Javen Shi

Research Interests

Artificial Intelligence Computer Vision Knowledge Representation and Machine Learning

Prof Javen Shi

Professor

School of Computer Science and Information Technology

College of Engineering and Information Technology

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

Professor Javen Qinfeng Shi is the Founding Director of the Causal AI Group and one of the directors at the Australian Institute for Machine Learning (AIML). His research spans causation, AI, mind, and metaphysics. Globally ranked 6th in probabilistic graphical models and 4th in causation by Google Scholar, he has contributed to industries including material discovery, agriculture, mining, sport, manufacturing, bushfire, health and education.
 
His awards include the ACM SIGIR 2025 Test of Time Award, first place in the Open Catalyst Challenge on AI driven material discovery at NeurIPS AI for Science 2023, victory in the AUS/NZ Bushfire Data Quest 2020, finalist recognition in the SA Department of Energy and Mining’s Gawler Challenge 2020, 2nd place in the global OZ Minerals Explorer Challenge 2019 (with 1,000+ participants from 62 countries), and the Golden Prize (1st place) from Volkswagen in 2019 for AI-powered digital factory innovation.
 
Beyond the lab, Professor Shi walks the path of a mystic. In Be Cause: You Are the Cause! and Seven Wisdoms for Success, he offers a vision for the Aquarian Age—awakening humanity as conscious co-creators, blending scientific brilliance with spiritual wisdom to transform personal destiny and collective evolution.

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?

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

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

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

Year Citation
2026 Mohammadi, B., Abbasnejad, E., Qi, Y., Wu, Q., Van Den Hengel, A., & Shi, J. Q. (2026). Parameter-efficient action planning with large language models for vision-and-language navigation. Pattern Recognition, 172, 11 pages.
DOI
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 Scopus1 WoS1
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 Scopus1 WoS1
2025 Liu, Y., Zhang, Z., Gong, D., Gong, M., Huang, B., van den Hengel, A., . . . Shi, J. Q. (2025). Latent Covariate Shift: Unlocking Partial Identifiability for Multi-Source Domain Adaptation. Transactions on Machine Learning Research, 2025-April.
Scopus1
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 Scopus4 WoS3
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 Scopus4 WoS4
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 Scopus211 WoS143 Europe PMC19
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 Scopus9 WoS9
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 Scopus5 WoS4
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 Scopus41 WoS43 Europe PMC10
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 Scopus10 WoS10
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.
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.
Scopus5
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 Scopus87
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 Scopus31 WoS23 Europe PMC3
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 Scopus6 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 Scopus7 WoS8 Europe PMC1
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 Scopus10 WoS9
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 Scopus7 WoS5 Europe PMC2
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 Scopus6 WoS5 Europe PMC2
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 Scopus5 WoS4
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 Scopus48 WoS41
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 Scopus5 WoS3
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 Scopus30 WoS29
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 Scopus14 WoS13
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 Scopus89 WoS80
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 Scopus100 WoS87
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 Scopus44 WoS35
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 WoS17
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 Scopus111 WoS95 Europe PMC46
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 Scopus15 WoS14
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 Scopus31 WoS26 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 Scopus90 WoS63 Europe PMC37
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 Europe PMC1
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 Scopus235 WoS201 Europe PMC22
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 Scopus100 WoS81 Europe PMC13
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 Scopus24 WoS21 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 Scopus27 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 Scopus27 WoS20 Europe PMC8
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 PMC3
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 Scopus65 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 Scopus35 WoS33
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 PMC8
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 WoS8
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 Scopus92 WoS80
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 Scopus73 WoS89
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 Scopus52 WoS46
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 Scopus147 WoS135 Europe PMC28
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 Scopus127 WoS119 Europe PMC20
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 Scopus116 WoS85
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.
Scopus159 WoS104
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

Year Citation
2025 Lin, Y., Xu, H., Liu, L., & Shi, J. Q. (2025). A Simple-but-Effective Baseline for Training-Free Class-Agnostic Counting. In Proceedings - 2025 IEEE Winter Conference on Applications of Computer Vision, WACV 2025 (pp. 8155-8164). Tucson, AZ, USA: IEEE.
DOI Scopus1
2025 Tong, R., Liu, Y., Shi, J. Q., & Gong, D. (2025). Coreset Selection Via Reducible Loss in Continual Learning. In Proceedings of the 13th International Conference on Learning Representations (ICLR 2025) (pp. 57701-57736). Singapore: International Conference on Learning Representations (ICLR).
Scopus5
2025 Zhang, Z., Ng, I., Gong, D., Liu, Y., Gong, M., Huang, B., . . . Shi, J. Q. (2025). ANALYTIC DAG CONSTRAINTS FOR DIFFERENTIABLE DAG LEARNING. In 13th International Conference on Learning Representations Iclr 2025 (pp. 63845-63870).
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 Scopus17 WoS15
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 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.
Scopus9
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 Scopus5
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 Scopus3 WoS2
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.
Scopus18
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 Scopus114 WoS86
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 Scopus12 WoS9
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 Scopus54 WoS38
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 Scopus143 WoS113
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 Scopus12 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 WoS7
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 AG.
DOI Scopus71 WoS61
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 Scopus23 WoS16
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 Scopus124 WoS95
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.
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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 Scopus84 WoS77
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 Scopus12 WoS13
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 Scopus306 WoS250
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 Scopus78 WoS59
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 Scopus22 WoS19
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.
Scopus10 WoS4
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 Scopus26 WoS98
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 Scopus40 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.
Scopus1
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 Scopus353 WoS278
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 Scopus80 WoS63
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 Scopus2 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 WoS9
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 Scopus2126 WoS1579
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 Scopus307 WoS233
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 Scopus17 WoS17
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 Scopus391 WoS276
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 Scopus115 WoS90
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 Scopus204 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.
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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 Scopus30 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 Scopus320 WoS234
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 WoS28
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.
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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

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.

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

  1. Representation [ pdf], ACVT, UoA, April 15, 2011 

     

  2. Inference [ pdf], ACVT, UoA, May 6, 2011 

     

  3. Learning [ pdf], ACVT, UoA, May 27, 2011 

     

  4. Sampling-based approximate inference [ pdf], ACVT, UoA, June 10, 2011 

     

  5. Temporal models [ pdf], ACVT, UoA, August 12, 2011 

     

Generalisation Bounds

  1. Basics [ pdf], ACVT, UoA, April 13, 2012 

     

  2. VC dimensions and bounds [ pdf], ACVT, UoA, April 27, 2012 

     

  3. Rademacher complexity and bounds [ pdf], ACVT, UoA, August 17, 2012 

     

  4. PAC Bayesian Bounds, [ pdf], ACVT, UoA, August 31, 2012 

     

  5. Regret bounds for online learning, [ pdf], ACVT, UoA, Nov. 2, 2012 

     

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

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
2025 Principal Supervisor Personalized Cancer Detection and Treatment via Casual Reinforcement Learning with Hyperspectral Imaging Doctor of Philosophy Doctorate Full Time Mr Meisam Mahmoodi
2025 Principal Supervisor Causal Representation Learning Doctor of Philosophy Doctorate Full Time Mr Hossein Allahresani
2025 Principal Supervisor Personalized Cancer Detection and Treatment via Casual Reinforcement Learning with Hyperspectral Imaging Doctor of Philosophy Doctorate Full Time Mr Meisam Mahmoodi
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
2025 Principal Supervisor Causal Representation Learning Doctor of Philosophy Doctorate Full Time Mr Hossein Allahresani
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
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
2023 Principal Supervisor Leveraging Causality for Robust Multi-Source Domain Adaptation Doctor of Philosophy Doctorate Full Time Miss Tianjiao Jiang
2023 Principal Supervisor Causal Discovery on Videos for Scene Graph Generation Doctor of Philosophy Doctorate Full Time Mr Hamed Damirchi
2023 Principal Supervisor Domain Adaptation via Causal Representation Learning Doctor of Philosophy Doctorate Full Time Mr Yichao Cai
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

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 Finding the Optimal Path in Real-World Environments Using Natural Language Instructions Doctor of Philosophy Doctorate Full Time Mr Bahram Mohammadi
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

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