. 675-691). Switzerland: Springer Nature. DOI Scopus9 WoS18
  2018	Zhuang, B., Shen, C., Tan, M., Liu, L., & Reid, I. (2018). Towards effective low-bitwidth convolutional neural networks. In Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2018) (pp. 7920-7928). Piscataway, NJ.: IEEE. DOI Scopus94 WoS68
  2017	Wang, P., Liu, L., Shen, C., Huang, Z., van den Hengel, A., & Shen, H. (2017). Multi-attention network for one shot learning. In Proceedings of the 30th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017) Vol. 2017-January (pp. 6212-6220). Online: IEEE. DOI Scopus57 WoS32
  2017	Li, Y., Lin, G., Zhuang, B., Liu, L., Shen, C., & van den Hengel, A. (2017). Sequential person recognition in photo albums with a recurrent network. In Proceedings: 30th IEEE Conference on Computer Vision and Pattern Recognition Vol. 2017-January (pp. 5660-5668). online: IEEE. DOI Scopus21 WoS6
  2017	Chen, Y., Shen, C., Wei, X., Liu, L., & Yang, J. (2017). Adversarial PoseNet: a structure-aware convolutional network for human pose estimation. In Proceedings of the IEEE International Conference on Computer Vision (ICCV 2017) Vol. 2017 (pp. 1221-1230). Piscataway, NJ: IEEE. DOI Scopus177 WoS85
  2017	Zhuang, B., Liu, L., Shen, C., & Reid, I. (2017). Towards context-aware interaction recognition for visual relationship detection. In Proceedings 2017 IEEE International Conference on Computer Vision ICCV 2017 Vol. 2017-October (pp. 589-598). Venice, Italy: IEEE. DOI Scopus89 WoS62
  2017	Teney, D., Liu, L., & van den Hengel, A. (2017). Graph-structured representations for visual question answering. In Proceedings of the 30th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017) Vol. 2017-January (pp. 3233-3241). Online: IEEE. DOI Scopus143 WoS102
  2017	Zhuang, B., Liu, L., Li, Y., Shen, C., & Reid, I. (2017). Attend in groups: a weakly-supervised deep learning framework for learning from web data. In Proceedings of the 30th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017) Vol. 2017-January (pp. 2915-2924). Online: IEEE. DOI Scopus34 WoS81
  2017	Gong, D., Yang, J., Liu, L., Zhang, Y., Reid, I., Shen, C., . . . Shi, Q. (2017). From motion blur to motion flow: a deep learning solution for removing heterogeneous motion blur. In Proceedings of the 30th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017) Vol. 2017-January (pp. 3806-3815). Online: IEEE. DOI Scopus179 WoS101
  2017	Chen, Z., Jacobson, A., Sunderhauf, N., Upcroft, B., Liu, L., Shen, C., . . . Milford, M. (2017). Deep Learning Features at Scale for Visual Place Recognition. In Proceedings - IEEE International Conference on Robotics and Automation (pp. 3223-3230). Online: IEEE. DOI Scopus171
  2017	Shen, T., Lin, G., Liu, L., Shen, C., & Reid, I. (2017). Weakly supervised semantic segmentation based on web image co-segmentation. In British Machine Vision Conference 2017, BMVC 2017 (pp. 1-12). online: BMVC. Scopus19
  2016	Ge, Z., McCool, C., Sanderson, C., Wang, P., Liu, L., Reid, I., & Corke, P. (2016). Exploiting temporal information for DCNN-based fine-grained object classification. In A. Liew, B. Lovell, C. Fookes, J. Zhou, Y. Gao, M. Blumenstein, & Z. Wang (Eds.), Proceedings of the 2016 International Conference on Digital Image Computing: Techniques and Applications (DICTA 2016) (pp. 442-447). Gold Coast, AUSTRALIA: IEEE. DOI Scopus7 WoS2
  2016	Li, Y., Liu, L., Shen, C., & van den Hengel, A. (2016). Image co-localization by mimicking a good detector’s confidence score distribution. In B. Leibe, J. Matas, N. Sebe, & M. Welling (Eds.), Proceedings of the 14th European Conference on Computer Vision, Part II Vol. 9906 LNCS (pp. 19-34). Amsterdam, Netherlands: Springer International Publishing. DOI Scopus31 WoS25
  2016	Wang, P., Liu, L., Shen, C., Huang, Z., Van Den Hengel, A., & Shen, H. (2016). What's wrong with that object? Identifying images of unusual objects by modelling the detection score distribution. In Proceedings of the 29th IEEE Computer Society Conference on Computer Vision and Pattern Recognition Vol. 2016-December (pp. 1573-1581). Las Vegas, NV: IEEE. DOI Scopus8 WoS4
  2016	Qiao, R., Liu, L., Shen, C., & van den Hengel, A. (2016). Less is more: zero-shot learning from online textual documents with noise suppression. In Proceedings of the 29th IEEE Computer Society Conference on Computer Vision and Pattern Recognition Vol. 2016-December (pp. 2249-2257). Las Vegas, NV: IEEE. DOI Scopus102 WoS66
  2016	Wu, Q., Shen, C., Liu, L., Dick, A., & Van Den Hengel, A. (2016). What value do explicit high level concepts have in vision to language problems?. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Vol. 2016-December (pp. 203-212). Las Vegas, NV: IEEE. DOI Scopus329 WoS215
  2015	Li, Y., Liu, L., Shen, C., & Van Den Hengel, A. (2015). Mid-level deep pattern mining. In Proceedings of the 2015 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Vol. 07-12-June-2015 (pp. 971-980). Boston, MA: IEEE. DOI Scopus66 WoS44
  2015	Liu, L., Shen, C., & van den Hengel, A. (2015). The treasure beneath convolutional layers: cross-convolutional-layer pooling for image classification. In Proceedings of the 2015 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Vol. 07-12-June-2015 (pp. 4749-4757). Boston, MA: IEEE. DOI Scopus132 WoS82
  2014	Wang, C., Wang, L., & Liu, L. (2014). Progressive mode-seeking on graphs for sparse feature matching. In Proceedings of the 13th European Conference on Computer Vision Vol. 8690 LNCS (pp. 788-802). Zurich, Switzerland: Springer. DOI Scopus34
  2014	Liu, L., Shen, C., Wang, L., Van Den Hengel, A., & Wang, C. (2014). Encoding high dimensional local features by sparse coding based fisher vectors. In Proceedings of the 27th International Conference on Neural Information Processing Systems Vol. 2 (pp. 1143-1151). Online: MIT Press. Scopus62 WoS1
  2014	Zhou, L., Wang, L., Liu, L., Ogunbona, P., & Shen, D. (2014). Max-margin based learning for discriminative Bayesian network from neuroimaging data. In Proceedings of the 17th International Conference on Medical Image Computing and Computer-Assisted Intervention Vol. 17 (pp. 321-328). Boston, MA, USA: Springer. DOI Scopus2
  2014	Liu, L., & Wang, L. (2014). HEp-2 cell image classification with multiple linear descriptors. In Pattern Recognition Vol. 47 (pp. 2400-2408). Elsevier BV. DOI Scopus20
  2013	Wang, C., Wang, L., & Liu, L. (2013). Improving graph matching via density maximization. In 2013 IEEE International Conference on Computer Vision (ICCV) (pp. 3424-3431). USA: IEEE. DOI Scopus10
  2013	Liu, L., & Wang, L. (2013). A scalable unsupervised feature merging approach to efficient dimensionality reduction of high-dimensional visual data. In 2013 IEEE International Conference on Computer Vision (ICCV) (pp. 3008-3015). USA: IEEE. DOI Scopus8
  2013	Zhang, J., Wang, L., Liu, L., Zhou, L., & Li, W. (2013). Accelerating the divisive information-theoretic clustering of visual words. In 2013 International Conference on Digital Image Computing: Techniques and Applications (DICTA) (pp. 1-8). USA: IEEE. DOI
  2013	Zhou, L., Wang, L., Liu, L., Ogunbona, P., & Shen, D. (2013). Discriminative brain effective connectivity analysis for Alzheimer's disease: a kernel learning approach upon sparse Gaussian Bayesian network. In 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 2243-2250). USA: IEEE. DOI Scopus16
  2012	Liu, L., & Wang, L. (2012). What has my classifier learned? Visualizing the classification rules of bag-of-feature model by support region detection. In 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 3586-3593). USA: IEEE. DOI Scopus14
  2011	Liu, L., & Wang, L. (2011). Exploring latent class information for image retrieval using the Bag-of-Feature model. In Proceedings of the 19th ACM international conference on Multimedia (pp. 1405-1408). New York; USA: ACM. DOI
  2011	Liu, L., Wang, L., & Liu, X. (2011). In defense of soft-assignment coding. In 2011 IEEE International Conference on Computer Vision (ICCV) (pp. 2486-2493). USA: IEEE. DOI Scopus430
  2011	Liu, L., Wang, L., & Shen, C. (2011). A generalized probabilistic framework for compact codebook creation. In Proceedings IEEE Conference on Computer Vision and Pattern Recognition (pp. 1537-1544). USA: IEEE. DOI Scopus9 WoS6

Introduction to Statistic Machine Learning, Puzzle based Learning, Big Data Analysis and Project, Artificial Intelligence

• 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

• Position: Senior Lecturer
• Phone: 83132133
• Email: lingqiao.liu@adelaide.edu.au
• Fax: 83134366
• Campus: North Terrace
• Building: Australian Institute for Machine Learning, floor 1
• Org Unit: School of Computer Science