Research Interests
Artificial Intelligence Computer Vision Knowledge Representation and Machine LearningProf Javen Shi
Professor
School of Computer Science and Information Technology
College of Engineering and Information Technology
Eligible to supervise Masters and PhD as Principal Supervisor - email supervisor to discuss availability.
Professor Javen Qinfeng Shi is the Founding Director of the Causal AI Group, Interim Director of the Responsible AI Research Centre, and Director in Advanced Reasoning and Learning at the Australian Institute for Machine Learning (AIML). His research spans causation, artificial intelligence, mind, and metaphysics. Ranked globally 3rd in causal AI, 3rd in causation and 6th in probabilistic graphical models by Google Scholar, his work has contributed to industries including materials discovery, agriculture, mining, supply chains, sport, manufacturing, bushfire management, healthcare, and education.
His accolades include the ACM SIGIR 2025 Test of Time Award; first place in the Open Catalyst Challenge for AI-driven materials discovery at NeurIPS AI for Science 2023; victory in the AUS/NZ Bushfire Data Quest 2020; finalist recognition in the South Australian Department for Energy and Mining’s Gawler Challenge 2020; second place in the global OZ Minerals Explorer Challenge 2019 (among more than 1,000 participants from 62 countries); and the Golden Prize (1st place) awarded by Volkswagen in 2019 for AI-powered digital factory innovation.
Beyond the laboratory, Professor Shi explores the deeper questions of consciousness, meaning, and human potential. In Be Cause : You Are the Cause!, Seven Wisdoms for Success, and What the Soul Wants, he presents a vision for the Aquarian Age, awakening humanity as conscious co-creators through the integration of scientific brilliance and spiritual wisdom to transform both 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(Part B), 112462. Scopus9 WoS8 |
| 2026 | Liu, Y., Zhang, Z., Gong, D., Gong, M., Huang, B., van den Hengel, A., . . . Shi, J. Q. (2026). Identifying Weight-Variant Latent Causal Models. Journal of Machine Learning Research, 27, 49 pages. Scopus1 |
| 2026 | Li, G., Zhang, S., Yuwono, J. A., Li, X., Shi, J. Q., Wang, C., & Guo, Z. (2026). Hydrophobic liquid electrolyte interphases for efficient aqueous zinc batteries. Nature Nanotechnology, 21(7), 967-975. Scopus11 WoS14 Europe PMC1 |
| 2026 | Yin, Z., Qi, Y., Gong, D., Abbasnejad, E., Yue, K., & Shi, J. Q. (2026). Link prediction on multi-relational graphs from an influence propagation perspective. Pattern Recognition, 180, 114039. Scopus1 |
| 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. Scopus10 WoS9 |
| 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. Scopus15 WoS13 |
| 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. Scopus5 WoS5 Europe PMC4 |
| 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. Scopus5 |
| 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. Scopus20 WoS20 |
| 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. Scopus6 WoS6 |
| 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. Scopus16 WoS14 |
| 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. Scopus9 WoS8 |
| 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. Scopus92 WoS87 Europe PMC27 |
| 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. Scopus486 WoS338 Europe PMC38 |
| 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. Scopus18 WoS17 |
| 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 | 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. Scopus110 |
| 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. Scopus42 WoS33 Europe PMC6 |
| 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. Scopus6 WoS6 Europe PMC2 |
| 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. Scopus12 WoS11 |
| 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. Scopus10 WoS7 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. Scopus10 WoS9 Europe PMC3 |
| 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. Scopus7 WoS7 |
| 2023 | Lin, L., Jones, T. W., Yang, T. C. -J., Li, X., Wu, C., Xiao, Z., . . . Wang, X. (2023). Hydrogen bonding in perovskite solar cells. Matter, 7(1), 38-58. Scopus127 WoS127 |
| 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. Scopus10 WoS12 Europe PMC2 |
| 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 | Zhang, Z., Dupty, M. H., Wu, F., Shi, J. Q., & Lee, W. S. (2023). Factor Graph Neural Networks. Journal of Machine Learning Research, 24. Scopus6 WoS3 |
| 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. Scopus3 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. Scopus34 WoS32 |
| 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. Scopus15 WoS14 |
| 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. Scopus58 WoS49 |
| 2022 | Wang, X., Liu, L., & Shi, J. Q. (2022). Computationally Efficient Dilated Convolutional Model for Melody Extraction. IEEE Signal Processing Letters, 29, 1599-1603. Scopus6 WoS5 |
| 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. Scopus12 WoS7 Europe PMC1 |
| 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. Scopus110 WoS102 |
| 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. Scopus125 WoS107 Europe PMC55 |
| 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. Scopus18 WoS15 |
| 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. Scopus34 WoS30 Europe PMC4 |
| 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. Scopus96 WoS67 Europe PMC39 |
| 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. Scopus111 WoS97 |
| 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. Scopus28 WoS18 |
| 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. Scopus50 WoS40 |
| 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. Scopus4 WoS4 |
| 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. Scopus16 WoS13 Europe PMC3 |
| 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. Scopus28 WoS24 Europe PMC5 |
| 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. Scopus285 WoS244 Europe PMC30 |
| 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. Scopus113 WoS94 Europe PMC22 |
| 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. Scopus29 WoS22 Europe PMC11 |
| 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. 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. Scopus28 WoS24 |
| 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 | 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. Scopus38 WoS35 |
| 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. Scopus23 WoS20 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. 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. Scopus5 WoS3 |
| 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. Scopus7 WoS4 |
| 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. Scopus1 |
| 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 | 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. Scopus68 WoS55 |
| 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 | 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. |
| 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. Scopus99 WoS84 |
| 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. Scopus79 WoS93 |
| 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 | 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. Scopus14 WoS12 |
| 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. Scopus19 WoS17 |
| 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. |
| 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. Scopus54 WoS47 |
| 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 | 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. 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. Scopus10 WoS9 Europe PMC2 |
| 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. Scopus148 WoS135 Europe PMC31 |
| 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. Scopus132 WoS125 Europe PMC23 |
| 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. |
| 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. 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. 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. Scopus116 WoS87 |
| 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. Scopus164 WoS106 |
| 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 |
|---|---|
| 2026 | Jiang, W., Liu, Y., Gao, E., Abbasnejad, E., Yao, L., & Shi, J. Q. (2026). Learning Latent Dynamical Causal Processes for Single-Cell Perturbation Prediction. In Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2 (pp. 11177-11188). ACM. DOI |
| 2025 | Lin, Y., Xu, H., Liu, L., & Shi, J. Q. (2025). A Simple-but-Effective Baseline for Training-Free Class-Agnostic Counting. In Proceedings - 2025 IEEE Winter Conference on Applications of Computer Vision, WACV 2025 (pp. 8155-8164). Tucson, AZ, USA: IEEE. DOI Scopus1 |
| 2025 | 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). Scopus25 |
| 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). |
| 2025 | Jiang, T., Zhang, Z., Liu, Y., & Shi, J. Q. (2025). Causal Disentanglement and Cross-Modal Alignment for Enhanced Few-Shot Learning. In Proceedings of the IEEE International Conference on Computer Vision (pp. 890-900). IEEE. DOI Scopus2 |
| 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 Scopus2 |
| 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 Scopus23 WoS20 |
| 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. Scopus12 |
| 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 Scopus9 WoS4 |
| 2024 | Jabri, M. K., Papadakis, P., Abbasnejad, E., Coppin, G., & Shi, J. (2024). Invariant Representation Learning for Generalizable Imitation. In Esann 2024 Proceedings 32ndeuropean Symposium on Artificial Neural Networks Computational Intelligence and Machine Learning (pp. 461-466). Ciaco - i6doc.com. 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 Scopus4 WoS3 |
| 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 | 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 Scopus11 WoS11 |
| 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 Scopus131 WoS111 |
| 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 Scopus14 WoS11 |
| 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 Scopus36 WoS26 |
| 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 Scopus63 WoS47 |
| 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 Scopus173 WoS145 |
| 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 WoS2 |
| 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). Scopus10 |
| 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. Scopus24 |
| 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. |
| 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 Scopus19 WoS16 |
| 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 Scopus15 WoS12 |
| 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 Scopus12 WoS8 |
| 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 Proceedings of the 32nd British Machine Vision Conference (pp. 0342-1-0342-13). United Kingdon: British Machine Vision Association, BMVA. 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 Scopus84 WoS69 |
| 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 Scopus25 WoS18 |
| 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 Scopus141 WoS108 |
| 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. Scopus37 |
| 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 Scopus7 |
| 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 | 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 Scopus90 WoS83 |
| 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 Scopus13 WoS13 |
| 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 | 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 Scopus20 WoS13 |
| 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 Scopus348 WoS285 |
| 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 Scopus94 WoS74 |
| 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 |
| 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 | 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 |
| 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 Scopus30 WoS105 |
| 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 Scopus42 WoS34 |
| 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 | 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 | 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 Scopus370 WoS298 |
| 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 WoS29 |
| 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 | 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 |
| 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 Scopus50 WoS31 |
| 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 Scopus82 WoS68 |
| 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 Scopus14 WoS11 |
| 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 Scopus2398 WoS1830 |
| 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 Scopus326 WoS246 |
| 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 | 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 Scopus396 WoS278 |
| 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 |
| 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 Scopus120 WoS92 |
| 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 Scopus206 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 WoS170 |
| 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 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 Scopus273 WoS194 |
| 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., 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. Scopus44 |
| 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 |
| 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. Scopus8 |
| 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). IEEE. DOI 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: $142.72M
- Total Australian Research Council (ARC) funding awarded: $4.34M
- Lead (1st) Chief Investigator (CI) (2 DPs, 1 DECRA): $1M
- Co-CI (4 LPs): $3.34M
- Other funding (RDCs, Industry, ...): ~$138.38M
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
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Representation [ pdf], ACVT, UoA, April 15, 2011
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Inference [ pdf], ACVT, UoA, May 6, 2011
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Learning [ pdf], ACVT, UoA, May 27, 2011
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Sampling-based approximate inference [ pdf], ACVT, UoA, June 10, 2011
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Temporal models [ pdf], ACVT, UoA, August 12, 2011
Generalisation Bounds
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Basics [ pdf], ACVT, UoA, April 13, 2012
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VC dimensions and bounds [ pdf], ACVT, UoA, April 27, 2012
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Rademacher complexity and bounds [ pdf], ACVT, UoA, August 17, 2012
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PAC Bayesian Bounds, [ pdf], ACVT, UoA, August 31, 2012
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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 |
|---|---|---|---|---|---|---|
| 2026 | Principal Supervisor | Causal Representation Learning for Decision Making: Linking Latent Identifiability with Language Model Concepts | Doctor of Philosophy | Doctorate | Full Time | Mr Brian Simon Drake |
| 2026 | Principal Supervisor | Large Language Model Application in Biology and Health | Doctor of Philosophy | Doctorate | Full Time | Mr Yijia Song |
| 2026 | Principal Supervisor | Exploiting Latent Causal Concept Structures in Large Language Models | Doctor of Philosophy | Doctorate | Full Time | Mr Shurui Mei |
| 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 |
| 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 |
| 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 |
