Professor Javen Shi

• 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

• 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 Scopus4 WoS3
  2022	Ghiasi, A., Moghaddam, M. K., Ng, C. T., Sheikh, A. H., & Shi, J. Q. (2022). Damage classification of in-service steel railway bridges using a novel vibration-based convolutional neural network. Engineering Structures, 264, 114474-1-114474-16. DOI Scopus2 WoS2
  2022	Wang, X., Liu, L., & Shi, J. Q. (2022). Computationally Efficient Dilated Convolutional Model for Melody Extraction. IEEE Signal Processing Letters, 29, 1599-1603. DOI
  2022	Parvaneh, A., Abbasnejad, E., Teney, D., Haffari, R., Van Den Hengel, A., & Shi, J. Q. (2022). Active Learning by Feature Mixing. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2022-June, 12227-12236. DOI Scopus1
  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 Scopus1 WoS1
  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 Scopus6 WoS7
  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 Scopus4 WoS2
  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 Scopus43 WoS37
  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 Scopus12
  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 Scopus25 WoS27 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 Scopus8 WoS8
  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 Scopus8 WoS6
  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 Scopus35 WoS29
  2021	Rezatofighi, H., Kaskman, R., Taghizadeh Motlagh, S. F., Shi, Q., Milan, A., Cremers, D., . . . Reid, I. D. (2021). Learn to Predict Sets Using Feed-Forward Neural Networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(12), 1-15. DOI
  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 Scopus55 WoS39 Europe PMC4
  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 Scopus36 WoS27 Europe PMC2
  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 Scopus7 WoS6
  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 Scopus2
  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 Scopus14 WoS13
  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 Scopus16 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 Scopus5 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 Scopus41 WoS34
  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 Scopus23 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 Scopus8 WoS6
  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 Scopus52 WoS43
  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 Scopus62 WoS75
  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 Scopus42 WoS37
  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 Scopus133 WoS122 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 WoS96 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 Scopus102 WoS73
  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. Scopus138 WoS91
  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). Online: IEEE. DOI
  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). New Orleans, LA: IEEE. DOI Scopus1
  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). New Orleans, LA: IEEE COMPUTER SOC. DOI Scopus1 WoS1
  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
  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 Scopus7 WoS6
  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 Scopus1
  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 Scopus2
  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 Scopus2
  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 Scopus12
  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 Scopus11
  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 Scopus39
  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. Scopus7
  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 Scopus43 WoS37
  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 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 Vol. 2019-October (pp. 9934-9942). Los Alamitos, California: IEEE. DOI Scopus4 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 WoS4
  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 Scopus18 WoS14
  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 Scopus10 WoS4
  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 Scopus12 WoS6
  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 Scopus103 WoS82
  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 Scopus22 WoS13
  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 Scopus9 WoS8
  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. Scopus5 WoS4
  2018	Abbasnejad, M. E., Dick, A. R., Shi, Q., & Hengel, A. V. D. (2018). Active learning from noisy tagged images. In Proceedings of BMVC 2018 and Workshops (pp. 1-13). Newcastle upon Tyne: BMVA Press.
  2018	Liu, Y., Dong, W., Gong, D., Zhang, L., & Shi, Q. (2018). Deblurring natural image using super-gaussian fields. In Proceedings of the 15th European Conference on Computer Vision as published in Lecture Notes in Computer Science Vol. 11205 LNCS (pp. 467-484). Switzerland: Springer Nature. DOI Scopus7 WoS10
  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 Scopus12 WoS28
  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 Scopus21 WoS15
  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 Scopus212 WoS136
  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 WoS7
  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 Scopus6 WoS4
  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 Scopus21 WoS20
  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 Scopus41 WoS23
  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 Scopus55 WoS48
  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 Scopus22 WoS15
  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 Scopus1104 WoS790
  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 Scopus35 WoS25
  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 Scopus235 WoS178
  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 Scopus14 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 Scopus347 WoS288
  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 Scopus87 WoS63
  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 Scopus198 WoS157
  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 Scopus34 WoS17
  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 Scopus206 WoS161
  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. Scopus32
  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 Scopus313 WoS227
  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 WoS190
  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 WoS24
  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. Scopus36
  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 WoS13
  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

University Courses 

AI, DL, ISML, MBD, ...

Tutorials 

Probabilistic Graphical Models

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

    

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

    

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

    

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

    

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

    

Generalisation Bounds

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

    

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

    

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

    

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

    

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

    

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

• 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
  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
  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 - 2021	Principal Supervisor	Deep Learning for Image Deblurring and Reflection Removal	Doctor of Philosophy	Doctorate	Full Time	Mr Jie Yang
  2015 - 2018	Principal Supervisor	Adaptive Markov Random Fields for Structured Compressive Sensing	Doctor of Philosophy	Doctorate	Full Time	Miss Suwichaya Suwanwimolkul
  2014 - 2019	Principal Supervisor	Joint Appearance and Motion Model for Multi-class Multi-object Tracking	Doctor of Philosophy	Doctorate	Full Time	Mr Chongyu Liu
  2014 - 2016	Co-Supervisor	Deep Learning for Multi-label Scene Classification	Master of Philosophy	Master	Full Time	Mr Junjie Zhang
  2012 - 2014	Co-Supervisor	Markov Random Fields with Unknown Heterogeneous Graphs	Doctor of Philosophy	Doctorate	Full Time	Mr Zhenhua Wang

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