Associate Professor 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
2025	Li, Y., Pang, G., Suo, W., Jing, C., Xi, Y., Liu, L., . . . Wang, P. (2025). CoLeCLIP: Open-Domain Continual Learning via Joint Task Prompt and Vocabulary Learning. IEEE Transactions on Neural Networks and Learning Systems, 1-15. DOI
2025	Shu, Y., Liu, Y., Cao, X., Chen, Q., Zhang, B., Zhou, Z., . . . Liu, L. (2025). Seeing Beyond Labels: Source-Free Domain Adaptation via Hypothesis Consolidation of Prediction Rationale. Transactions on Machine Learning Research, 2025-June.
2024	Lin, W. D., Deng, Y. Y., Gao, Y., Wang, N., Liu, L. Q., Zhang, L., & Wang, P. (2024). CAT: A Simple yet Effective Cross-Attention Transformer for One-Shot Object Detection. Journal of Computer Science and Technology, 39(2), 460-471. DOI
2024	Zhang, Y., Chang, R., Mao, W., Zuo, J., Liu, L., & Han, Y. (2024). Challenges of Automating Interior Construction Progress Monitoring. Journal of Construction Engineering and Management, 150(9), 16 pages. DOI
2024	Liu, Y., Li, Y., Wang, Z., Liang, X., Liu, L., Wang, L., . . . Zhou, L. (2024). A systematic evaluation of GPT-4V's multimodal capability for chest X-ray image analysis. Meta-Radiology, 2(4), 100099. DOI Scopus6
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, 103023. DOI Scopus6 Europe PMC1
2024	Chen, L., Zhang, Y., Song, Y., Zhang, Z., & Liu, L. (2024). A Causal Inspired Early-Branching Structure for Domain Generalization. International Journal of Computer Vision, 132(9), 4052-4072. DOI Scopus3
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 Scopus27 Europe PMC2
2023	Shi, X., Qiao, Y., Wu, Q., Liu, L., & Dayoub, F. (2023). Improving Online Source-free Domain Adaptation for Object Detection by Unsupervised Data Acquisition.
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 Scopus3
2023	Wang, Z., Liu, L., Wang, L., & Zhou, L. (2023). R2GenGPT: Radiology Report Generation with frozen LLMs. Meta-Radiology, 1(3), 100033. DOI Scopus44
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 Scopus25 WoS11 Europe PMC1
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 Scopus2
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 Scopus8 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 Scopus5
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 Scopus20 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 Scopus95 Europe PMC10
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 Scopus8 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 Scopus25 WoS8 Europe PMC4
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 Scopus67 WoS10 Europe PMC3
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 Scopus21 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 Scopus8 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 Scopus8 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 Scopus10
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 Scopus93 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 Scopus68 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 Scopus5 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 Scopus34
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 Scopus17
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 Scopus22 WoS10 Europe PMC2
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 Scopus28 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 Scopus57 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 Scopus131 WoS56 Europe PMC7
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 Scopus25 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 Scopus42 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 Scopus93
2017	Qiao, R., Liu, L., Shen, C., & Hengel, A. V. D. (2017). Visually Aligned Word Embeddings for Improving Zero-shot Learning.
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 Scopus37 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 Scopus56 WoS37 Europe PMC7
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 Europe PMC3
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 Scopus52 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 Scopus74 WoS54 Europe PMC7
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 Scopus27
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 Scopus117 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 Scopus12 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
2008	Liu, L. Q., Fu, Z. Z., Qian, W., Deng, Z. Q., & Xie, W. (2008). Small target detection based on small target isolation degree. Guangdian Gongcheng Opto Electronic Engineering, 35(12), 13-22.
2008	Qian, W., Fu, Z. Z., Liu, L. Q., Deng, Z. Q., & Xie, W. (2008). Voting-strategy-based approach to image registration. Guangdian Gongcheng Opto Electronic Engineering, 35(10), 86-91. Scopus3

Book Chapters

Year	Citation
2025	Shi, X., Qiao, Y., Wu, Q., Liu, L., & Dayoub, F. (2025). Improving Online Source-Free Domain Adaptation for Object Detection by Unsupervised Data Acquisition. In Lecture Notes in Computer Science (Vol. 15629 LNCS, pp. 195-205). Springer Nature Switzerland. DOI
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 Scopus11

Conference Papers

Year	Citation
2025	Lin, Y., Xu, H., Liu, L., & Shi, J. Q. (2025). A Simple-but-Effective Baseline for Training-Free Class-Agnostic Counting. In Proceedings - 2025 IEEE Winter Conference on Applications of Computer Vision, WACV 2025 (pp. 8155-8164). Tucson, AZ, USA: IEEE. DOI Scopus1
2025	Xia, J., Sun, L., & Liu, L. (2025). Enhancing Close-up Novel View Synthesis via Pseudo-labeling. In Proceedings of the AAAI Conference on Artificial Intelligence Vol. 39 (pp. 8567-8574). Philadelphia, USA: Association for the Advancement of Artificial Intelligence (AAAI). DOI
2025	Xu, H. M., Chen, Q., Wang, L., & Liu, L. (2025). Attention-Driven GUI Grounding: Leveraging Pretrained Multimodal Large Language Models Without Fine-Tuning. In Proceedings of the AAAI Conference on Artificial Intelligence Vol. 39 (pp. 8851-8859). Philadelphia, USA: Association for the Advancement of Artificial Intelligence (AAAI). DOI
2025	Wang, Z., Liu, L., Weston, S. R. F., Tian, S., & Li, P. (2025). On Learning Discriminative Features from Synthesized Data for Self-supervised Fine-Grained Visual Recognition. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 15147 LNCS (pp. 101-117). Milan, Italy: Springer Nature Switzerland. DOI
2025	Chapman, A., Xu, H., & Liu, L. (2025). Enhancing Fine-Grained Visual Recognition in the Low-Data Regime Through Feature Magnitude Regularization. In Proceedings - 2024 25th International Conference on Digital Image Computing: Techniques and Applications, DICTA 2024 (pp. 101-108). Perth, Australia: IEEE. DOI
2024	Chen, L., Zhang, Y., Song, Y., Shen, Z., & Liu, L. (2024). LFME: A Simple Framework for Learning from Multiple Experts in Domain Generalization. In Advances in Neural Information Processing Systems Vol. 37. Vancouver, Canada: Neural information processing systems foundation.
2024	Ren, X., Wu, B., & Liu, L. (2024). I Learn Better If You Speak My Language: Understanding the Superior Performance of Fine-Tuning Large Language Models with LLM-Generated Responses. In EMNLP 2024 - 2024 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference (pp. 10225-10245). Hybrid, Miami: Association for Computational Linguistics (ACL). DOI
2024	Lin, Y., Xu, H., Liu, L., Zou, J., & Shi, J. (2024). Revisiting Image Reconstruction for Semi-supervised Semantic Segmentation. In 2023 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2023 (pp. 32-40). Online: IEEE. DOI Scopus1
2024	Zou, J., Guo, M., Tian, Y., Lin, Y., Cao, H., Liu, L., . . . Shi, J. Q. (2024). Semantic Role Labeling Guided Out-of-distribution Detection. In 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation, LREC-COLING 2024 - Main Conference Proceedings (pp. 14641-14651). Online: European Language Resources Association (ELRA). Scopus1
2024	Zhou, Z., Xu, H. -M., Shu, Y., & Liu, L. (2024). Unlocking the Potential of Pre-Trained Vision Transformers for Few-Shot Semantic Segmentation through Relationship Descriptors. In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Vol. 33 (pp. 3817-3827). Seattle, WA, USA: IEEE. DOI Scopus3
2024	Li, Y., Wang, Z., Liu, Y., Wang, L., Liu, L., & Zhou, L. (2024). KARGEN: Knowledge-Enhanced Automated Radiology Report Generation Using Large Language Models. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 15005 LNCS (pp. 382-392). Marrakesh: Springer Science and Business Media Deutschland GmbH. DOI Scopus1
2024	Liu, Y., Wang, Z., Li, Y., Liang, X., Liu, L., Wang, L., & Zhou, L. (2024). MRScore: Evaluating Medical Report with LLM-Based Reward System. In A. Feragen, S. Giannarou, B. Glocker, K. Lekadir, J. A. Schnabel, M. G. Linguraru, & Q. Dou (Eds.), Lecture Notes in Computer Science Vol. 15003 LNCS (pp. 283-292). Marrakesh, Morocco: SPRINGER INTERNATIONAL PUBLISHING AG. DOI
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 Scopus20
2024	Phan, V. M. H., Xie, Y., Qi, Y., Liu, L., Liu, L., Zhang, B., . . . Verjans, J. W. (2024). Decomposing Disease Descriptions for Enhanced Pathology Detection: A Multi-Aspect Vision-Language Pre-training Framework. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2024) (pp. 11492-11501). Seattle, WA, USA: Institute of Electrical and Electronics Engineers (IEEE). DOI Scopus9
2023	Jiao, B., Liu, L., Gao, L., Wu, R., Lin, G., Wang, P., & Zhang, Y. (2023). Toward Re-Identifying Any Animal. In Advances in Neural Information Processing Systems Vol. 36 (pp. 12 pages). Online: Neural information processing systems foundation. Scopus11
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 Proceedings of the 31st ACM International Conference on Multimedia (pp. 2507-2515). Online: 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). Online: Association for Computing Machinery, Inc. DOI Scopus2
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). Online: IEEE. 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 H. Bouamor, J. Pino, & K. Bali (Eds.), Emnlp 2023 2023 Conference on Empirical Methods in Natural Language Processing Proceedings (pp. 4533-4559). SINGAPORE, Singapore: ASSOC COMPUTATIONAL LINGUISTICS-ACL. DOI Scopus12
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 Scopus143
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 Scopus47
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 (pp. 10 pages). Melbourne, Australia: 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 Scopus13
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 Scopus40
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 Scopus76 WoS2
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 Scopus6
2023	Ding, Y., Tian, C., Ding, H., & Liu, L. (2023). The CLIP Model is Secretly an Image-to-Prompt Converter. In A. Oh, T. Neumann, A. Globerson, K. Saenko, M. Hardt, & S. Levine (Eds.), ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 36 (NEURIPS 2023) Vol. 36 (pp. 12 pages). Online: NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). Scopus5
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, K. Cho, & A. Oh (Eds.), ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 35 (NEURIPS 2022) (pp. 14 pages). Online: NEURAL INFORMATION PROCESSING SYSTEMS (NIPS).
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 Scopus11
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.. Scopus4
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 Scopus4
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. Scopus63
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 Scopus181 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 Scopus48 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 Scopus27 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). Scopus4
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 (pp. 14 pages). Online: Neural information processing systems foundation. Scopus42
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 Scopus57
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. DOI Scopus11
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 Scopus5
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 Scopus4
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 Scopus7
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 Scopus9 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 Scopus20
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 Scopus56
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 Scopus6
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 Scopus5
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 Scopus19 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 Scopus1491 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 Scopus128 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 Scopus32 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 Scopus21 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 Scopus209 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 Scopus29 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 Scopus80 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 Scopus286 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 Scopus131 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 Scopus51 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 Scopus337 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 Scopus295
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 Scopus289 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. Scopus27
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 Scopus37 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 Scopus157 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 Scopus423 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 Scopus15 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 Scopus80 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 Scopus166 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. Scopus68 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 Scopus47
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 Europe PMC1
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 Scopus23
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 Scopus10
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 Scopus18
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 Scopus18
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 Scopus458
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
2010	Zhizhong, F., Lingqiao, L., Haiying, X., & Jin, X. (2010). Human computer interaction research and realization based on leg movement analysis. In 2010 International Conference on Apperceiving Computing and Intelligence Analysis Icacia 2010 Proceeding (pp. 421-426). IEEE. DOI Scopus5
2008	Lingqiao, L., Zhizhong, F., Jingjing, X., & Wei, Q. (2008). Edge mapping: A new motion estimation method for video stabilization. In Proceedings International Symposium on Computer Science and Computational Technology Iscsct 2008 Vol. 2 (pp. 440-444). IEEE. DOI Scopus9
2008	Qian, W., Fu, Z., Liu, L., & Deng, Z. (2008). Image registration based on feature extraction and voting strategy. In 2008 International Conference on Communications Circuits and Systems Proceedings Icccas 2008 (pp. 706-709). IEEE. DOI Scopus2

Preprint

Year	Citation
2024	Phan, V. M. H., Xie, Y., Qi, Y., Liu, L., Liu, L., Zhang, B., . . . Verjans, J. W. (2024). Decomposing Disease Descriptions for Enhanced Pathology Detection: A Multi-Aspect Vision-Language Pre-training Framework..

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
2025	Principal Supervisor	Improving the Automation Efficiency of Q&A Chatbots Based on AI Agents	Master of Philosophy	Master	Full Time	Ms Shiya Huang
2025	Principal Supervisor	Toward Multi-Agent 3D Dynamic Scene Generation: A Framework for Complex Interactions in Shared Virtual Environments	Master of Philosophy	Master	Full Time	Mr Zhiyuan Zhang
2024	Principal Supervisor	Long-tailed visual recognition in an open world	Doctor of Philosophy	Doctorate	Full Time	Mr Qing Zhong
2024	Principal Supervisor	Novel View Synthesis in Real-World Scenarios	Doctor of Philosophy	Doctorate	Full Time	Mr Jiatong Xia
2024	Co-Supervisor	Data-driven physically plausible dexterous manipulation	Doctor of Philosophy	Doctorate	Full Time	Mr King Hang Wong
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	Generative AI: Video Generation from text	Doctor of Philosophy	Doctorate	Full Time	Mr Ankit Yadav
2023	Co-Supervisor	Semi-supervised object detection for mobile robots	Master of Philosophy	Master	Full Time	Mr Xiangyu Shi
2022	Principal Supervisor	Improving the Few-Shot Generalization of Data-to-Text Generation Models	Doctor of Philosophy	Doctorate	Full Time	Mr Xuan Ren

Past Higher Degree by Research Supervision (University of Adelaide)

Date	Role	Research Topic	Program	Degree Type	Student Load	Student Name
2023 - 2024	Principal Supervisor	Neural Radiance Fields in Real-World Scenarios	Master of Philosophy	Master	Full Time	Mr Jiatong Xia
2021 - 2024	Principal Supervisor	Low-Shot Learning based on Pre-trained Model	Doctor of Philosophy	Doctorate	Full Time	Dr Ziqin Zhou
2021 - 2024	Principal Supervisor	Domain Generalization and its Application in Deepfake Detection	Doctor of Philosophy	Doctorate	Full Time	Mr Liang Chen
2021 - 2025	Co-Supervisor	Strategic Reduction of Training and Annotation in Computer Vision: Leveraging Pre-trained Models and Auxiliary Tasks for Efficient Learning	Doctor of Philosophy	Doctorate	Full Time	Mr Yuhao Lin
2020 - 2024	Principal Supervisor	Enhancing Model Generalization in Weakly Supervised and Low-Shot Transfer Learning Scenarios	Master of Philosophy	Master	Part Time	Mr Avi Nisel Chapman
2020 - 2023	Co-Supervisor	Deep Learning for Multipitch Detection and Melody Extraction	Doctor of Philosophy	Doctorate	Part Time	Mr Xian Wang
2020 - 2024	Co-Supervisor	Convolutional Neural Network for Analysing Gravitational Wave Signals	Master of Philosophy	Master	Full Time	Miss Kendall Louise Jenner
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: Lot 14
Building: Australian Institute for Machine Learning Building, floor First Floor
Room: 1.23
Org Unit: Computer Science