Dr Lingqiao Liu

Senior Lecturer

School of Computer and Mathematical Sciences

Faculty of Sciences, Engineering and Technology

Eligible to supervise Masters and PhD - email supervisor to discuss availability.

My Research
Publications
Grants and Funding
Teaching
Supervision
Contact

Dr. Lingqiao Liu is an Associate Professor and was an 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 and natural language processing. More information about me can be found in my home page

Journals

Year	Citation
2024	Xie, Y., Zhang, J., Liu, L., Wang, H., Ye, Y., Johan, V., & Xia, Y. (2024). ReFs: A hybrid pre-training paradigm for 3D medical image segmentation. Medical Image Analysis, 91, 10 pages. DOI
2023	Wu, L. Y., Liu, L., Wang, Y., Zhang, Z., Boussaid, F., Bennamoun, M., & Xie, X. (2023). Learning Resolution-Adaptive Representations for Cross-Resolution Person Re-Identification. IEEE Transactions on Image Processing, 32, 4800-4811. DOI
2023	Ding, Y., Liu, L., Tian, C., Zhang, X., & Tian, X. (2023). Balanced image captioning with task-aware decoupled learning and fusion. Neurocomputing, 538, 12 pages. DOI
2022	Liu, D., Wu, L., Zheng, F., Liu, L., & Wang, M. (2022). Verbal-Person Nets: Pose-Guided Multi-Granularity Language-to-Person Generation. IEEE Transactions on Neural Networks and Learning Systems, PP(11), 1-13. DOI Scopus8 WoS11
2022	Wang, X., Liu, L., & Shi, J. Q. (2022). Computationally Efficient Dilated Convolutional Model for Melody Extraction. IEEE Signal Processing Letters, 29, 1599-1603. DOI
2022	Shu, Y., Li, Q., Liu, L., & Xu, G. (2022). Privileged multi-task learning for attribute-aware aesthetic assessment. Pattern Recognition, 132, 1-11. DOI Scopus4 WoS2
2022	Yang, L., Wang, Y., Liu, L., Wang, P., & Zhang, Y. (2022). Center Prediction Loss for Re-identification. Pattern Recognition, 132, 1-11. DOI Scopus2
2022	Wei, X. S., Cui, Q., Yang, L., Wang, P., Liu, L., & Yang, J. (2022). RPC: a large-scale and fine-grained retail product checkout dataset. Science China Information Sciences, 65(9), 2 pages. DOI Scopus13 WoS5
2022	Zhou, Y., Song, X., Zhang, Y., Liu, F., Zhu, C., & Liu, L. (2022). Feature Encoding With Autoencoders for Weakly Supervised Anomaly Detection. IEEE Transactions on Neural Networks and Learning Systems, 33(6), 2454-2465. DOI Scopus40 Europe PMC4
2022	Zhuang, B., Shen, C., Tan, M., Chen, P., Liu, L., & Reid, I. (2022). Structured Binary Neural Networks for Image Recognition. INTERNATIONAL JOURNAL OF COMPUTER VISION, 130(9), 22 pages. DOI Scopus4 WoS3
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, 44(10), 6140-6152. DOI Scopus13 WoS8
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 Scopus35 WoS10
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 Scopus11 WoS10
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 Scopus7 WoS4
2021	Zhang, J., Liu, L., Wang, P., & Zhang, J. (2021). Exploring the auxiliary learning for long-tailed visual recognition. Neurocomputing, 449, 303-314. DOI Scopus6 WoS5
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 Scopus4 WoS2
2021	Shu, Y., Li, Q., Liu, L., & Xu, G. (2021). Semi-supervised Adversarial Learning for Attribute-Aware Photo Aesthetic Assessment. IEEE Transactions on Multimedia, 26, 1-11. DOI Scopus4
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 Scopus55 WoS46
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 Scopus41 WoS28
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 Scopus2 WoS2
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 Scopus21
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 Scopus10
2020	Chen, Y., Shen, C., Chen, H., Wei, X. S., 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 Scopus17 WoS10 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 Scopus22 WoS25
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 Scopus46 WoS27
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. WoS4
2019	Wei, X. S., 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 Scopus93 WoS56 Europe PMC3
2019	Wang, P., Liu, L., Shen, C., & Shen, H. T. (2019). Order-aware convolutional pooling for video based action recognition. Pattern Recognition, 91, 357-365. DOI Scopus23 WoS21
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 Scopus30 WoS25
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 Scopus77
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 Scopus34 WoS24
2017	Liu, L., Wang, P., Shen, C., Wang, L., Van Den Hengel, A., Wang, C., & Shen, H. T. (2017). Compositional model based Fisher vector coding for image classiﬁcation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(12), 2335-2348. DOI Scopus53 WoS37 Europe PMC3
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 Scopus5 WoS6
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 Scopus51 WoS43
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 Scopus66 WoS54 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 Scopus25
2016	Wang, P., Cao, Y., Shen, C., Liu, L., & Shen, H. T. (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 Scopus104 WoS88
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
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 Scopus11 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 Scopus23
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.

Book Chapters

Year	Citation
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
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 Scopus10

Conference Papers

Year	Citation
2024	Chen, L., Zhang, Y., Song, Y., Van Den Hengel, A., & Liu, L. (2024). Domain Generalization via Rationale Invariance. In Proceedings of the IEEE International Conference on Computer Vision (pp. 1751-1760). Paris, France: IEEE. DOI Scopus1
2023	Luo, Q., & Liu, L. (2023). Zero-Shot Slot Filling with Slot-Prefix Prompting and Attention Relationship Descriptor. In Proceedings of the AAAI Conference on Artificial Intelligence, AAAI 2023 Vol. 37 (pp. 13344-13352). Washington, DC, USA: PKP/PS. Part of PKP Publsihing Sewrvices Nework. DOI Scopus1
2023	Zhou, Z., Lei, Y., Zhang, B., Liu, L., & Liu, Y. (2023). ZegCLIP: Towards Adapting CLIP for Zero-shot Semantic Segmentation. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Vol. 2023-June (pp. 11175-11185). Vancouver, BC, Canada: IEEE. DOI Scopus12
2023	Chen, L., Zhang, Y., Song, Y., Shan, Y., & Liu, L. (2023). Improved Test-Time Adaptation for Domain Generalization. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Vol. 2023-June (pp. 24172-24182). Online: IEEE. DOI Scopus3
2023	Pang, T. Y., Ding, B., Liu, L., & Sergiienko, N. (2023). SHORT-TERM SEA SURFACE ELEVATION PREDICTION USING DEEP LEARNING METHODS. In Proceedings of the International Conference on Offshore Mechanics and Arctic Engineering - OMAE Vol. 5. American Society of Mechanical Engineers. DOI
2023	Shu, Y., Van Den Hengel, A., & Liu, L. (2023). Learning Common Rationale to Improve Self-Supervised Representation for Fine-Grained Visual Recognition Problems. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Vol. 2023-June (pp. 11392-11401). Online: IEEE. DOI Scopus1
2023	Wang, Q., Liu, L., Jing, C., Chen, H., Liang, G., Wang, P., & Shen, C. (2023). Learning Conditional Attributes for Compositional Zero-Shot Learning. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Vol. 2023-June (pp. 11197-11206). Online: IEEE. DOI Scopus5
2023	Wang, Z., Liu, L., Wang, L., & Zhou, L. (2023). METransformer: Radiology Report Generation by Transformer with Multiple Learnable Expert Tokens. In Proceedings / CVPR, IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Vol. 2023-June (pp. 11558-11567). Vancouver, BC, Canada: IEEE. DOI Scopus5 WoS2
2023	Xue, X., Yu, D., Liu, L., Liu, Y., Tsutsui, S., Li, Y., . . . Shou, M. Z. (2023). Transformer-based Open-world Instance Segmentation with Cross-task Consistency Regularization. In MM 2023 - Proceedings of the 31st ACM International Conference on Multimedia (pp. 2507-2515). ACM. DOI
2023	Zou, J., Liu, Y., Qi, Y., Cao, H., Liu, L., & Shi, J. Q. (2023). A Generative Approach for Comprehensive Financial Event Extraction at the Document Level. In ICAIF 2023 - 4th ACM International Conference on AI in Finance (pp. 323-330). NY, Brooklyn: ASSOC COMPUTING MACHINERY. DOI
2023	Xu, H. M., Liu, L., Chen, H., Abbasnejad, E., & Felix, R. (2023). Progressive Feature Adjustment for Semi-supervised Learning from Pretrained Models. In Proceedings - 2023 IEEE/CVF International Conference on Computer Vision Workshops, ICCVW 2023 (pp. 3284-3294). FRANCE, Paris: IEEE COMPUTER SOC. DOI
2023	Yang, L., Song, Y., Ren, X., Lyu, C., Wang, Y., Zhuo, J., . . . Zhang, Y. (2023). Out-of-Distribution Generalization in Natural Language Processing: Past, Present, and Future. In EMNLP 2023 - 2023 Conference on Empirical Methods in Natural Language Processing, Proceedings (pp. 4533-4559).
2023	Lin, Y., Xu, H., Liu, L., Zou, J., & Shi, J. (2023). Revisiting Image Reconstruction for Semi-supervised Semantic Segmentation. In 2023 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2023 (pp. 32-40). IEEE. DOI
2022	Xu, H. M., Liu, L., & Abbasnejad, E. (2022). Progressive Class Semantic Matching for Semi-supervised Text Classification. In M. Carpuat, M. -C. De Marneffe, & I. V. Meza Ruiz (Eds.), NAACL 2022 - 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the Conference (pp. 3003-3013). Seattle, Washington & Online: Association for Computational Linguistics. DOI Scopus6
2022	Xu, H. M., Chen, H., Liu, L., & Yin, Y. (2022). Dual Decision Improves Open-Set Panoptic Segmentation. In BMVC 2022 - 33rd British Machine Vision Conference Proceedings (pp. 1-13). London, UK: The British Machine Vision Association.. Scopus2
2022	Yang, L., Liu, H., Liu, L., Zhou, J., Zhang, L., Wang, P., & Zhang, Y. (2022). Pluggable Weakly-Supervised Cross-View Learning for Accurate Vehicle Re-Identification. In ICMR 2022 - Proceedings of the 2022 International Conference on Multimedia Retrieval (pp. 81-89). Online: ACM. DOI Scopus1
2022	Xu, H. M., Liu, L., Bian, Q., & Yang, Z. (2022). Semi-supervised Semantic Segmentation with Prototype-based Consistency Regularization. In S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, & A. Oh (Eds.), Advances in Neural Information Processing Systems Vol. 35 (pp. 1-18). New Orleans, LA, USA: Curran Associates. Scopus12
2022	Chen, L., Zhang, Y., Song, Y., Liu, L., & Wang, J. (2022). Self-supervised Learning of Adversarial Example: Towards Good Generalizations for Deepfake Detection. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Vol. 2022-June (pp. 18689-18698). Online: IEEE. DOI Scopus56 WoS3
2022	Jiao, B., Liu, L., Gao, L., Lin, G., Yang, L., Zhang, S., . . . Zhang, Y. (2022). Dynamically Transformed Instance Normalization Network for Generalizable Person Re-Identification. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 13674 LNCS (pp. 285-301). Online: Springer Nature Switzerland. DOI Scopus15 WoS6
2022	Shu, Y., Yu, B., Xu, H., & Liu, L. (2022). Improving Fine-Grained Visual Recognition in Low Data Regimes via Self-boosting Attention Mechanism. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 13685 LNCS (pp. 449-465). Online: Springer Nature Switzerland. DOI Scopus10 WoS4
2022	Zou, J., Cao, H., Liu, Y., Liu, L., Abbasnejad, E., & Shi, J. Q. (2022). UOA at the FinNLP-2022 ERAI Task: Leveraging the Class Label Description for Financial Opinion Mining. In FinNLP 2022 - 4th Workshop on Financial Technology and Natural Language Processing, Proceedings of the Workshop (pp. 122-126). Online: Association for Computational Linguistics (ACL). Scopus1
2022	Zou, J., Cao, H., Liu, L., Lin, Y., Abbasnejad, E., & Shi, J. Q. (2022). Astock: A New Dataset and Automated Stock Trading based on Stock-specific News Analyzing Model. In FinNLP 2022 - 4th Workshop on Financial Technology and Natural Language Processing, Proceedings of the Workshop (pp. 178-186). Online: Association for Computational Linguistics (ACL). Scopus2
2022	Chen, L., Zhang, Y., Song, Y., Wang, J., & Liu, L. (2022). OST: Improving Generalization of DeepFake Detection via One-Shot Test-Time Training. In Advances in Neural Information Processing Systems Vol. 35. Online: Neural information processing systems foundation. Scopus12
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 Scopus34
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. Scopus9
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 Scopus3
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 Scopus3
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 Scopus5
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 Scopus7 WoS3
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 Scopus12
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 Scopus31
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 Scopus2
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 Scopus2
2019	Abbasnejad, M. E., 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 Scopus13 WoS6
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 Scopus3
2019	Gong, D., Liu, L., Le, V., Saha, B., Mansour, M. R., 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 Scopus920 WoS683
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 Scopus110 WoS81
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 Scopus25 WoS21
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 Scopus16 WoS33
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 Scopus168 WoS109
2018	Milan, A., Pham, T., Vijay, K., Morrison, D., Tow, A. W., 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 Scopus26 WoS19
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 Scopus75 WoS44
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 Scopus24 WoS8
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 Scopus249 WoS92
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 Scopus123 WoS93
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 Scopus46 WoS73
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 Scopus275 WoS167
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 Scopus249
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 Scopus241 WoS180
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. Scopus25
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 Scopus35 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 Scopus10 WoS7
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 Scopus145 WoS103
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 Scopus399 WoS278
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 Scopus12 WoS5
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 Scopus71 WoS48
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 Scopus156 WoS101
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. Scopus67 WoS1
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 Scopus43
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 Scopus22
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 Scopus9
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 Scopus17
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 Scopus16
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 Scopus450
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

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

Current Higher Degree by Research Supervision (University of Adelaide)

Date	Role	Research Topic	Program	Degree Type	Student Load	Student Name
2023	Co-Supervisor	Semi-supervised object detection for mobile robots	Master of Philosophy	Master	Full Time	Mr Xiangyu Shi
2023	Principal Supervisor	3D indoor Scene Reconstruction	Doctor of Philosophy	Doctorate	Full Time	Mr Wenbo Zhang
2023	Co-Supervisor	Implement a scalable, automated workflow for transposon annotation as part of the Ruminant T2T genome sequencing consortium	Doctor of Philosophy	Doctorate	Full Time	Miss Luan Zhong
2023	Principal Supervisor	Low-supervision Learning via Knowledge Transfer from Pretrained Models	Doctor of Philosophy	Doctorate	Full Time	Mr Zicheng Duan
2023	Principal Supervisor	Depth Estimation Using Deep Learning	Master of Philosophy	Master	Full Time	Mr Jiatong Xia
2023	Principal Supervisor	Generative AI: Video Generation from text	Doctor of Philosophy	Doctorate	Full Time	Mr Ankit Yadav
2022	Co-Supervisor	Improving the Few-Shot Generalization of Data-to-Text Generation Models	Doctor of Philosophy	Doctorate	Full Time	Mr Xuan Ren
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	Principal Supervisor	Computer Vision, Computational Photography, Machine Learning	Doctor of Philosophy	Doctorate	Full Time	Mr Liang Chen
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
2019	Principal Supervisor	Unsupervised learning in Natural Language Processing	Doctor of Philosophy	Doctorate	Part Time	Mr Qiaoyang Luo

Past Higher Degree by Research Supervision (University of Adelaide)

Date	Role	Research Topic	Program	Degree Type	Student Load	Student Name
2020 - 2023	Co-Supervisor	Deep Learning for Multipitch Detection and Melody Extraction	Doctor of Philosophy	Doctorate	Part Time	Mr Xian Wang
2019 - 2023	Co-Supervisor	Machine Learning and Natural Language Processing in Stock Prediction	Doctor of Philosophy	Doctorate	Full Time	Mr Jinan Zou
2018 - 2019	Co-Supervisor	High-performance Object Detection and Tracking using Deep Learning	Master of Philosophy	Master	Full Time	Mr Xinyu Wang
2018 - 2022	Principal Supervisor	Deep Semi-Supervised Learning Methodologies and Applications	Doctor of Philosophy	Doctorate	Full Time	Mr Hai-Ming Xu
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: Computer Science