Associate Professor Lingqiao Liu

• 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, 36(8), 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.
  2025	Jiao, B., Liu, L., Gao, L., Oliver Wu, D., Lin, G., Wang, P., & Zhang, Y. (2025). Generalizable Person Re-Identification From a 3D Perspective: Addressing Unpredictable Viewpoint Changes. IEEE Transactions on Information Forensics and Security, 20, 6576-6591. DOI Scopus1 WoS1
  2025	Zhang, Y., Chang, R., Mao, W., Zuo, J., Zhanga, W. E., & Liu, L. (2025). Low-Cost iOS-Based Automated Detection of Under-Construction Interior Drywalls: An Exploratory Study. Journal of Construction Engineering and Management, 151(10), 18 pages. DOI
  2025	Wang, Q., Liu, L., Jing, C., Wang, P., Zhang, Y., & Shen, C. (2025). Learning Dual-Stream Conditional Concepts in Compositional Zero-Shot Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 47(11), 1-18. DOI
  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 Scopus2 WoS1
  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 Scopus9 WoS9
  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 Scopus8 WoS6 Europe PMC2
  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 Scopus6 WoS5
  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 Scopus31 WoS26 Europe PMC3
  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), 7 pages. DOI Scopus79 WoS35
  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 Scopus28 WoS28 Europe PMC3
  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 Scopus4 WoS2
  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 Scopus10 WoS10
  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 WoS4
  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 Scopus24 WoS18
  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 Scopus110 Europe PMC17
  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 WoS8
  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 Scopus27 WoS22 Europe PMC5
  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 Scopus79 WoS69 Europe PMC6
  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 Scopus25 WoS24
  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 Scopus9 WoS8
  2021	Zhang, J., Liu, L., Wang, P., & Zhang, J. (2021). Exploring the auxiliary learning for long-tailed visual recognition. Neurocomputing, 449, 303-314. DOI Scopus9 WoS9
  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 Scopus10 WoS10
  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 WoS9
  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 Scopus96 WoS83
  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 Scopus70 WoS67
  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 Scopus6 WoS6
  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 Scopus35
  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 WoS15 Europe PMC3
  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 WoS33
  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 Scopus61 WoS50
  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. WoS12
  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 Scopus137 WoS113 Europe PMC13
  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 WoS22
  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 Scopus43 WoS41
  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 Scopus99
  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 Scopus38 WoS32
  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 WoS44 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 PMC4
  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 WoS45
  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 Scopus75 WoS66 Europe PMC9
  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 Scopus28 WoS22
  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 Scopus119 WoS93
  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 WoS10 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 A. DelBue, C. Canton, J. Pont-Tuset, & T. Tommasi (Eds.), Lecture Notes in Computer Science (Vol. 15629 LNCS, pp. 195-205). SPRINGER INTERNATIONAL PUBLISHING AG. DOI Scopus1 WoS1
  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 Scopus12

• Conference Papers

  Year	Citation
  2026	Huy, T. D., Shoby, A., Tran, S., Xie, Y., Chen, Q., Nguyen, P. L., . . . Phan, M. H. (2026). PedCLIP: A Vision-Language Model for Pediatric X-Rays with Mixture of Body Part Experts. In J. C. Gee, D. C. Alexander, J. Hong, J. E. Iglesias, C. H. Sudre, A. Venkataraman, . . . J. Park (Eds.), Lecture Notes in Computer Science Vol. 15964 LNCS (pp. 487-497). Springer Nature Switzerland. DOI
  2025	Zhang, W., Li, Y., Qiao, Y., Huang, S., Liu, J., Dayoub, F., . . . Liu, L. (2025). Effective Tuning Strategies for Generalist Robot Manipulation Policies. In 2025 IEEE International Conference on Robotics and Automation (ICRA) (pp. 7255-7262). Atlanta, GA, USA: IEEE. DOI
  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 Scopus1 WoS1
  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. Scopus2
  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). Scopus2
  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 Scopus5 WoS5
  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 Scopus8 WoS2
  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 Scopus29 WoS20
  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 Scopus12 WoS6
  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. Scopus15 WoS3
  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 WoS1
  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 Scopus15 WoS7
  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 Scopus187 WoS127
  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 Scopus51 WoS34
  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 Scopus1 WoS1
  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 Scopus18 WoS11
  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 Scopus46 WoS37
  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 Scopus112 WoS84
  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 Scopus8
  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). WoS22
  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 Scopus12
  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 WoS4
  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. Scopus81
  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 Scopus215 WoS143
  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 Scopus53 WoS48
  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 Scopus29 WoS25
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  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 Scopus58 WoS51
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  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 WoS11
  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 Scopus1623 WoS1318
  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 Scopus130 WoS107
  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 WoS33
  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 Scopus25 WoS94
  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 Scopus217 WoS175
  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 WoS24
  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 Scopus82 WoS51
  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 WoS11
  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 Scopus297 WoS173
  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 Scopus133 WoS111
  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 Scopus52 WoS82
  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 Scopus348 WoS274
  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 Scopus300
  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 Scopus294 WoS281
  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 WoS31
  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 Scopus159 WoS117
  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 Scopus430 WoS298
  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 Scopus16 WoS6
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• 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..

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
  2025	Principal Supervisor	Low-Cost Implementation of Visual Pseudo-Tactile and Multitasking Imitation Learning	Master of Philosophy	Master	Full Time	Mr Yukun Chen
  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
  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
  2024 - 2025	Principal Supervisor	Robust and Generalizable Temporal Video Understanding	Doctor of Philosophy	Doctorate	Full Time	Mr Qing Zhong
  2023 - 2024	Principal Supervisor	Neural Radiance Fields in Real-World Scenarios	Master of Philosophy	Master	Full Time	Mr Jiatong Xia
  2023 - 2025	Co-Supervisor	Domain Adaptation Object Detection for Mobile Robots	Master of Philosophy	Master	Full Time	Mr Xiangyu Shi
  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