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.
Dr. Lingqiao Liu is a Senior Lecturer and ARC DECRA Fellow in School of Computer Science, University of Adelaide, Australia. He obtained his P.h.D. from the Australian National University in 2014. He is a recipient of ARC DECRA (Discovery Early Career Researcher Award) award in 2016 and the University of Adelaide Research Fellowship award in 2016. He has a broad research interest in machine learning, computer vision and natural language processing. More information about me can be found in my home page
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Journals
Year Citation 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.
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.
2022 Wang, X., Liu, L., & Shi, J. Q. (2022). Computationally Efficient Dilated Convolutional Model for Melody Extraction. IEEE Signal Processing Letters, 29, 1599-1603.
2022 Shu, Y., Li, Q., Liu, L., & Xu, G. (2022). Privileged multi-task learning for attribute-aware aesthetic assessment. Pattern Recognition, 132, 1-11.
Scopus3 WoS22022 Yang, L., Wang, Y., Liu, L., Wang, P., & Zhang, Y. (2022). Center Prediction Loss for Re-identification. Pattern Recognition, 132, 1-11.
Scopus22022 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.
Scopus8 WoS22022 Xu, H. M., Liu, L., Bian, Q., & Yang, Z. (2022). Semi-supervised Semantic Segmentation with Prototype-based Consistency Regularization. Advances in Neural Information Processing Systems, 35.
Scopus42022 Xu, H. M., Liu, L., & Abbasnejad, E. (2022). Progressive Class Semantic Matching for Semi-supervised Text Classification. NAACL 2022 - 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the Conference, 3003-3013.
Scopus22022 Xu, H. M., Chen, H., Liu, L., & Yin, Y. (2022). Dual Decision Improves Open-Set Panoptic Segmentation. BMVC 2022 - 33rd British Machine Vision Conference Proceedings. 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.
Scopus5 WoS52022 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.
Scopus28 Europe PMC32022 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.
Scopus2 WoS12021 Zhuang, B., Tan, M., Liu, J., Liu, L., Reid, I., & Shen, C. (2021). Effective Training of Convolutional Neural Networks with Low-bitwidth Weights and Activations. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(10), 6140-6152.
Scopus12 WoS82021 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.
Scopus22 WoS102021 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.
Scopus10 WoS102021 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.
Scopus5 WoS42021 Zhang, J., Liu, L., Wang, P., & Zhang, J. (2021). Exploring the auxiliary learning for long-tailed visual recognition. Neurocomputing, 449, 303-314.
Scopus5 WoS52021 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.
Scopus2 WoS22021 Shu, Y., Li, Q., Liu, L., & Xu, G. (2021). Semi-supervised Adversarial Learning for Attribute-Aware Photo Aesthetic Assessment. IEEE Transactions on Multimedia, 1-11.
Scopus42021 Lei, Y., Liu, Y., Zhang, P., & Liu, L. (2021). Towards using count-level weak supervision for crowd counting. Pattern Recognition, 109, 1-13.
Scopus42 WoS352020 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.
Scopus35 WoS282020 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.
Scopus2 WoS22020 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.
Scopus202020 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.
Scopus82020 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.
Scopus17 WoS10 Europe PMC12020 Zhang, L., Wang, P., Shen, C., Liu, L., Wei, W., Zhang, Y., & van den Hengel, A. (2020). Adaptive importance learning for improving lightweight image super-resolution network. International Journal of Computer Vision, 128(2), 479-499.
Scopus22 WoS252020 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.
Scopus43 WoS272019 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.
WoS42019 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.
Scopus83 WoS56 Europe PMC22019 Wang, P., Liu, L., Shen, C., & Shen, H. T. (2019). Order-aware convolutional pooling for video based action recognition. Pattern Recognition, 91, 357-365.
Scopus22 WoS212019 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.
Scopus26 WoS252018 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.
Scopus682017 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.
Scopus31 WoS242017 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.
Scopus49 WoS37 Europe PMC32017 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.
Scopus5 WoS62017 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.
Scopus49 WoS432016 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.
Scopus65 WoS54 Europe PMC52016 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.
Scopus232016 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.
Scopus103 WoS882016 Liu, L., Wang, L., & Shen, C. (2016). A generalized probabilistic framework for compact codebook creation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(2), 224-237.
Scopus3 WoS22015 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.
Scopus11 WoS8 Europe PMC12014 Wang, L., Zhou, L., Shen, C., Liu, L., & Liu, H. (2014). A hierarchical word-merging algorithm with class separability measure. IEEE Transactions on Pattern Analysis and Machine Intelligence, 36(3), 417-435.
Scopus13 WoS9 Europe PMC12013 Liu, X., Yin, J., Wang, L., Liu, L., Liu, J., Hou, C., & Zhang, J. (2013). An adaptive approach to learning optimal neighborhood kernels. IEEE Transactions on Cybernetics, 43(1), 371-384.
Scopus232012 Liu, X., Wang, L., Yin, J., & Liu, L. (2012). Incorporation of radius-info can be simple with SimpleMKL. Neurocomputing, 89, 30-38.
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.
Scopus32014 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.
Scopus9 -
Conference Papers
Year Citation 2023 Luo, Q., & Liu, L. (2023). Zero-Shot Slot Filling with Slot-Prefix Prompting and Attention Relationship Descriptor. In Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023 Vol. 37 (pp. 13344-13352). 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.
Scopus52023 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). CANADA, Vancouver: IEEE COMPUTER SOC.
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.
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). CANADA, Vancouver: IEEE COMPUTER SOC.
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). CANADA, Vancouver: IEEE COMPUTER SOC.
2023 Wang, Z., Liu, L., Wang, L., & Zhou, L. (2023). METransformer: Radiology Report Generation by Transformer with Multiple Learnable Expert Tokens. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Vol. 2023-June (pp. 11558-11567). CANADA, Vancouver: IEEE COMPUTER SOC.
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).
Scopus12022 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).
Scopus22022 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.
Scopus42022 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.
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.
Scopus23 WoS12022 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.
Scopus3 WoS12022 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.
Scopus8 WoS42021 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.
Scopus312021 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.
Scopus72021 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.
Scopus22021 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.
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.
Scopus42020 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.
Scopus5 WoS32020 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.
Scopus122020 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.
Scopus262020 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.
Scopus22020 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.
Scopus22019 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.
Scopus13 WoS62019 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.
Scopus32019 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.
Scopus748 WoS5322019 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.
Scopus100 WoS812019 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.
Scopus22 WoS212018 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.
Scopus15 WoS342018 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.
Scopus154 WoS1092018 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.
Scopus26 WoS192017 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.
Scopus73 WoS442017 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.
Scopus24 WoS82017 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.
Scopus240 WoS922017 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.
Scopus122 WoS932017 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.
Scopus43 WoS732017 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.
Scopus260 WoS1672017 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.
Scopus2352017 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.
Scopus221 WoS1802017 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.
Scopus222016 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.
Scopus35 WoS252016 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.
Scopus10 WoS72016 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.
Scopus141 WoS1032016 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.
Scopus387 WoS2782016 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.
Scopus12 WoS52015 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.
Scopus70 WoS482015 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.
Scopus154 WoS1012014 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.
Scopus65 WoS12014 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.
Scopus422014 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.
Scopus22014 Liu, L., & Wang, L. (2014). HEp-2 cell image classification with multiple linear descriptors. In Pattern Recognition Vol. 47 (pp. 2400-2408). Elsevier BV.
Scopus222013 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.
Scopus102013 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.
Scopus92013 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.
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.
Scopus172012 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.
Scopus162011 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.
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.
Scopus4432011 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.
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
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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
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