Professor Javen Shi

Professor

School of Computer and Mathematical Sciences

Faculty of Sciences, Engineering and Technology

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

Professor Javen Qinfeng Shi is the Founding Director of Causal AI Group at the University of Adelaide, and one of the directors at Australian Institute for Machine Learning (AIML). His research interests include causation, AI, mind and metaphysics. Google Scholar ranks him 4th globally in causation and 7th in probabilistic graphical models. He served as a panellist for the Responsible AI Think Tank from 2022 to 2024 and currently holds the position of an AI Industry Forum panellist from 2024 onward, actively contributing to the cultivation of the national and state AI ecosystem. He has transferred his research to diverse industries including material discovery, agriculture, mining, sport, manufacturing, bushfire, health and education.Recent awards include: 1) 1st place at Open Catalyst Challenge at NeurIPS AI for Science 2023, using AI to discover energy material; 2) Won AUS/NZ Bushfire Data Quest 2020 using AI to predict fire spread, which led to winning a Citizen Science Grant in 2021, and released a bushfire app NOBURN in 2023 (over 50 media coverages); 3) Finalist of SA Department of Energy and Mining Gawler Challenge 2020 (over 2k participants from 100+ countries) with his team's work being considered as ''The most innovative modelling'' by the judge panel; 4) 2nd place in Explorer Challenge 2019 (over 1k entries from 62 countries); 5) 1st place at SAIC Volkswagen Logistics Innovation Day for Smart Manufacturing 2019.

My Research
Career
Publications
Grants and Funding
Teaching
Supervision
Contact

We try to understand and causally influence the underlying distributions and processes to help and serve humanity.

Our world is undergoing inevitable and tumultuous changes. Causality, operating beneath the veneer of cause and effect, is essentially the way of change. Our causal AI methods can identify the root causes, discover latent variables, build immunity from spurious correlations, improve generalistion to diverse domains and distribution shifts, model the consequence of interventions, and answer What-If counterfactual questions. More importantly, causal AI holds the key to answer the reverse question: What is the ideal sequence of interventions, given resources or budgets, to optimise future outcomes?

Appointments

Date	Position	Institution name
2020 - ongoing	Professor	University of Adelaide
2018 - 2019	Associate Professor	University of Adelaide
2015 - 2017	Senior Lecturer	University of Adelaide
2012 - 2014	ARC DECRA fellow	University of Adelaide
2010 - 2011	Senior Research Associate	University of Adelaide

Awards and Achievements

Date Type Title Institution Name Country Amount

2012 Award ARC Discovery Early Career Researcher Award ARC Australia -
Language Competencies

Language Competency

Chinese (Mandarin) Can read, write, speak, understand spoken and peer review

English Can read, write, speak, understand spoken and peer review

Date	Type	Title	Institution Name	Country	Amount
2012	Award	ARC Discovery Early Career Researcher Award	ARC	Australia	-

Language	Competency
Chinese (Mandarin)	Can read, write, speak, understand spoken and peer review
English	Can read, write, speak, understand spoken and peer review

Education

Date	Institution name	Country	Title
2006 - 2010	Australian National University, Canberra	Australia	PhD
2003 - 2006	Northwestern Polytechnical University, Xi'an	China	Master
1999 - 2003	Northwestern Polytechnical University, Xi'an	China	Bachelor

Research Interests

Artificial Intelligence Computer Vision Knowledge Representation and Machine Learning

Journals

Year	Citation
2025	Ghiasi, A., Zhang, Z., Zeng, Z., Ng, C. T., Sheikh, A. H., & Shi, J. Q. (2025). Generalization of anomaly detection in bridge structures using a vibration-based Siamese convolutional neural network. COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING, 40(18), 18 pages. DOI Scopus3 WoS3
2025	Tan, Z., Li, X., Bai, R., Guo, C., Han, X., Shi, J. Q., . . . Li, H. (2025). AI for Complex Catalytic Systems: High-Entropy Alloys in Electrocatalytic Acetylene Semihydrogenation. ACS Catalysis, 15(15), 13097-13106. DOI
2025	Yang, G., Qiao, Y., Deng, H., Shi, J. Q., & Song, H. (2025). One-stage keypoint detection network for end-to-end cow body measurement. Engineering Applications of Artificial Intelligence, 146, 12 pages. DOI Scopus1
2025	Jin, S., Li, X., Yang, G., Zhang, Z., Shi, J. Q., Liu, Y., & Zhao, C. -X. (2025). Active Learning-Based Prediction of Drug Combination Efficacy. ACS Nano, 19(18), 17929-17940. DOI
2024	Cao, H., Zou, J., Liu, Y., Zhang, Z., Abbasnejad, E., Hengel, A. V. D., & Shi, J. Q. (2024). InvariantStock: Learning Invariant Features for Mastering the Shifting Market. Transactions on Machine Learning Research, 2024.
2024	Yin, Z., Zhang, Z., Gong, D., Albrecht, S. V., & Shi, J. Q. (2024). Highway Graph to Accelerate Reinforcement Learning. Transactions on Machine Learning Research, 2024.
2024	Cheng, H., Zhang, M., & Shi, J. Q. (2024). A Survey on Deep Neural Network Pruning: Taxonomy, Comparison, Analysis, and Recommendations. IEEE Transactions on Pattern Analysis and Machine Intelligence, 46(12), 10558-10578. DOI Scopus102 WoS70 Europe PMC10
2024	Tan, Z., Li, X., Zhao, Y., Zhang, Z., Shi, J. Q., & Li, H. (2024). Machine Learning‐Driven Selection of Two‐Dimensional Carbon‐Based Supports for Dual‐Atom Catalysts in CO2 Electroreduction. ChemCatChem, 16(22), e202400470-1-e202400470-8. DOI Scopus4 WoS7
2024	Cheng, H., Zhang, M., & Shi, J. Q. (2024). Influence Function Based Second-Order Channel Pruning: Evaluating True Loss Changes For Pruning Is Possible Without Retraining. IEEE Transactions on Pattern Analysis and Machine Intelligence, 46(12), 9023-9037. DOI Scopus2 WoS1
2024	Li, H., Li, X., Wang, P., Zhang, Z., Davey, K., Shi, J. Q., & Qiao, S. -Z. (2024). Machine Learning Big Data Set Analysis Reveals C-C Electro-Coupling Mechanism. Journal of the American Chemical Society, 146(32), 22850-22858. DOI Scopus26 WoS27 Europe PMC4
2024	Zhang, X., Zhang, Z., Chinnici, A., Sun, Z., Shi, J. Q., Nathan, G. J., & Chin, R. C. (2024). Physics-informed data-driven unsteady Reynolds-averaged Navier-Stokes turbulence modeling for particle-laden jet flows. Physics of Fluids, 36(5), 23 pages. DOI Scopus1 WoS1
2024	Jin, S., Lan, Z., Yang, G., Li, X., Shi, J. Q., Liu, Y., & Zhao, C. (2024). Computationally guided design and synthesis of dual‐drug loaded polymeric nanoparticles for combination therapy. Aggregate, 5(5), e606-1-e606-10. DOI Scopus7 WoS7
2023	Zhang, Z., Dupty, M. H., Wu, F., Shi, J. Q., & Lee, W. S. (2023). Factor Graph Neural Networks. Journal of Machine Learning Research, 24. Scopus3
2023	Yan, Q., Liu, S., Xu, S., Dong, C., Li, Z., Shi, J. Q., . . . Dai, D. (2023). 3D Medical image segmentation using parallel transformers. Pattern Recognition, 138, 1-14. DOI Scopus65
2023	Yan, Q., Gong, D., Wang, P., Zhang, Z., Zhang, Y., & Shi, J. Q. (2023). SharpFormer: Learning Local Feature Preserving Global Representations for Image Deblurring. IEEE Transactions on Image Processing, 32, 2857-2866. DOI Scopus22 WoS17 Europe PMC1
2023	Meng, J., Wang, Z., Ying, K., Zhang, J., Guo, D., Zhang, Z., . . . Chen, S. (2023). Human Interaction Understanding with Consistency-Aware Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(10), 11898-11914. DOI Scopus5 WoS4 Europe PMC2
2023	Zhao, Y., Li, H., Shan, J., Zhang, Z., Li, X., Shi, J. Q., . . . Li, H. (2023). Machine Learning Confirms the Formation Mechanism of a Single-Atom Catalyst via Infrared Spectroscopic Analysis. Journal of Physical Chemistry Letters, 14(49), 11058-11062. DOI Scopus6 WoS7
2023	Damirchi, H., Opazo, C. R., Abbasnejad, E., Teney, D., Shi, J. Q., Gould, S., & Hengel, A. V. D. (2023). Zero-shot Retrieval: Augmenting Pre-trained Models with Search Engines.. CoRR, abs/2311.17949.
2023	Li, X., Li, H., Zhang, Z., Shi, J. Q., Jiao, Y., & Qiao, S. -Z. (2023). Active-learning accelerated computational screening of A₂B@NG catalysts for CO₂ electrochemical reduction. Nano Energy, 115, 108695-1-108695-9. DOI Scopus8 WoS7
2023	Yan, Q., Fruzangohar, M., Taylor, J., Gong, D., Walter, J., Norman, A., . . . Coram, T. (2023). Improved genomic prediction using machine learning with Variational Bayesian sparsity. Plant Methods, 19(1), 1-14. DOI Scopus4 WoS3 Europe PMC1
2023	Li, X., Shi, J. Q., & Page, A. J. (2023). Discovery of Graphene Growth Alloy Catalysts Using High-Throughput Machine Learning. Nano Letters, 23(21), 9796-9802. DOI Scopus3 WoS3 Europe PMC1
2023	Yuwono, J. A., Li, X., Doležal, T. D., Samin, A. J., Shi, J. Q., Li, Z., & Birbilis, N. (2023). A computational approach for mapping electrochemical activity of multi-principal element alloys. npj Materials Degradation, 7(1), 87-1-87-11. DOI Scopus4 WoS3
2022	Ghiasi, A., Moghaddam, M. K., Ng, C. T., Sheikh, A. H., & Shi, J. Q. (2022). Damage classification of in-service steel railway bridges using a novel vibration-based convolutional neural network. Engineering Structures, 264, 114474-1-114474-16. DOI Scopus43 WoS35
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	Yan, Q., Gong, D., Shi, J. Q., den Hengel, A. V., Sun, J., Zhu, Y., & Zhang, Y. (2022). High dynamic range imaging via gradient-aware context aggregation network. Pattern Recognition, 122, 16 pages. DOI Scopus28 WoS27
2022	Sun, W., Gong, D., Shi, J. Q., van den Hengel, A., & Zhang, Y. (2022). Video super-resolution via mixed spatial-temporal convolution and selective fusion. Pattern Recognition, 126, 1-14. DOI Scopus13 WoS12
2022	Parvaneh, A., Abbasnejad, E., Wu, Q., Shi, Q., & Van Den Hengel, A. (2022). Show, price and negotiate: a negotiator with online value look-ahead. IEEE Transactions on Multimedia, 24, 1426-1434. DOI Scopus2 WoS1
2021	Abedin, A., Ehsanpour, M., Shi, Q., Rezatofighi, H., & Ranasinghe, D. C. (2021). Attend And Discriminate: Beyond the State-of-the-Art for Human Activity Recognition using Wearable Sensors. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 5(1), 1-22. DOI Scopus79 WoS72
2021	Neshat, M., Nezhad, M. M., Abbasnejad, E., Mirjalili, S., Groppi, D., Heydari, A., . . . Wagner, M. (2021). Wind turbine power output prediction using a new hybrid neuro-evolutionary method. Energy, 229, 120617-1-120617-24. DOI Scopus97 WoS84
2021	Yan, Q., Gong, D., Shi, J. Q., van den Hengel, A., Shen, C., Reid, I., & Zhang, Y. (2021). Dual-attention-guided network for ghost-free high dynamic range imaging. International Journal of Computer Vision, 130(1), 19 pages. DOI Scopus43 WoS34
2021	Wang, Y., Gong, D., Yang, J., Shi, Q., Hengel, A. V. D., Xie, D., & Zeng, B. (2021). Deep Single Image Deraining via Modeling Haze-Like Effect. IEEE Transactions on Multimedia, 23, 2481-2492. DOI Scopus21 WoS16
2021	Yan, Q., Wang, B., Zhang, W., Luo, C., Xu, W., Xu, Z., . . . You, Z. (2021). An attention-guided deep neural network with multi-scale feature fusion for liver vessel segmentation. IEEE Journal of Biomedical and Health Informatics, 25(7), 2629-2642. DOI Scopus103 WoS88 Europe PMC36
2021	Yan, Q., Wang, B., Zhang, L., Zhang, J., You, Z., Shi, Q., & Zhang, Y. (2021). Towards accurate HDR imaging with learning generator constraints. Neurocomputing, 428, 79-91. DOI Scopus13 WoS13
2021	Sun, W., Gong, D., Shi, Q., van den Hengel, A., & Zhang, Y. (2021). Learning to zoom-in via learning to zoom-out: real-world super-resolution by generating and adapting degradation. IEEE Transactions on Image Processing, 30, 1-16. DOI Scopus30 WoS23 Europe PMC1
2021	Yan, Q., Wang, B., Gong, D., Luo, C., Zhao, W., Shen, J., . . . You, Z. (2021). COVID-19 chest CT image segmentation network by multi-scale fusion and enhancement operations. IEEE Transactions on Big Data, 7(1), 13-24. DOI Scopus89 WoS62 Europe PMC34
2021	Rezatofighi, H., Kaskman, R., Taghizadeh Motlagh, S. F., Shi, Q., Milan, A., Cremers, D., . . . Reid, I. D. (2021). Learn to Predict Sets Using Feed-Forward Neural Networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(12), 1-15. DOI Scopus7 WoS4
2020	Yan, Q., Zhang, L., Liu, Y., Zhu, Y., Sun, J., Shi, Q., & Zhang, Y. (2020). Deep HDR imaging via A non-local network. IEEE Transactions on Image Processing, 29, 4308-4322. DOI Scopus210 WoS174 Europe PMC17
2020	Gong, D., Zhang, Z., Shi, Q., van den Hengel, A., Shen, C., & Zhang, Y. (2020). Learning deep gradient descent optimization for image deconvolution. IEEE Transactions on Neural Networks and Learning Systems, 31(12), 5468-5482. DOI Scopus92 WoS77 Europe PMC7
2020	Yan, Y., Tan, M., Tsang, I., Yang, Y., Shi, Q., & Zhang, C. (2020). Fast and Low Memory Cost Matrix Factorization: Algorithm, Analysis and Case Study. IEEE Transactions on Knowledge and Data Engineering, 32(2), 288-301. DOI Scopus4 WoS4
2020	Zhang, L., Wei, W., Shi, Q., Shen, C., van den Hengel, A., & Zhang, Y. (2020). Accurate tensor completion via adaptive low-rank representation. IEEE Transactions on Neural Networks and Learning Systems, 31(1), 4170-4184. DOI Scopus20 WoS17 Europe PMC1
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	Yan, Q., Wang, B., Li, P., Li, X., Zhang, A., Shi, Q., . . . Zhang, Y. (2020). Ghost removal via channel attention in exposure fusion. Computer Vision and Image Understanding, 201, 1-8. DOI Scopus26 WoS23
2020	Dendorfer, P., Rezatofighi, H. S., Milan, A., Shi, Q. J., Cremers, D., Reid, I. D., . . . Leal-Taixé, L. (2020). MOT20: A benchmark for multi object tracking in crowded scenes. CoRR, abs/2003.09003, 7 pages.
2020	Guo, Y., Chen, J., Du, Q., Van Den Hengel, A., Shi, Q., & Tan, M. (2020). Multi-way backpropagation for training compact deep neural networks.. Neural Netw, 126, 250-261. DOI Scopus25 WoS19 Europe PMC6
2020	Liu, C., Yao, R., Rezatofighi, S. H., Reid, I., & Shi, Q. (2020). Model-Free Tracker for Multiple Objects Using Joint Appearance and Motion Inference. IEEE Transactions on Image Processing, 29, 277-288. DOI Scopus15 WoS13 Europe PMC2
2020	Yan, Q., Wang, B., Gong, D., Luo, C., Zhao, W., Shen, J., . . . You, Z. (2020). COVID-19 Chest CT Image Segmentation -- A Deep Convolutional Neural Network Solution.
2019	Wang, Y., Gong, D., Yang, J., Shi, Q., Hengel, A. V. D., Xie, D., & Zeng, B. (2019). An Effective Two-Branch Model-Based Deep Network for Single Image Deraining.
2019	Wang, Y., Shi, Q., Abbasnejad, E., Ma, C., Ma, X., & Zeng, B. (2019). Deep Single Image Deraining Via Estimating Transmission and Atmospheric Light in rainy Scenes.
2019	Kang, L., Liu, J., Liu, L., Shi, Q., & Ye, D. (2019). Creating Auxiliary Representations from Charge Definitions for Criminal Charge Prediction.
2019	Wang, Y., Zhang, H., Liu, Y., Shi, Q., & Zeng, B. (2019). Gradient Information Guided Deraining with A Novel Network and Adversarial Training.
2019	Guo, Y., Chen, Q., Chen, J., Wu, Q., Shi, Q., & Tan, M. (2019). Auto-Embedding Generative Adversarial Networks for High Resolution Image Synthesis. IEEE Transactions on Multimedia, 21(11), 2726-2737. DOI Scopus63 WoS52
2019	Neshat, M., Abbasnejad, E., Shi, Q., Alexander, B., & Wagner, M. (2019). Adaptive Neuro-Surrogate-Based Optimisation Method for Wave Energy Converters Placement Optimisation.. CoRR, abs/1907.03076.
2019	Liu, Y., Liu, L., Rezatofighi, H., Do, T. -T., Shi, Q., & Reid, I. (2019). Learning Pairwise Relationship for Multi-object Detection in Crowded Scenes.
2019	Liu, W., Gong, D., Tan, M., Shi, Q., Yang, Y., & Hauptmann, A. G. (2019). Learning Distilled Graph for Large-scale Social Network Data Clustering. IEEE Transactions on Knowledge and Data Engineering, 32(7), 1393-1404. DOI Scopus6 WoS3
2019	Dendorfer, P., Rezatofighi, H., Milan, A., Shi, J., Cremers, D., Reid, I., . . . Leal-Taixe, L. (2019). CVPR19 Tracking and Detection Challenge: How crowded can it get?.
2019	Zhang, L., Wei, W., Shi, Q., Shen, C., van den Hengel, A., & Zhang, Y. (2019). Accurate imagery recovery using a multi-observation patch model. Information Sciences, 501, 724-741. DOI Scopus1
2019	Yao, R., Lin, G., Shen, C., Zhang, Y., & Shi, Q. (2019). Semantics-Aware Visual Object Tracking. IEEE Transactions on Circuits and Systems for Video Technology, 29(6), 1687-1700. DOI Scopus34 WoS32
2019	Gong, D., Tan, M., Shi, Q., van den Hengel, A., & Zhang, Y. (2019). MPTV: matching pursuit based total variation minimization for image deconvolution. IEEE Transactions on Image Processing, 28(4), 1851-1865. DOI Scopus21 WoS17 Europe PMC5
2019	Suwanwimolkul, S., Zhang, L., Gong, D., Zhang, Z., Chen, C., Ranasinghe, D. C., & Qinfeng Shi, J. (2019). An adaptive markov random field for structured compressive sensing. IEEE Transactions on Image Processing, 28(3), 1556-1570. DOI Scopus9 WoS7
2019	Suwanwimolkul, S., Zhang, L., Ranasinghe, D. C., & Shi, Q. (2019). One-step adaptive Markov random field for structured compressive sensing. Signal Processing, 156, 116-144. DOI Scopus5 WoS3
2018	Yao, R., Lin, G., Shi, Q., & Ranasinghe, D. C. (2018). Efficient dense labelling of human activity sequences from wearables using fully convolutional networks. Pattern Recognition, 78, 252-266. DOI Scopus89 WoS77
2018	Zhang, L., Wei, W., Zhang, Y., Shen, C., van den Hengel, A., & Shi, Q. (2018). Cluster sparsity field: an internal hyperspectral imagery prior for reconstruction. International Journal of Computer Vision, 126(8), 797-821. DOI Scopus71 WoS88
2018	Rezatofighi, S. H., Kaskman, R., Motlagh, F. T., Shi, Q., Cremers, D., Leal-Taixé, L., & Reid, I. (2018). Deep Perm-Set Net: Learn to predict sets with unknown permutation and cardinality using deep neural networks.
2017	Zhang, L., Wei, W., Shi, Q., Shen, C., Hengel, A. V. D., & Zhang, Y. (2017). Beyond Low Rank: A Data-Adaptive Tensor Completion Method.
2017	Shinmoto Torres, R., Shi, Q., van den Hengel, A., & Ranasinghe, D. (2017). A hierarchical model for recognizing alarming states in a batteryless sensor alarm intervention for preventing falls in older people. Pervasive and Mobile Computing, 40, 1-16. DOI Scopus13 WoS11
2017	Yao, R., Shi, Q., Shen, C., Zhang, Y., & Van Den Hengel, A. (2017). Part-based robust tracking using online latent structured learning. IEEE Transactions on Circuits and Systems for Video Technology, 27(6), 1235-1248. DOI Scopus18 WoS17
2016	Zhang, L., Wei, W., Zhang, Y., Shen, C., Van Den Hengel, A., & Shi, Q. (2016). Dictionary learning for promoting structured sparsity in hyperspectral compressive sensing. IEEE Transactions on Geoscience and Remote Sensing, 54(12), 7223-7235. DOI Scopus51 WoS44
2016	Torres, R. L. S., Ranasinghe, D. C., Shi, Q., & Hengel, A. V. D. (2016). Learning from Imbalanced Multiclass Sequential Data Streams Using Dynamically Weighted Conditional Random Fields.
2016	Guo, Y., Chen, J., Du, Q., Hengel, A. V. D., Shi, Q., & Tan, M. (2016). The Shallow End: Empowering Shallower Deep-Convolutional Networks through Auxiliary Outputs.
2015	Tan, M., Xiao, S., Gao, J., Xu, D., Hengel, A. V. D., & Shi, Q. (2015). Scalable Nuclear-norm Minimization by Subspace Pursuit Proximal Riemannian Gradient.
2015	Shi, Q., Reid, M., Caetano, T., Van Den Hengel, A., & Wang, Z. (2015). A hybrid loss for multiclass and structured prediction. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37(1), 2-12. DOI Scopus3 WoS2
2015	Li, H., Shen, C., Van Den Hengel, A., & Shi, Q. (2015). Worst case linear discriminant analysis as scalable semidefinite feasibility problems. IEEE Transactions on Image Processing, 24(8), 2382-2392. DOI Scopus10 WoS9 Europe PMC1
2015	Shen, F., Shen, C., Shi, Q., Van Den Hengel, A., Tang, Z., & Shen, H. (2015). Hashing on nonlinear manifolds. IEEE Transactions on Image Processing, 24(6), 1839-1851. DOI Scopus146 WoS134 Europe PMC22
2015	Yin, M., Gao, J., Lin, Z., Shi, Q., & Guo, Y. (2015). Dual graph regularized latent low-rank representation for subspace clustering. IEEE Transactions on Image Processing, 24(12), 4918-4933. DOI Scopus124 WoS117 Europe PMC13
2014	Paisitkriangkrai, S., Shen, C., Shi, Q., & van den Hengel, A. (2014). RandomBoost: simplified multiclass boosting through randomization. IEEE Transactions on Neural Networks and Learning Systems, 25(4), 764-779. DOI Scopus6 WoS7 Europe PMC1
2013	Zhang, Z., Shi, Q., Zhang, Y., Shen, C., & Hengel, A. V. D. (2013). Constraint Reduction using Marginal Polytope Diagrams for MAP LP Relaxations.
2012	Gao, J., Shi, Q., & Caetano, T. (2012). Dimensionality reduction via compressive sensing. Pattern Recognition Letters, 33(9), 1163-1170. DOI Scopus30 WoS26
2011	Shi, Q., Li, C., Wang, L., & Smola, A. (2011). Human action segmentation and recognition using discriminative semi-Markov models. International Journal of Computer Vision, 93(1), 22-32. DOI Scopus114 WoS84
2010	Li, H., Shen, C., & Shi, Q. (2010). Real-time Visual Tracking Using Sparse Representation.
2010	Shi, Q., Reid, M. D., & Caetano, T. (2010). Conditional Random Fields and Support Vector Machines: A Hybrid Approach.
2009	Shi, Q., Petterson, J., Dror, G., Langford, J., Smola, A., & Vishwanathan, S. (2009). Hash Kernels for Structured Data. Journal of Machine Learning Research (Print), 10, 2615-2637. Scopus154 WoS103
2006	Li, Y., Shi, Q. F., Zhang, Y. N., & Zhao, R. C. (2006). Automatic segmentation for synthetic aperture radar images. Dianzi Yu Xinxi Xuebao/Journal of Electronics and Information Technology, 28(5), 932-935. Scopus3

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
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	Usmani, E., Bacchi, S., Zhang, H., Guymer, C., Kraczkowska, A., Qinfeng Shi, J., . . . Chan, W. O. (2024). Prediction of vitreomacular traction syndrome outcomes with deep learning: A pilot study. In European Journal of Ophthalmology Vol. 51 (pp. 937). WILEY. DOI Scopus1
2024	Mohammadi, B., Hong, Y., Qi, Y., Wu, Q., Pan, S., & Shi, J. Q. (2024). Augmented Commonsense Knowledge for Remote Object Grounding. In Proceedings of the AAAI Conference on Artificial Intelligence Vol. 38 (pp. 4269-4277). Online: Association for the Advancement of Artificial Intelligence (AAAI). DOI Scopus10 WoS6
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	Liu, Y., Zhang, Z., Gong, D., Gong, M., Huang, B., van den Hengel, A., . . . Shi, J. Q. (2024). IDENTIFIABLE LATENT POLYNOMIAL CAUSAL MODELS THROUGH THE LENS OF CHANGE. In 12th International Conference on Learning Representations, ICLR 2024. Online: ICLR. Scopus6
2024	Cai, Y., Liu, Y., Zhang, Z., & Shi, J. Q. (2024). CLAP: Isolating Content from Style Through Contrastive Learning with Augmented Prompts. In Lecture Notes in computer science Vol. 15079 (pp. 130-147). Milan, Italy: Springer Nature Switzerland. 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	Jabri, M. K., Papadakis, P., Abbasnejad, E., Coppin, G., & Shi, J. (2023). Improving Reward Estimation in Goal-Conditioned Imitation Learning with Counterfactual Data and Structural Causal Models. In Proceedings of the International Conference on Informatics in Control, Automation and Robotics Vol. 2 (pp. 329-337). Online: SCITEPRESS - Science and Technology Publications. DOI
2022	Zhang, Z., Ng, I., Gong, D., Liu, Y., Abbasnejad, E. M., Gong, M., . . . Shi, J. Q. (2022). Truncated Matrix Power Iteration for Differentiable DAG Learning. In Advances in Neural Information Processing Systems Vol. 35 (pp. 13 pages). Online: Neural information processing systems foundation. Scopus15
2022	Parvaneh, A., Abbasnejad, E., Teney, D., Haffari, R., Hengel, A. V. D., & Shi, Q. J. (2022). Active Learning by Feature Mixing.. In CoRR Vol. abs/2203.07034.
2022	Zhang, X., Zhang, Z., Chinnici, A., Sun, Z., Shi, J., Nathan, G., & Chin, R. (2022). Physics-informed data-driven RANS turbulence modelling for single-phase and particle-laden jet flows. In Proceedings of the 23nd Australasian Fluid Mechanics Conference. Sydney.
2022	Kazemi Moghaddam, M., Abbasnejad, E., Wu, Q., Qinfeng Shi, J., & Van Den Hengel, A. (2022). ForeSI: Success-Aware Visual Navigation Agent. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV 2022) (pp. 3401-3410). Online: IEEE. DOI Scopus10 WoS9
2022	Parvaneh, A., Abbasnejad, E., Teney, D., Haffari, R., Van Den Hengel, A., & Shi, J. Q. (2022). Active Learning by Feature Mixing. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Vol. 2022-June (pp. 12227-12236). New Orleans, LA, USA: IEEE. DOI Scopus104 WoS77
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). Scopus6
2022	Yan, Q., Zhang, S., Chen, W., Liu, Y., Zhang, Z., Zhang, Y., . . . Gong, D. (2022). A Lightweight Network for High Dynamic Range Imaging. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Vol. 2022-June (pp. 823-831). Online: IEEE. DOI Scopus11 WoS8
2022	Perez-Pellitero, E., Catley-Chandar, S., Shaw, R., Leonardis, A., Timofte, R., Zhang, Z., . . . Park, C. Y. (2022). NTIRE 2022 Challenge on High Dynamic Range Imaging: Methods and Results. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Vol. 2022-June (pp. 1008-1022). Online: IEEE. DOI Scopus32 WoS15
2022	Yan, Q., Gong, D., Liu, Y., Van Den Hengel, A., & Shi, J. Q. (2022). Learning Bayesian Sparse Networks with Full Experience Replay for Continual Learning. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Vol. 2022-June (pp. 109-118). Online: IEEE. DOI Scopus47 WoS32
2022	Zhang, X., Li, D., Wang, Z., Wang, J., Ding, E., Shi, J. Q., . . . Wang, J. (2022). Implicit Sample Extension for Unsupervised Person Re-Identification. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Vol. 2022-June (pp. 7359-7368). Online: IEEE. DOI Scopus125 WoS99
2022	Doan, B. G., Abbasnejad, E., Shi, J. Q., & Ranashinghe, D. C. (2022). Bayesian Learning with Information Gain Provably Bounds Risk for a Robust Adversarial Defense. In INTERNATIONAL CONFERENCE ON MACHINE LEARNING, VOL 162 Vol. 162 (pp. 15 pages). Baltimore, MD: JMLR-JOURNAL MACHINE LEARNING RESEARCH. Scopus2 WoS3
2021	Gong, D., Zhang, Z., Shi, J. Q., & van den Hengel, A. (2021). Memory-augmented Dynamic Neural Relational Inference. In Proceedings 2021 IEEE/CVF International Conference on Computer Vision ICCV 2021 (pp. 11823-11832). Los Alamitos, CA, USA: IEEE. DOI Scopus11 WoS9
2021	Wang, Z., Meng, J., Guo, D., Zhang, J., Shi, J. Q., & Chen, S. (2021). Consistency-Aware Graph Network for Human Interaction Understanding. In Proceedings of the IEEE International Conference on Computer Vision (pp. 13349-13358). online: IEEE. DOI Scopus11 WoS6
2021	Kazemi Moghaddam, M., Wu, Q., Abbasnejad, E., & Shi, J. (2021). Optimistic Agent: Accurate Graph-Based Value Estimation for More Successful Visual Navigation. In Proceedings of the IEEE Winter Conference on Applications of Computer Vision (WACV 2021) (pp. 3732-3741). online: IEEE. DOI Scopus15 WoS13
2021	Yan, Q., Wang, B., Gong, D., Zhang, D., Yang, Y., You, Z., . . . Shi, J. Q. (2021). A Comprehensive CT Dataset for Liver Computer Assisted Diagnosis. In 32nd British Machine Vision Conference, BMVC 2021 (pp. 1-13). Online: BMVC. Scopus2
2020	Ehsanpour, M., Abedin, A., Saleh, F., Shi, J., Reid, I., & Rezatofighi, H. (2020). Joint Learning of Social Groups, Individuals Action and Sub-group Activities in Videos. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 12354 LNCS (pp. 177-195). Cham, Switzerland: Springer International Publishing. DOI Scopus62
2020	Abedin Varamin, A., Taghizadeh Motlagh, S. F., Shi, Q., Rezatofighi, H., & Ranasinghe, D. (2020). Towards deep clustering of human activities from wearables. In Proceedings - International Symposium on Wearable Computers, ISWC (pp. 1-6). New York, NY, United States: Association for Computing Machinery (ACM). DOI Scopus21 WoS14
2020	Abbasnejad, M., Teney, D., Parvaneh, A., Shi, Q., & Van Den Hengel, A. (2020). Counterfactual Vision and Language Learning.. In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 10041-10051). online: IEEE. DOI Scopus117 WoS86
2020	Abbasnejad, M., Abbasnejad, I., Wu, Q., Shi, Q., & Van Den Hengel, A. (2020). Gold seeker: Information gain from policy distributions for goal-oriented vision-and-langauge reasoning. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 13447-13456). online: IEEE. DOI Scopus4 WoS1
2020	Ehsanpour, M., Abedin Varamin, A., Saleh, F., Shi, Q., Reid, I. D., & Rezatofighi, H. (2020). Joint learning of social groups, individuals action and sub-group activities in videos. In A. Vedaldi, H. Bischof, T. Brox, & J. -M. Frahm (Eds.), Proceedings of the 16th European Conference on Computer Vision Workshops (ECCV 2020), as published in Lecture Notes in Computer Science Vol. 12354 (pp. 177-195). Cham, Switzerland: Springer. DOI
2020	Parvaneh, A., Abbasnejad, M., Teney, D., Shi, Q., & Van Den Hengel, A. (2020). Counterfactual Vision-and-Language Navigation: Unravelling the Unseen.. In H. Larochelle, M. Ranzato, R. Hadsell, M. -F. Balcan, & H. -T. Lin (Eds.), NeurIPS Vol. 2020-December (pp. 1-12). virtual online: NIPS. Scopus30
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	Xu, C., Shi, H., Gao, Y., Zhou, L., Shi, Q., & Li, J. (2019). Space-Based optical imaging dynamic simulation for spatial target. In Proceedings of SPIE - The International Society for Optical Engineering Vol. 11338 (pp. 1-6). online: SPIE. DOI Scopus3
2019	Neshat, M., Abbasnejad, E., Shi, Q., Alexander, B., & Wagner, M. (2019). Adaptive neuro-surrogate-based optimisation method for wave energy converters placement optimisation. In T. Gedeon, K. W. Wong, & M. Lee (Eds.), Proceedings of the 26th International Conference on Neural Information Processing (ICONIP 2019), as published in Lecture Notes in Computer Science (Neural Information Processing Proceedings, Part II) Vol. 11954 (pp. 353-366). Switzerland: Springer Nature. DOI Scopus26 WoS21
2019	Moghaddam, M. M. K., Abbasnejad, E., & Shi, J. (2019). Follow the Attention: Combining Partial Pose and Object Motion for Fine-Grained Action Detection.
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	Wang, Z., Liu, T., Shi, Q., Kumar, M. P., & Zhang, J. (2019). New convex relaxations for MRF inference with unknown graphs. In Proceedings: 2019 International Conference on Computer Vision (pp. 9934-9942). Los Alamitos, California: IEEE. DOI Scopus6 WoS4
2019	Yan, Q., Gong, D., Zhang, P., Shi, Q., Sun, J., Reid, I., & Zhang, Y. (2019). Multi-scale dense networks for deep high dynamic range imaging. In Proceedings of the 2019 IEEE Winter Conference on Applications of Computer Vision (pp. 41-50). Waikoloa Village, HI, USA: IEEE. DOI Scopus79 WoS72
2019	Abedin Varamin, A., Rezatofighi, H., Shi, Q., & Ranasinghe, D. (2019). SparseSense: Human Activity Recognition from Highly Sparse Sensor Data-streams Using Set-based Neural Networks. In IJCAI International Joint Conference on Artificial Intelligence Vol. 2019-August (pp. 5780-5786). online: IJCAI Organization. DOI Scopus11 WoS12
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	Abbasnejad, E., Wu, Q., Shi, Q., & Van Den Hengel, A. (2019). What's to know? uncertainty as a guide to asking goal-oriented questions. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Vol. 2019-June (pp. 4150-4159). online: IEEE. DOI Scopus18 WoS10
2019	Yan, Q., Gong, D., Shi, Q., Van Den Hengel, A., Shen, C., Reid, I., & Zhang, Y. (2019). Attention-guided network for ghost-free high dynamic range imaging. In Proceedings: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Vol. 2019-June (pp. 1751-1760). online: IEEE. DOI Scopus288 WoS234
2019	Li, J., Liu, Y., Gong, D., Shi, Q., Yuan, X., Zhao, C., & Reid, I. (2019). RGBD based dimensional decomposition residual network for 3D semantic scene completion. In Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2019) Vol. 2019-June (pp. 7685-7694). online: Computer Vision Foundation / IEEE. DOI Scopus74 WoS55
2019	Liu, Y., Dong, W., Zhang, L., Gong, D., & Shi, Q. (2019). Variational bayesian dropout with a hierarchical prior. In Proceedings: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Vol. 2019-June (pp. 7117-7126). online: IEEE. DOI Scopus20 WoS18
2018	Rezatofighi, H., Milan, A., Shi, Q., Dick, A., & Reid, I. (2018). Joint learning of set cardinality and state distribution. In 32nd AAAI Conference on Artificial Intelligence, AAAI 2018 (pp. 3968-3975). online: AAAI. Scopus9 WoS5
2018	Abbasnejad, M. E., Dick, A. R., Shi, Q., & Hengel, A. V. D. (2018). Active learning from noisy tagged images. In Proceedings of BMVC 2018 and Workshops (pp. 1-13). Newcastle upon Tyne: BMVA Press.
2018	Liu, Y., Dong, W., Gong, D., Zhang, L., & Shi, Q. (2018). Deblurring natural image using super-gaussian fields. In Proceedings of the 15th European Conference on Computer Vision as published in Lecture Notes in Computer Science Vol. 11205 LNCS (pp. 467-484). Switzerland: Springer Nature. DOI Scopus9 WoS19
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 Scopus22 WoS88
2018	Abedin Varamin, A., Abbasnejad, E., Shi, Q., Ranasinghe, D., & Rezatofighi, H. (2018). Deep Auto-Set: A Deep Auto-Encoder-Set Network for Activity Recognition Using Wearables. In MobiQuitous (pp. 1-8). online: ACM. DOI Scopus39 WoS33
2018	Ehsan Abbasnejad, M., Dick, A., Shi, Q., & Van Den Hengel, A. (2018). Active learning from noisy tagged images. In British Machine Vision Conference 2018 Bmvc 2018.
2017	Xu, C., Shi, N., Zhou, L., Shi, Q., Yang, Y., & Li, Z. (2017). Defect analysis and detection of micro nano structured optical thin film. In Proceedings of SPIE - The International Society for Optical Engineering Vol. 10460 (pp. 7 pages). Beijing, China: SPIE - International Society for Optics and Photonics. DOI
2017	Liu, C., Yao, R., Rezatofighi, S., Reid, I., & Shi, Q. (2017). Multi-object model-free tracking with joint appearance and motion inference. In Y. Guo, H. Li, W. Cai, M. Murshed, Z. Wang, J. Gao, & D. Feng (Eds.), Proceedings of the International Conference on Digital Image Computing: Techniques and Applications (DICTA 2017) Vol. 2017-December (pp. 1-8). Piscataway, NJ: IEEE. DOI Scopus5
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 Scopus344 WoS269
2017	Gong, D., Tan, M., Zhang, Y., Van Den Hengel, A., & Shi, Q. (2017). MPGL: An efficient matching pursuit method for generalized LASSO. In Proceedings of the 31st AAAI Conference on Artificial Intelligence, AAAI 2017 (pp. 1934-1940). San Francisco: AAAI. Scopus12 WoS8
2017	Zhang, Z., Shi, Q., McAuley, J., Wei, W., Zhang, Y., Yao, R., & Van Den Hengel, A. (2017). Solving constrained combinatorial optimization problems via MAP inference without high-order penalties. In Proceedings of the 31st AAAI Conference on Artificial Intelligence, AAAI 2017 (pp. 3804-3810). San Francisco: AAAI. Scopus1 WoS1
2017	Zhang, Z., McAuley, J., Li, Y., Wei, W., Zhang, Y., & Shi, Q. (2017). Dynamic programming bipartite belief propagation for hyper graph matching. In Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI 2017) Vol. 0 (pp. 4662-4668). online: AAAI Press. DOI Scopus7 WoS5
2017	Gong, D., Tan, M., Zhang, Y., Hengel, A., & Shi, Q. (2017). Self-paced kernel estimation for robust blind image deblurring. In Proceedings of the IEEE International Conference on Computer Vision (ICCV 2017) Vol. 2017 (pp. 1670-1679). Online: IEEE. DOI Scopus28 WoS28
2016	Zhang, Z., Shi, Q., McAuley, J., Wei, W., Zhang, Y., & Van Den Hengel, A. (2016). Pairwise matching through max-weight bipartite belief propagation. In Proceedings of the 29th IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPR 2016) Vol. 2016 (pp. 1202-1210). Las Vegas, NV: IEEE. DOI Scopus49 WoS30
2016	Gong, D., Tan, M., Zhang, Y., Van Den Hengel, A., & Shi, Q. (2016). Blind image deconvolution by automatic gradient activation. In Proceedings of the 29th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2016) Vol. 2016-December (pp. 1827-1836). Las Vegas, NV: IEEE. DOI Scopus76 WoS62
2016	Rezatofighi, S., Milan, A., Zhang, Z., Shi, Q., Dick, A., & Reid, I. (2016). Joint probabilistic matching using m-best solutions. In Proceedings of the 29th IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPR 2016) Vol. 2016-December (pp. 136-145). Las Vegas, NV: IEEE. DOI Scopus25 WoS20
2016	Tan, M., Xiao, S., Gao, J., Xu, D., Van Den Hengel, A., & Shi, Q. (2016). Proximal riemannian pursuit for large-scale trace-norm minimization. In Proceedings of the I29th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2016) Vol. 2016-December (pp. 5877-5886). Las Vegas, NV: IEEE. DOI Scopus1 WoS1
2016	Zhang, L., Wei, W., Zhang, Y., Shen, C., Van Den Hengel, A., & Shi, Q. (2016). Cluster sparsity field for hyperspectral imagery denoising. In B. Leibe, J. Matas, N. Sebe, & M. Welling (Eds.), Proceedings of the 14th European Conference on Computer Vision Vol. 9909 (pp. 631-647). Amsterdam, Netherlands: Springer International Publishing AG. DOI Scopus15 WoS13
2016	Zhang, W., Tan, M., Sheng, Q., Yao, L., & Shi, Q. (2016). Efficient orthogonal non-negative matrix factorization over stiefel manifold. In Proceedings of the 25th ACM International Conference on Information and Knowledge Management (CIKM '16) Vol. 24-28-October-2016 (pp. 1743-1752). Indianapolis, IN, USA: Association for Computing Machinery (ACM). DOI Scopus12 WoS6
2016	Tan, M., Yan, Y., Wang, L., Van Den Hengel, A., Tsang, I., & Shi, Q. (2016). Learning sparse confidence-weighted classifier on very high dimensional data. In Proceedings of the 30th AAAI Conference on Artificial Intelligence Vol. 3 (pp. 2080-2086). Phoenix, AZ: AAAI Press. Scopus4 WoS2
2015	Yan, Y., Tan, M., Tsang, I., Yang, Y., Zhang, C., & Shi, Q. (2015). Scalable maximum margin matrix factorization by active Riemannian subspace search. In Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence Vol. 2015-January (pp. 3988-3994). Buenos Aires, Argentina: AAAI Press. Scopus12 WoS10
2015	McAuley, J., Targett, C., Shi, Q., & Van Den Hengel, A. (2015). Image-based recommendations on styles and substitutes. In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 43-52). Santiago, Chile: Association for Computing Machinery. DOI Scopus1999 WoS1491
2015	Tan, M., Shi, Q., Van Den Hengel, A., Shen, C., Gao, J., Hu, F., & Zhang, Z. (2015). Learning graph structure for multi-label image classification via clique generation. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Vol. 07-12-June-2015 (pp. 4100-4109). Boston, MA: IEEE. DOI Scopus44 WoS35
2015	Rezatofighi, S., Milan, A., Zhang, Z., Shi, Q., Dick, A., & Reid, I. (2015). Joint Probabilistic Data Association Revisited. In Proceedings of the 2015 IEEE International Conference on Computer Vision Vol. 2015 International Conference on Computer Vision, ICCV 2015 (pp. 3047-3055). Santiago, CHILE: IEEE. DOI Scopus296 WoS223
2015	Zhang, L., Wei, W., Zhang, Y., Li, F., Shen, C., & Shi, Q. (2015). Hyperspectral Compressive Sensing Using Manifold-Structured Sparsity Prior. In Proceedings of the IEEE International Conference on Computer Vision (ICCV) Vol. 2015 International Conference on Computer Vision, ICCV 2015 (pp. 3550-3558). Santiago, Chile: IEEE. DOI Scopus16 WoS16
2014	Shinmoto Torres, R., Ranasinghe, D., & Shi, Q. (2014). Evaluation of wearable sensor tag data segmentation approaches for real time activity classification in elderly. In I. Stojmenovic, Z. Cheng, & S. Guo (Eds.), Mobile and Ubiquitous Systems: Computing, Networking, and Services Vol. 131 (pp. 384-395). Tokyo, Japan: SPRINGER INTERNATIONAL PUBLISHING AG. DOI Scopus12 WoS6
2014	Lin, G., Shen, C., Shi, Q., Van Den Hengel, A., & Suter, D. (2014). Fast supervised hashing with decision trees for high-dimensional data. In Proceedings of 2014 IEEE Conference on Computer Vision and Pattern Recognition (pp. 1971-1978). Columbus, Ohio: IEEE. DOI Scopus388 WoS275
2013	Shinmoto Torres, R., Ranasinghe, D., Shi, Q., & Sample, A. (2013). Sensor enabled wearable RFID technology for mitigating the risk of falls near beds. In Proceedings of the 2013 IEEE International Conference on RFID (pp. 191-198). United States: IEEE. DOI Scopus113 WoS87
2013	Yao, R., Shi, Q., Shen, C., Zhang, Y., & Van Den Hengel, A. (2013). Part-based visual tracking with online latent structural learning. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 2363-2370). United States of America: IEEE. DOI Scopus203 WoS160
2013	Wang, Z., Shi, Q., Shen, C., & Van Den Hengel, A. (2013). Bilinear programming for human activity recognition with unknown MRF graphs. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 1690-1697). United States of America: IEEE. DOI Scopus45 WoS28
2013	Shen, F., Shen, C., Shi, Q., Van Den Hengel, A., & Tang, Z. (2013). Inductive hashing on manifolds. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 1562-1569). United States of America: IEEE. DOI Scopus219 WoS171
2012	Shi, Q., Shen, C., Hill, R., & Van Den Hengel, A. (2012). Is margin preserved after random projection?. In Proceedings of the29th International Conference on Machine Learning, ICML 12 Vol. 1 (pp. 591-598). USA: Omnipress. Scopus34
2012	Yao, R., Shi, Q., Shen, C., Zhang, Y., & Van Den Hengel, A. (2012). Robust tracking with weighted online structured learning. In Proceedings of the 2012 European Conference on Computer Vision, ECCV 2012 Vol. 7574 LNCS (pp. 158-172). Germany: Springer-Verlag. DOI Scopus29 WoS25
2012	Li, X., Shen, C., Shi, Q., Dick, A., & Van Den Hengel, A. (2012). Non-sparse linear representations for visual tracking with online reservoir metric learning. In Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2012 (pp. 1760-1767). USA: IEEE. DOI Scopus73 WoS46
2011	Li, H., Shen, C., & Shi, Q. (2011). Real-time visual tracking using compressive sensing. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 1305-1312). Online: IEEE. DOI Scopus319 WoS233
2011	Shi, Q., Eriksson, A., Van Den Hengel, A., & Shen, C. (2011). Is face recognition really a compressive sensing problem?. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 553-560). USA: IEEE. DOI Scopus270 WoS192
2010	Shi, Q., Li, H., & Shen, C. (2010). Rapid face recognition using hashing. In Proceedings of 23rd IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 2753-2760). USA: IEEE. DOI Scopus48 WoS27
2009	Shi, Q., Zhou, L., Cheng, L., & Schuurmans, D. (2009). Discriminative maximum margin image object categorization with exact inference. In The 5th International Conference on Image and Graphics (pp. 232-237). Los Alamitos, California: IEEE Computer Society. DOI
2009	Shi, Q., Petterson, J., Dror, G., Langford, J., Smola, A., Strehl, A., & Vishwanathan, S. (2009). Hash kernels. In D. Dyk, & M. Welling (Eds.), JMLR Workshop and Conference Proceedings : Volume 5: AISTATS 2009 Vol. 5 (pp. 496-503). Online: JMLR. Scopus42
2008	Shi, Q., Wang, L., Cheng, L., & Smola, A. (2008). Discriminative human action segmentation and recognition using semi-Markov model. In 2008 IEEE conference on computer vision and pattern recognition (pp. 1-8). Online: IEEE. DOI Scopus83 WoS6
2007	Shi, Q., Altun, Y., Smola, A., & Vishwanathan, S. (2007). Semi-Markov models for sequence segmentation. In Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (pp. 640-648). United States: Association for Computational Linguistics. Scopus7
2004	Shi, Q., Li, Y., & Zhang, Y. (2004). A new automatic segmentation for synthetic aperture radar images. In 2004 International Symposium on Intelligent Multimedia, Video and Speech Processing, ISIMP 2004 (pp. 739-742). Scopus1
2004	Shi, Q. F., & Zhang, Y. N. (2004). Adaptive linear feature detection based on beamlet. In Proceedings of 2004 International Conference on Machine Learning and Cybernetics Vol. 7 (pp. 3981-3984). Scopus8

Conference Items

Year	Citation
2018	Bennett, B., Westra, S., Cavagnaro, T., Wheeler, S., Shi, Q., & Pagay, V. (2018). Unpacking Agricultural System Complexities for Improved Outcomes: Taking Advantage of Emerging Data, Technologies and Analysis. Poster session presented at the meeting of AGU Fall Meeting.

Preprint

Year	Citation
2021	Moghaddam, M. K., Abbasnejad, E., Wu, Q., Shi, J., & Hengel, A. V. D. (2021). Learning for Visual Navigation by Imagining the Success.
2020	Abedin, A., Ehsanpour, M., Shi, Q., Rezatofighi, H., & Ranasinghe, D. C. (2020). Attend And Discriminate: Beyond the State-of-the-Art for Human Activity Recognition using Wearable Sensors..
2019	Parvaneh, A., Abbasnejad, E., Wu, Q., & Shi, J. (2019). Show, Price and Negotiate: A Hierarchical Attention Recurrent Visual Negotiator..
2017	Abbasnejad, M. E., Shi, Q., Abbasnejad, I., Hengel, A. V. D., & Dick, A. R. (2017). Bayesian Conditional Generative Adverserial Networks..

Grants Summary

Total research funding awarded: $19.74M

Total Australian Research Council (ARC) funding awarded: $4.34 M
- Lead (1st) Chief Investigator (CI) (2 DPs, 1 DECRA): $1M
- Co-CI (4 LPs): $3.34M
Other funding (RDCs, Industry, ...): ~$15.4M

ARC Grants

ARC Discovery Project Grant 2024-2027, 2nd co-Chief Investigator (CI)

Learning to Reason in Reinforcement Learning
ARC Linkage Grant 2021-2024, 3rd Chief Investigator (CI), and Machine Learning Lead

A Machine Learning driven flow modelling of fragmented rocks in cave mining
ARC Discovery Project Grant 2016-2019, 1st Chief Investigator (CI)

Probabilistic Graphical Models For Interventional Queries
ARC Linkage Project Grant 2014-2017, 2nd CI

Sentient Buildings
ARC Discovery Project Grant 2014-2016, 1st CI

Online Learning for Large Scale Structured Data in Complex Situations
ARC Linkage Grant 2013-2016, 4th CI

Semantic change detection through large-scale learning
ARC Linkage Grant 2012-2015, 3rd CI

Scalable classification for massive datasets: randomized algorithms
ARC DECRA fellowship, 2012-2014, Sole CI

Compressive Sensing Based Probabilistic Graphical Models

University Courses

AI, DL, ISML, MBD, ...

Tutorials

Probabilistic Graphical Models

Representation [ pdf], ACVT, UoA, April 15, 2011
Inference [ pdf], ACVT, UoA, May 6, 2011
Learning [ pdf], ACVT, UoA, May 27, 2011
Sampling-based approximate inference [ pdf], ACVT, UoA, June 10, 2011
Temporal models [ pdf], ACVT, UoA, August 12, 2011

Generalisation Bounds

Basics [ pdf], ACVT, UoA, April 13, 2012
VC dimensions and bounds [ pdf], ACVT, UoA, April 27, 2012
Rademacher complexity and bounds [ pdf], ACVT, UoA, August 17, 2012
PAC Bayesian Bounds, [ pdf], ACVT, UoA, August 31, 2012
Regret bounds for online learning, [ pdf], ACVT, UoA, Nov. 2, 2012

Please email me if you find errors or typos in the slides.

Current Higher Degree by Research Supervision (University of Adelaide)

Date	Role	Research Topic	Program	Degree Type	Student Load	Student Name
2025	Principal Supervisor	Causal Reinforcement Learning for Interpretable Chain-of-Thought Reasoning in Large Language Models	Doctor of Philosophy	Doctorate	Full Time	Mr Jiayu Huang
2024	Principal Supervisor	Unraveling Opinion Polarization Dynamics in Social Network Echo Chambers: An Graph Modeling Approach with Causality	Doctor of Philosophy	Doctorate	Full Time	Mr Wenkang Jiang
2023	Principal Supervisor	Causal Discovery on Videos for Scene Graph Generation	Doctor of Philosophy	Doctorate	Full Time	Mr Hamed Damirchi
2023	Principal Supervisor	Leveraging Causality for Robust Multi-Source Domain Adaptation	Doctor of Philosophy	Doctorate	Full Time	Miss Tianjiao Jiang
2023	Principal Supervisor	Domain Adaptation via Causal Representation Learning	Doctor of Philosophy	Doctorate	Full Time	Mr Yichao Cai
2021	Principal Supervisor	Recognizing Individual and Collective Activity Using Videos	Doctor of Philosophy	Doctorate	Full Time	Mr Bahram Mohammadi
2020	Principal Supervisor	End-To-End semi-supervised text classification with automated text augmentation and character attention	Master of Philosophy	Master	Full Time	Mr Adrian John Orenstein

Past Higher Degree by Research Supervision (University of Adelaide)

Date	Role	Research Topic	Program	Degree Type	Student Load	Student Name
2022 - 2025	Principal Supervisor	The Role of Invariant Feature Selection and Causal Reinforcement Learning in Developing Robust Financial Trading Algorithms	Doctor of Philosophy	Doctorate	Full Time	Mr Haiyao Cao
2021 - 2025	Principal Supervisor	Efficient Deep Neural Network Pruning: From Convolutional Networks to Large Language Models	Doctor of Philosophy	Doctorate	Full Time	Ms Hongrong Cheng
2021 - 2025	Principal 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
2021 - 2025	Principal Supervisor	Towards Better Efficiency and Generalization in Imitation Learning : A Causal Perspective	Doctor of Philosophy under a Jointly-awarded Degree Agreement with	Doctorate	Full Time	Mr Mohamed Khalil Jabri
2020 - 2021	Principal Supervisor	Connecting Machine Learning to Causal Structure Learning with Jacobian Matrix	Master of Philosophy	Master	Full Time	Xiongren Chen
2019 - 2023	Principal Supervisor	Machine Learning and Natural Language Processing in Stock Prediction	Doctor of Philosophy	Doctorate	Full Time	Mr Jinan Zou
2019 - 2022	Principal Supervisor	Towards Optimistic, Imaginative, and Harmonious Reinforcement Learning in Single-Agent and Multi-Agent Environments	Doctor of Philosophy	Doctorate	Full Time	Mr Mahdi Kazemi Moghaddam
2018 - 2022	Principal Supervisor	Interactive Vision and Language Learning	Doctor of Philosophy	Doctorate	Full Time	Mr Amin Parvaneh
2017 - 2020	Co-Supervisor	Deep Learning Methods for Human Activity Recognition using Wearables	Doctor of Philosophy	Doctorate	Full Time	Mr Alireza Abedin Varamin
2016 - 2023	Principal Supervisor	Deep Learning for Multipitch Detection and Melody Extraction	Doctor of Philosophy	Doctorate	Part Time	Mr Xian Wang
2016 - 2021	Principal Supervisor	Deep Learning for Image Deblurring and Reflection Removal	Doctor of Philosophy	Doctorate	Full Time	Mr Jie Yang
2015 - 2018	Principal Supervisor	Adaptive Markov Random Fields for Structured Compressive Sensing	Doctor of Philosophy	Doctorate	Full Time	Miss Suwichaya Suwanwimolkul
2014 - 2019	Principal Supervisor	Joint Appearance and Motion Model for Multi-class Multi-object Tracking	Doctor of Philosophy	Doctorate	Full Time	Mr Chongyu Liu
2014 - 2016	Co-Supervisor	Deep Learning for Multi-label Scene Classification	Master of Philosophy	Master	Full Time	Mr Junjie Zhang
2012 - 2014	Co-Supervisor	Markov Random Fields with Unknown Heterogeneous Graphs	Doctor of Philosophy	Doctorate	Full Time	Mr Zhenhua Wang

Position: Professor
Phone: 83130324
Email: javen.shi@adelaide.edu.au
Fax: 83134366
Campus: North Terrace
Building: Australian Institute for Machine Learning, floor 1
Org Unit: Computer Science