Professor Javen Shi

Professor

School of Computer Science

Faculty of Sciences, Engineering and Technology

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

Professor Javen Shi is the Founding Director of Probabilistic Graphical Model Group at the University of Adelaide, Director in Advanced Reasoning and Learning of Australian Institute for Machine Learning (AIML), and the Chief Scientist of Smarter Regions. He is a leader in machine learning in both high-end AI research and also real world applications with high impacts.

He is recognised both locally and internationally for the impact of his work, through an impressive record of publishing in the highest ranked venue in the field of Computer Vision & Pattern Recognition (25 CVPR papers), through an impeccable record of ARC funding (including his DECRA, 2 DP as 1st CI, 4 LP as Co-CI). He has published over 90 peer reviewed papers, over 80% are at ERA [A/A*]. He has over 6700 Google Scholar citations with h-index 33. Google Scholar ranks him 6th globally in Probabilistic Graphical Models.

My Research
Career
Publications
Grants and Funding
Teaching
Supervision
Contact

He has transferred his research to diverse industries including agriculture, mining, sport, manufacturing, bushfire, water utility, health and education. Recent awards include:

1) 2nd place from a global mining competition OZ Minerals Explorer Challenge 2019 (over 1000 participants from 62 countries);

2) Golden prize (1st place) from Volkswagen in 2019 (digital factory powered by AI);

3) Finalist of SA Department of Energy and Mining’s Gawler Challenge 2020 (over 2k participants from 100+ countries) with his team’s work being considered as “The most innovative modelling” by the judge panel;

4) the top winning team (in collaboration with USC and CSIRO) in AUS/NZ Bushfire Data Quest 2020 using AI to predict fire scar and spread that led to their winning $0.5M Citizen Science Grant in 2021.

Appointments

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

Awards and Achievements

Date Type Title Institution Name Country Amount

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

Language Competency

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

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

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

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

Education

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

Research Interests

Artificial Intelligence Computer Vision Knowledge Representation and Machine Learning

Journals

Year	Citation
2022	Parvaneh, A., Abbasnejad, E., Wu, Q., Shi, Q., & Van Den Hengel, A. (2022). Show, price and negotiate: a hierarchical attention recurrent visual negotiator. IEEE Transactions on Multimedia, 24, 1426-1434. DOI
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 Scopus2 WoS1
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 Scopus1 WoS1
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, 14 pages. DOI
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 WoS1
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 Scopus3 WoS1
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 Scopus33 WoS32
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 Scopus7
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 Scopus18 WoS20 Europe PMC6
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 Scopus7 WoS4
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 Scopus6 WoS5
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 Scopus25 WoS21
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, PP, 13 pages. DOI
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 Scopus38 WoS29 Europe PMC3
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 Scopus20 WoS15 Europe PMC1
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 Scopus1 WoS1
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 Scopus6 WoS5
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 Scopus1
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 Scopus13 WoS12
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 Scopus14 WoS9 Europe PMC1
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 Scopus4 WoS6
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	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 Scopus37 WoS32
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 Scopus1 WoS1
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 Scopus22 WoS22
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 Scopus14 WoS12
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 Scopus7 WoS5
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 Scopus4 WoS2
2018	Yao, R., Lin, G., Shi, Q., & Ranasinghe, D. (2018). Efficient dense labelling of human activity sequences from wearables using fully convolutional networks. Pattern Recognition, 78, 252-266. DOI Scopus49 WoS41
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 Scopus60 WoS72
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 Scopus10 WoS8
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 Scopus12 WoS10
2017	Abbasnejad, M., Shi, Q., Abbasnejad, I., Hengel, A., & Dick, A. (2017). Bayesian Conditional Generative Adverserial Networks.. CoRR, abs/1706.05477.
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 Scopus41 WoS36
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 Scopus7 WoS6
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 Scopus128 WoS121 Europe PMC13
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 Scopus101 WoS94 Europe PMC8
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
2012	Gao, J., Shi, Q., & Caetano, T. (2012). Dimensionality reduction via compressive sensing. Pattern Recognition Letters, 33(9), 1163-1170. DOI Scopus28 WoS24
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 Scopus101 WoS71
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. Scopus137
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
	Li, H., Shen, C., & Shi, Q. (n.d.). Real-time Visual Tracking Using Sparse Representation.
	Liu, Y., Liu, L., Rezatofighi, H., Do, T. -T., Shi, Q., & Reid, I. (n.d.). Learning Pairwise Relationship for Multi-object Detection in Crowded Scenes.

Conference Papers

Year	Citation
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). Waikoloa, Hawaii: IEEE. DOI
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
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 Scopus1
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 Scopus4 WoS3
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 Scopus1
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 Scopus2
2020	Ehsanpour, M., Abedin, A., Saleh, F., Shi, J., Reid, I., & Rezatofighi, H. (2020). Joint Learning of Social Groups, Individuals Action and Sub-group Activities in Videos. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 12354 LNCS (pp. 177-195). Cham, Switzerland: Springer International Publishing. DOI Scopus5
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 Scopus9
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 Scopus33
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 Scopus2
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. Scopus1
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 Scopus35 WoS32
2019	Abbasnejad, M. E., Shi, Q., Van Den Hengel, A., & Liu, L. (2019). A Generative Adversarial Density Estimator.. In CVPR (pp. 10782-10791). online: Computer Vision Foundation / IEEE.
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 Scopus2
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 Vol. 2019-October (pp. 9934-9942). Los Alamitos, California: IEEE. DOI Scopus3 WoS2
2019	Ehsan Abbasnejad, M., Dick, A., Shi, Q., & Van Den Hengel, A. (2019). Active learning from noisy tagged images. In British Machine Vision Conference 2018, BMVC 2018.
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 Scopus1
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 Scopus6 WoS3
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 Scopus15 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 Scopus8 WoS2
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 Scopus11 WoS5
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 Scopus71 WoS61
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 Scopus17 WoS12
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 Scopus8 WoS6
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. Scopus4 WoS3
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 Scopus6 WoS3
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 Scopus10 WoS23
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 Scopus19 WoS12
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 Scopus194 WoS128
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. Scopus9 WoS6
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. 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 Scopus5 WoS3
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 Scopus20 WoS18
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 Scopus37 WoS20
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 Scopus53 WoS45
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 Scopus21 WoS14
2016	Tan, M., Xiao, S., Gao, J., Xu, D., Van Den Hengel, A., & Shi, Q. (2016). Proximal riemannian pursuit for large-scale trace-norm minimization. In Proceedings of the I29th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2016) Vol. 2016-December (pp. 5877-5886). Las Vegas, NV: IEEE. DOI Scopus1 WoS1
2016	Zhang, L., Wei, W., Zhang, Y., Shen, C., Van Den Hengel, A., & Shi, Q. (2016). Cluster sparsity field for hyperspectral imagery denoising. In B. Leibe, J. Matas, N. Sebe, & M. Welling (Eds.), Proceedings of the 14th European Conference on Computer Vision Vol. 9909 (pp. 631-647). Amsterdam, Netherlands: Springer International Publishing AG. DOI Scopus14 WoS12
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 Scopus6 WoS6
2016	Tan, M., Yan, Y., Wang, L., Van Den Hengel, A., Tsang, I., & Shi, Q. (2016). Learning sparse confidence-weighted classifier on very high dimensional data. In Proceedings of the 30th AAAI Conference on Artificial Intelligence Vol. 3 (pp. 2080-2086). Phoenix, AZ: AAAI Press. Scopus3 WoS3
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 WoS11
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 Scopus1017 WoS696
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 Scopus34 WoS24
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 Scopus225 WoS166
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 Scopus13 WoS14
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 Scopus11 WoS5
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 Scopus342 WoS279
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 Scopus86 WoS61
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 Scopus199 WoS156
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 Scopus31 WoS14
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 Scopus203 WoS157
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. Scopus31
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 Scopus26 WoS23
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 Scopus71 WoS44
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 Scopus312 WoS222
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 Scopus265 WoS188
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 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
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. Scopus34
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 Scopus80 WoS12
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

Grants Summary

Total research funding awarded: $13.5M

Total Australian Research Council (ARC) funding awarded: $2.8 M
- Lead (1st) Chief Investigator (CI) (2 DPs, 1 DECRA): $1M
- Co-CI (4 LPs): $1.7M
Other funding (RDCs, Industry, ...): ~$10.7M

ARC Grants

ARC Linkage Grant 2021-2024, 3rd Chief Investigator (CI), and Machine Learning Lead
A Machine Learning driven flow modelling of fragmented rocks in cave mining
ARC Discovery Project Grant 2016-2019, 1st Chief Investigator (CI)
Probabilistic Graphical Models For Interventional Queries
ARC Linkage Project Grant 2014-2017, 2nd CI
Sentient Buildings
ARC Discovery Project Grant 2014-2016, 1st CI
Online Learning for Large Scale Structured Data in Complex Situations
ARC Linkage Grant 2013-2016, 4th CI
Semantic change detection through large-scale learning
ARC Linkage Grant 2012-2015, 3rd CI
Scalable classification for massive datasets: randomized algorithms
ARC DECRA fellowship, 2012-2014, Sole CI
Compressive Sensing Based Probabilistic Graphical Models

University Courses

AI, DL, ISML, MBD, ...

Tutorials

Probabilistic Graphical Models

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

Generalisation Bounds

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

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

Current Higher Degree by Research Supervision (University of Adelaide)

Date	Role	Research Topic	Program	Degree Type	Student Load	Student Name
2022	Principal Supervisor	Causality and Deep Reinforcement Learning on Financial Trading.	Doctor of Philosophy	Doctorate	Full Time	Mr Haiyao Cao
2021	Principal Supervisor	Research on lightweight intelligent models based on deep learning: incorporating artificial intelligence on end devices	Doctor of Philosophy	Doctorate	Full Time	Ms Hongrong Cheng
2021	Principal Supervisor	Task-Based Mapping of Human Kinematics to Robot Arm Kinematics Using Generative Adversarial Networks (GANs)	Doctor of Philosophy under a Jointly-awarded Degree Agreement with	Doctorate	Full Time	Mr Mohamed Khalil Jabri
2021	Principal Supervisor	Data efficient learning	Doctor of Philosophy	Doctorate	Full Time	Mr Yuhao Lin
2021	Principal Supervisor	Recognizing Individual and Collective Activity Using Videos	Doctor of Philosophy	Doctorate	Full Time	Mr Bahram Mohammadi
2020	Principal Supervisor	End-To-End semi-supervised text classification with automated text augmentation and character attention	Master of Philosophy	Master	Full Time	Mr Adrian John Orenstein
2019	Principal Supervisor	Machine Learning and Natural Language Processing in Stock Prediction	Doctor of Philosophy	Doctorate	Full Time	Mr Jinan Zou
2019	Principal Supervisor	Visual Navigation in Embodied Autonomous Agents	Doctor of Philosophy	Doctorate	Full Time	Mr Mahdi Kazemi Moghaddam
2018	Co-Supervisor	Social Human Activity Recognition and Localization in Video Sequences	Doctor of Philosophy	Doctorate	Full Time	Ms Mahsa Ehsanpour
2017	Principal Supervisor	Social network analysis using statistical machine learning	Doctor of Philosophy	Doctorate	Part Time	Mrs Iman Fahmy Shoaib
2016	Principal Supervisor	Markov Logic Networks. Generalisations and Applications	Doctor of Philosophy	Doctorate	Part Time	Mr Xian Wang

Past Higher Degree by Research Supervision (University of Adelaide)

Date	Role	Research Topic	Program	Degree Type	Student Load	Student Name
2020 - 2021	Principal Supervisor	Connecting Machine Learning to Causal Structure Learning with Jacobian Matrix	Master of Philosophy	Master	Full Time	Mr Xiongren Chen
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 - 2021	Principal Supervisor	Deep Learning for Image Deblurring and Reflection Removal	Doctor of Philosophy	Doctorate	Full Time	Mr Jie Yang
2015 - 2018	Principal Supervisor	Adaptive Markov Random Fields for Structured Compressive Sensing	Doctor of Philosophy	Doctorate	Full Time	Miss Suwichaya Suwanwimolkul
2014 - 2019	Principal Supervisor	Joint Appearance and Motion Model for Multi-class Multi-object Tracking	Doctor of Philosophy	Doctorate	Full Time	Mr Chongyu Liu
2014 - 2016	Co-Supervisor	Deep Learning for Multi-label Scene Classification	Master of Philosophy	Master	Full Time	Mr Junjie Zhang
2012 - 2014	Co-Supervisor	Markov Random Fields with Unknown Heterogeneous Graphs	Doctor of Philosophy	Doctorate	Full Time	Mr Zhenhua Wang

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

Professor Javen Shi

Appointments

Awards and Achievements

Language Competencies