Professor Anton Van Den Hengel

AIML Director

Australian Institute for Machine Learning - Operations

Division of Research and Innovation

Anton van den Hengel is the founding Director of The Australian Institute for Machine Learning, a Chief Investigator of the Australian Centre of Excellence in Robotic Vision, and a Professor of Computer Science at the University of Adelaide. Prof. van den Hengel has been a CI on over $60m in external research funding from sources including Google, Canon, Facebook, BHP Billiton and the ARC. Prof. van den Hengel has won a number of awards, including the Pearcey Foundation Entrepreneur Award, the SA Science Excellence Award for Research Collaboration, and the CVPR Best Paper prize in 2010. He has authored over 300 publications, had 8 patents commercialised, formed 2 start-ups, and has recently had a medical technology achieve first-in-class FDA approval. Current research interests include Deep Learning, vision and language problems, interactive image-based modelling, large-scale video surveillance, and medical machine learning.

Career
Publications
Supervision
Professional Activities
Contact

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Appointments

Date Position Institution name
2019 Director, Australian Institute for Machine Learning University of Adelaide
2009 Professor University of Adelaide

Date	Position	Institution name
2019	Director, Australian Institute for Machine Learning	University of Adelaide
2009	Professor	University of Adelaide

Awards and Achievements

Date	Type	Title	Institution Name	Country	Amount
2017	Award	SA Science Excellence Award for Research Collaboration	SA State Government	Australia	—
2017	Achievement	First place in Visual Question Answering 2 challenge at CVPR 2017	Facebook and Georgia Tech	United States	—

Education

Date	Institution name	Country	Title
1995 - 2000	University of Adelaide	Australia	Ph. D. in Computer Science
1993 - 1994	University of Adelaide	Australia	Masters Degree in Computer Science
1989 - 1993	University of Adelaide	Australia	Bachelor of Laws
1988 - 1991	University of Adelaide	Australia	Bachelor of Mathematical Science

Research Interests

Computer Vision Knowledge Representation and Machine Learning

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Journals

Year	Citation
2021	Shevchenko, V., Teney, D., Dick, A. R., & Hengel, A. V. D. (2021). Reasoning over Vision and Language: Exploring the Benefits of Supplemental Knowledge.. CoRR, abs/2101.06013.
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, 2947-2962. DOI
2020	Orlando, J. I., Fu, H., Barbossa Breda, J., van Keer, K., Bathula, D. R., Diaz-Pinto, A., . . . Bogunović, H. (2020). REFUGE Challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs. Medical Image Analysis, 59, 21 pages. DOI Scopus58 WoS28 Europe PMC5
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
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 Scopus3
2020	Teney, D., Abbasnejad, E., & Hengel, A. V. D. (2020). Unshuffling Data for Improved Generalization.. CoRR, abs/2002.11894.
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 Scopus4 WoS1
2020	Zhang, L., Wang, P., Liu, L., Shen, C., Wei, W., Zhang, Y., & van den Hengel, A. (2020). Towards Effective Deep Embedding for Zero-Shot Learning. IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, 30(9), 2843-2852. DOI Scopus1
2020	Chen, Q., Wu, Q., Chen, J., Wu, Q., Van Den Hengel, A., & Tan, M. (2020). Scripted Video Generation with a Bottom-Up Generative Adversarial Network. IEEE Transactions on Image Processing, 29, 7454-7467. DOI Scopus1 WoS1
2020	Shevchenko, V., Teney, D., Dick, A. R., & Hengel, A. V. D. (2020). Visual Question Answering with Prior Class Semantics.. CoRR, abs/2005.01239.
2020	Teney, D., Kafle, K., Shrestha, R., Abbasnejad, E., Kanan, C., & Hengel, A. V. D. (2020). On the Value of Out-of-Distribution Testing: An Example of Goodhart's Law.. CoRR, abs/2005.09241.
2020	Abbasnejad, M. E., Shi, J., 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
2020	Zhang, L., Wang, P., Shen, C., Liu, L., Wei, W., Zhang, Y., & van den Hengel, A. (2020). Adaptive importance learning for improving lightweight image super-resolution network. International Journal of Computer Vision, 128(2), 479-499. DOI Scopus6 WoS5
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
2019	Teney, D., Wang, P., Cao, J., Liu, L., Shen, C., & Hengel, A. V. D. (2019). V-PROM: A Benchmark for Visual Reasoning Using Visual Progressive Matrices.. CoRR, abs/1907.12271.
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 Scopus9 WoS9
2019	Zhang, L., Wang, P., Wei, W., Lu, H., Shen, C., van den Hengel, A., & Zhang, Y. (2019). Unsupervised domain adaptation using robust class-wise matching. IEEE Transactions on Circuits and Systems for Video Technology, 29(5), 1339-1349. DOI Scopus9 WoS7
2019	Wu, Z., Shen, C., & van den Hengel, A. (2019). Wider or Deeper: Revisiting the ResNet Model for Visual Recognition. Pattern Recognition, 90, 119-133. DOI Scopus151 WoS99
2019	Teney, D., Abbasnejad, E., & Hengel, A. V. D. (2019). On Incorporating Semantic Prior Knowlegde in Deep Learning Through Embedding-Space Constraints.. CoRR, abs/1909.13471.
2019	Kellett, J., Barreto, R., Van Den Hengel, A., & Vogiatzis, N. (2019). How might autonomous vehicles impact the city? The case of commuting to central Adelaide. Urban Policy and Research, 37(4), 442-457. DOI Scopus3 WoS3
2019	Ward, B., Brien, C., Oakey, H., Pearson, A., Negrão, S., Schilling, R. K., . . . Van Den Hengel, A. (2019). High‐throughput 3D modelling to dissect the genetic control of leaf elongation in barley (Hordeum vulgare). The Plant Journal, 98(3), 555-570. DOI Scopus5 WoS5 Europe PMC5
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 Scopus46 WoS54
2018	Lu, H., Shen, C., Cao, Z., Xiao, Y., & Van Den Hengel, A. (2018). An embarrassingly simple approach to visual domain adaptation. IEEE Transactions on Image Processing, 27(7), 3403-3417. DOI Scopus34 WoS27 Europe PMC2
2018	Wu, Q., Shen, C., Wang, P., Dick, A., & van den Hengel, A. (2018). Image captioning and visual question answering based on attributes and external knowledge. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(6), 1367-1381. DOI Scopus106 WoS68 Europe PMC1
2018	Hu, Q., Wang, P., Shen, C., Van Den Hengel, A., & Porikli, F. (2018). Pushing the limits of deep CNNs for pedestrian detection. IEEE Transactions on Circuits and Systems for Video Technology, 28(6), 1358-1368. DOI Scopus31 WoS24
2017	Liu, L., Wang, P., Shen, C., Wang, L., Van Den Hengel, A., Wang, C., & Shen, H. (2017). Compositional model based Fisher vector coding for image classiﬁcation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(12), 2335-2348. DOI Scopus27 WoS17
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 Scopus8 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 Scopus10 WoS9
2017	Abbasnejad, M., Shi, Q., Abbasnejad, I., Hengel, A., & Dick, A. (2017). Bayesian Conditional Generative Adverserial Networks.. CoRR, abs/1706.05477.
2017	Qiao, R., Liu, L., Shen, C., & van den Hengel, A. (2017). Learning discriminative trajectorylet detector sets for accurate skeleton-based action recognition. Pattern Recognition, 66, 202-212. DOI Scopus35 WoS27
2017	Wu, L., Chunhua, S., & Van Den Hengel, A. (2017). Deep linear discriminant analysis on fisher networks: a hybrid architecture for person re-identification. Pattern Recognition, 65, 238-250. DOI Scopus126 WoS98
2017	Wang, P., Shen, C., Van Den Hengel, A., & Torr, P. (2017). Large-scale binary quadratic optimization using semidefinite relaxation and applications. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(3), 470-485. DOI Scopus14 WoS11
2017	Paisitkriangkrai, S., Wu, L., Shen, C., & van den Hengel, A. (2017). Structured learning of metric ensembles with application to person re-identification. Computer Vision and Image Understanding, 156, 51-65. DOI Scopus8 WoS5
2017	Ball, D., Upcroft, B., van Henten, E., van den Hengel, A., Tokekar, P., & Das, J. (2017). JFR Special Issue on Agricultural Robotics. Journal of Field Robotics, 34(6), 1037-1038. DOI
2017	Wang, P., Wu, Q., Shen, C., Dick, A., & Van Den Hengel, A. (2017). FVQA: fact-based Visual Question Answering. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(10), 2413-2427. DOI Scopus49 WoS29
2017	Wu, Q., Teney, D., Wang, P., Shen, C., Dick, A. R., & Hengel, A. V. D. (2017). Visual question answering: A survey of methods and datasets.. Comput. Vis. Image Underst., 163, 21-40.
2017	Teney, D., Wu, Q., & Van Den Hengel, A. (2017). Visual Question Answering: a tutorial. IEEE Signal Processing Magazine, 34(6), 63-75. DOI Scopus9 WoS6
2017	Teney, D., & Hengel, A. V. D. (2017). Visual Question Answering as a Meta Learning Task.. CoRR, abs/1711.08105.
2017	Wu, Q., Teney, D., Wang, P., Shen, C., Dick, A., & van den Hengel, A. (2017). Visual question answering: a survey of methods and datasets. Computer Vision and Image Understanding, 163, 21-40. DOI Scopus88 WoS54
2017	Li, Y., Liu, L., Shen, C., & Hengel, A. (2017). Mining Mid-level Visual Patterns with Deep CNN Activations. International Journal of Computer Vision, 121(3), 344-364. DOI Scopus19 WoS14
2016	Paisitkriangkrai, S., Sherrah, J., Janney, P., & Van Den Hengel, A. (2016). Semantic labeling of aerial and satellite imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(7), 2868-2881. DOI Scopus78 WoS63
2016	Liu, F., Shen, C., Reid, I., & Van Den Hengel, A. (2016). Online unsupervised feature learning for visual tracking. Image and Vision Computing, 51, 84-94. DOI Scopus11 WoS10
2016	Paisitkriangkrai, S., Shen, C., & Van Den Hengel, A. (2016). Pedestrian detection with spatially pooled features and structured ensemble learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(6), 1243-1257. DOI Scopus79 WoS60 Europe PMC4
2016	Wigley, P., Everitt, P., Van Den Hengel, A., Bastian, J., Sooriyabandara, M., Mcdonald, G., . . . Hush, M. (2016). Fast machine-learning online optimization of ultra-cold-atom experiments. Scientific Reports, 6(1), 25890-1-25890-6. DOI Scopus72 WoS72 Europe PMC11
2016	Wang, P., Shen, C., van den Hengel, A., & Torr, P. (2016). Efficient semidefinite branch-and-cut for MAP-MRF inference. International Journal of Computer Vision, 117(3), 269-289. DOI Scopus2 WoS1
2016	Shinmoto Torres, R., Visvanathan, R., Hoskins, S., Van den Hengel, A., & Ranasinghe, D. (2016). Effectiveness of a batteryless and wireless wearable sensor system for identifying bed and chair exits in healthy older people. Sensors, 16(4), 546-1-546-17. DOI Scopus15 WoS11 Europe PMC6
2016	Hu, Q., Paisitkriangkrai, S., Shen, C., Van Den Hengel, A., & Porikli, F. (2016). Fast detection of multiple objects in traffic scenes with a common detection framework. IEEE Transactions on Intelligent Transportation Systems, 17(4), 1002-1014. DOI Scopus51 WoS35
2016	Teney, D., & Hengel, A. (2016). Zero-Shot Visual Question Answering.. CoRR, abs/1611.05546.
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 Scopus35 WoS30
2016	Liu, L., Shen, C., & van den Hengel, A. (2016). Cross-convolutional-layer pooling for image recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(11), 2305-2313. DOI Scopus38 WoS30 Europe PMC3
2016	Lin, G., Shen, C., Hengel, A., & Reid, I. (2016). Exploring context with deep structured models for semantic segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(6), 1352-1366. DOI Scopus33 WoS32 Europe PMC2
2015	Szpak, Z., Chojnacki, W., & van den Hengel, A. (2015). Guaranteed ellipse fitting with a confidence region and an uncertainty measure for centre, axes, and orientation. Journal of Mathematical Imaging and Vision, 52(2), 173-199. DOI Scopus22 WoS20
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 Scopus2 WoS1
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 Scopus111 WoS96 Europe PMC13
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 Scopus6 WoS5
2015	Lin, G., Shen, C., & van den Hengel, A. (2015). Supervised hashing using graph cuts and boosted decision trees. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37(11), 2317-2331. DOI Scopus59 WoS50 Europe PMC6
2015	Chojnacki, W., Szpak, Z., Brooks, M., & van Den Hengel, A. (2015). Enforcing consistency constraints in uncalibrated multiple homography estimation using latent variables. Machine Vision and Applications, 26(2-3), 401-422. DOI Scopus3 WoS4
2014	Shen, C., Lin, G., & van den Hengel, A. (2014). StructBoost: boosting methods for predicting structured output variables. IEEE Transactions on Pattern Analysis and Machine Intelligence, 36(10), 2089-2103. DOI Scopus11 WoS4 Europe PMC2
2014	Paisitkriangkrai, S., Shen, C., & Van Den Hengel, A. (2014). Large-margin learning of compact binary image encodings. IEEE Transactions on Image Processing, 23(9), 4041-4054. DOI Scopus1 WoS1 Europe PMC1
2014	Shen, C., Kim, J., Liu, F., Wang, L., & van den Hengel, A. (2014). Efficient dual approach to distance metric learning. IEEE Transactions on Neural Networks and Learning Systems, 25(2), 394-406. DOI Scopus27 WoS22 Europe PMC6
2014	Paisitkriangkrai, S., Shen, C., & van den Hengel, A. (2014). Asymmetric pruning for learning cascade detectors. IEEE Transactions on Multimedia, 16(5), 1254-1267. DOI Scopus4 WoS2
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
2014	Li, Y., Jia, W., Shen, C., & van den Hengel, A. (2014). Characterness: an indicator of text in the wild. IEEE Transactions on Image Processing, 23(4), 1666-1677. DOI Scopus70 WoS58 Europe PMC5
2014	Szpak, Z., Chojnacki, W., Eriksson, A., & van den Hengel, A. (2014). Sampson distance based joint estimation of multiple homographies with uncalibrated cameras. Computer Vision and Image Understanding, 125, 200-213. DOI Scopus14 WoS12
2014	Shen, F., Shen, C., Hill, R., Van Den Hengel, A., & Tang, Z. (2014). Fast approximate L∞ minimization: speeding up robust regression. Computational Statistics & Data Analysis, 77, 25-37. DOI Scopus12 WoS11
2014	Paisitkriangkrai, S., Shen, C., & Van Den Hengel, A. (2014). A scalable stagewise approach to large-margin multiclass loss-based boosting. IEEE Transactions on Neural Networks and Learning Systems, 25(5), 1002-1013. DOI Scopus8 WoS9 Europe PMC2
2013	Li, X., Dick, A., Shen, C., Van Den Hengel, A., & Wang, H. (2013). Incremental learning of 3D-DCT compact representations for robust visual tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(4), 863-881. DOI Scopus76 WoS62 Europe PMC6
2013	Shen, C., Wang, P., Paisitkriangkrai, S., & Van Den Hengel, A. (2013). Training effective node classifiers for cascade classification. International Journal of Computer Vision, 103(3), 326-347. DOI Scopus26 WoS21
2013	Luo, L., Shen, C., Zhang, C., & Van Den Hengel, A. (2013). Shape similarity analysis by self-tuning locally constrained mixed-diffusion. IEEE Transactions on Multimedia, 15(5), 1174-1183. DOI Scopus19 WoS18
2013	Shen, C., Li, H., & Van Den Hengel, A. (2013). Fully corrective boosting with arbitrary loss and regularization. Neural Networks, 48, 44-58. DOI Scopus7 WoS6 Europe PMC2
2013	Li, X., Dick, A., Shen, C., Zhang, Z., Van Den Hengel, A., & Wang, H. (2013). Visual tracking with spatio-temporal Dempster-Shafer information fusion. IEEE Transactions on Image Processing, 22(8), 3028-3040. DOI Scopus27 WoS28 Europe PMC4
2013	Chojnacki, W., & Van Den Hengel, A. (2013). On the dimension of the set of two-view multi-homography matrices. Complex Analysis and Operator Theory, 7(2), 465-484. DOI Scopus4 WoS5
2013	Shen, F., Shen, C., Van Den Hengel, A., & Tang, Z. (2013). Approximate least trimmed sum of squares fitting and applications in image analysis. IEEE Transactions on Image Processing, 22(5), 1836-1847. DOI Scopus26 WoS19
2013	Chen, Y., Dick, A., Li, X., & Van Den Hengel, A. (2013). Spatially aware feature selection and weighting for object retrieval. Image and Vision Computing, 31(12), 935-948. DOI Scopus11 WoS6
2013	Li, X., Hu, W., Shen, C., Zhang, Z., Dick, A., & Van Den Hengel, A. (2013). A survey of appearance models in visual object tracking. ACM Transactions on Intelligent Systems and Technology, 4(58), 1-48. DOI Scopus547 WoS491
2012	Chen, Y., Li, X., Dick, A., & Van Den Hengel, A. (2012). Boosting object retrieval with group queries. IEEE Signal Processing Letters, 19(11), 765-768. DOI Scopus9 WoS7
2012	Eriksson, A., & Van Den Hengel, A. (2012). Efficient computation of robust weighted low-rank matrix approximations using the L₁ norm. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(9), 1681-1690. DOI Scopus47 WoS30 Europe PMC7
2012	Shen, C., Kim, J., Wang, L., & Van Den Hengel, A. (2012). Positive semidefinite metric learning using boosting-like algorithms. Journal of Machine Learning Research (Print), 13(0), 1007-1036. Scopus67 WoS52
2011	Yoon, H., Van Den Hengel, A., Reitmayr, G., Lifton, J., Lee, S., & Woo, W. (2011). Toward a Digital Ecosystem: International Symposium on Ubiquitous Virtual Reality 2010. IEEE Pervasive Computing, 10(2), 90-93. DOI Scopus1
2010	Stothard, P., & Van Den Hengel, A. (2010). Development of serious computer game based training module and its integration into working at heights mine site induction - Part I. Transactions of the Institution of Mining and Metallurgy Section A-Mining Industry, 119(2), 68-78. DOI Scopus4
2010	Stothard, P., & Van Den Hengel, A. (2010). Development of a serious computer game based training module and its integration into working at heights mine site induction - Paper II. Transactions of the Institution of Mining and Metallurgy Section A-Mining Industry, 119(4), 199-204. DOI Scopus1
2009	Van Den Hengel, A., Sale, D., & Dick, A. (2009). SecondSkin: An interactive method for appearance transfer. Computer Graphics Forum, 28(7 Sp Iss), 1735-1744. DOI Scopus1 WoS1
2008	Flint, A., Dick, A., & Van Den Hengel, A. (2008). Local 3D structure recognition in range images. IET Computer Vision, 2(4), 208-217. DOI Scopus30 WoS28
2008	Detmold, H., Van Den Hengel, A., Dick, A., Falkner, K., Munro, D., & Morrison, R. (2008). Middleware for distributed video surveillance. IEEE Distributed Systems Online, 9(2), 1-11. DOI Scopus29
2008	Van Den Hengel, A., & Dick, A. (2008). Image based modelling with VideoTrace. Computer Graphics, 42(2), 2-1-2-8. DOI
2007	Van Den Hengel, A., Dick, A., Thormaehlen, T., Ward, B., & Torr, P. (2007). Video Trace: rapid interactive scene modelling from video. ACM Transactions on Graphics, 26(3), 86-1-86-5. DOI Scopus91 WoS72
2007	Shen, C., Brooks, M., & Van Den Hengel, A. (2007). Fast global kernel density mode seeking: Applications to localization and tracking. IEEE Transactions on Image Processing, 16(5), 1457-1469. DOI Scopus69 WoS50 Europe PMC5
2005	Chojnacki, W., Brooks, M., Van Den Hengel, A., & Gawley, D. (2005). FNS, CFNS and HEIV: A unifying approach. Journal of Mathematical Imaging and Vision, 23(2), 175-183. DOI Scopus28 WoS19
2004	Chojnacki, W., Brooks, M., Van Den Hengel, A., & Gawley, D. (2004). From FNS to HEIV: A link between two vision parameter estimation methods. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(2), 264-268. DOI Scopus28 WoS24 Europe PMC2
2004	Chojnacki, W., Brooks, M., Van Den Hengel, A., & Gawley, D. (2004). A new constrained parameter estimator for computer vision applications. Image and Vision Computing, 22(2), 85-91. DOI Scopus34 WoS24
2003	Chojnacki, W., Brooks, M., Van Den Hengel, A., & Gawley, D. (2003). Revisiting Hartley's normalized eight-point algorithm. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(9), 1172-1177. DOI Scopus52 WoS40
2001	Chojnacki, W., Brooks, M., & Van Den Hengel, A. (2001). Rationalising the renormalisation method of Kanatani. Journal of Mathematical Imaging and Vision, 14(1), 21-38. DOI Scopus40 WoS34
2000	Chojnacki, W., Brooks, M., Van Den Hengel, A., & Gawley, D. (2000). On the fitting of surfaces to data with covariances. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(11), 1294-1303. DOI Scopus132 WoS107
2000	Kanatani, K., Shimizu, Y., Ohta, N., Brooks, M., Chojnacki, W., & Van Den Hengel, A. (2000). Fundamental matrix from optical flow: optimal computation and reliability evaluation. Journal of Electronic Imaging, 9(2), 194-202. DOI Scopus9 WoS7
—	Wu, Z., Shen, C., & Hengel, A. V. D. (n.d.). Real-time Semantic Image Segmentation via Spatial Sparsity.
—	Zhuang, B., Wu, Q., Shen, C., Reid, I., & Hengel, A. V. D. (n.d.). Care about you: towards large-scale human-centric visual relationship detection.
—	Qiao, R., Liu, L., Shen, C., & Hengel, A. V. D. (n.d.). Visually Aligned Word Embeddings for Improving Zero-shot Learning.
—	Wu, L., Shen, C., & Hengel, A. V. D. (n.d.). PersonNet: Person Re-identification with Deep Convolutional Neural Networks.
—	Wu, L., Shen, C., & Hengel, A. V. D. (n.d.). Deep Recurrent Convolutional Networks for Video-based Person Re-identification: An End-to-End Approach.
—	Wu, Z., Shen, C., & Hengel, A. V. D. (n.d.). Bridging Category-level and Instance-level Semantic Image Segmentation.
—	Wu, Z., Shen, C., & Hengel, A. V. D. (n.d.). High-performance Semantic Segmentation Using Very Deep Fully Convolutional Networks.
—	Wang, P., Liu, L., Shen, C., Hengel, A. V. D., & Shen, H. T. (n.d.). Hi Detector, What's Wrong with that Object? Identifying Irregular Object From Images by Modelling the Detection Score Distribution.
—	Shen, C., Paisitkriangkrai, S., & Hengel, A. V. D. (n.d.). A Direct Approach to Multi-class Boosting and Extensions.
—	Shen, C., Wang, P., & Hengel, A. V. D. (n.d.). Optimally Training a Cascade Classifier.
—	He, T., Shen, C., & Hengel, A. V. D. (n.d.). DyCo3D: Robust Instance Segmentation of 3D Point Clouds through Dynamic Convolution.
—	Pang, G., Pham, N. T. A., Baker, E., Bentley, R., & Hengel, A. V. D. (n.d.). Deep Multi-task Learning for Depression Detection and Prediction in Longitudinal Data.

Book Chapters

Year	Citation
2019	Hunter, J., Jateff, E., & van den Hengel, A. (2019). Using Digital Visualization of Archival Sources to Enhance Archaeological Interpretation of the ‘Life History’ of Ships: The Case Study of HMCS/HMAS Protector. In J. K. McCarthy, J. Benjamin, T. Winton, & W. van Duivenwoorde (Eds.), 3D Recording and Interpretation for Maritime Archaeology (Vol. 31, pp. 89-101). Cham, Switzerland: Springer Nature. DOI Scopus1
2014	Dick, A., Hengel, A., & Detmold, H. (2014). Large-scale camera topology mapping: application to re-identification. In S. Gong, M. Cristani, S. Yan, & C. Loy (Eds.), Person Re-Identification (Vol. 56, pp. 391-411). London: Springer. DOI Scopus4
2011	Van Den Hengel, A., Dick, A., Detmold, H., Cichowski, A., Madden, C., & Hill, R. (2011). Distributed camera overlap estimation - Enabling large scale surveillance. In P. Remagnino, D. Monekosso, & L. Jain (Eds.), Innovations in Defence Support Systems - 3: Intelligent Paradigms in Security (Vol. 336, 1 ed., pp. 147-182). Germany: Springer. DOI

Conference Papers

Year	Citation
2020	Wang, X., Liu, Y., Shen, C., Ng, C. C., Luo, C., Jin, L., . . . Wang, L. (2020). On the general value of evidence, and bilingual scene-text visual question answering. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2020) (pp. 10123-10132). online: IEEE. DOI Scopus2
2020	Pang, G., Yan, C., Shen, C., van den Hengel, A., & Bai, X. (2020). Self-trained deep ordinal regression for end-to-end video anomaly detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2020) (pp. 12170-12179). online: IEEE. DOI Scopus1
2020	Liao, Z., Liu, L., Wu, Q., Teney, D., Shen, C., Van Den Hengel, A., & Verjans, J. (2020). Medical data inquiry using a question answering model. In Proceedings: 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI 2020) Vol. 2020-April (pp. 1490-1493). online: IEEE. DOI
2020	Teney, D., Wang, P., Cao, J., Liu, L., Shen, C., & Van Den Hengel, A. (2020). V-PROM: A benchmark for visual reasoning using visual progressive matrices. In Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20) Vol. 34 (pp. 12071-12078). Palo Alto, CA: Association for the Advancement of Artificial Intelligence. DOI
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 Scopus2
2020	Qi, Y., Wu, Q., Anderson, P., Wang, X., Wang, W. Y., Shen, C., & Van Den Hengel, A. (2020). Reverie: Remote embodied visual referring expression in real indoor environments. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 9979-9988). online: IEEE. DOI Scopus6
2020	Liao, Z., Wu, Q., Shen, C., Van Den Hengel, A., & Verjans, J. (2020). AIML at VQA-Med 2020: Knowledge Inference via a Skeleton-based Sentence Mapping Approach for Medical Domain Visual Question Answering.. In L. Cappellato, C. Eickhoff, N. Ferro, & A. Névéol (Eds.), CLEF (Working Notes) Vol. 2696 (pp. 1-14). online: CEUR-WS.org.
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
2020	Teney, D., Abbasnejad, M., & Van Den Hengel, A. (2020). Learning what makes a difference from counterfactual examples and gradient supervision. In A. Vedaldi, H. Bischof, T. Brox, & J. -M. Frahm (Eds.), Proceedings of the 16th European Conference on Computer Vision Workshops (ECCV 2020) Vol. 12355 (pp. 580-599). 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 (pp. 1-12). virtual online: NIPS.
2020	Qi, Y., Pan, Z., Zhang, S., van den Hengel, A., & Wu, Q. (2020). Object-and-Action Aware Model for Visual Language Navigation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 12355 LNCS (pp. 303-317). Switzerland: Springer International Publishing. DOI
2019	Snaauw, G., Gong, D., Maicas, G., Hengel, A. V. D., Niessen, W. J., Verjans, J., & Carneiro, G. (2019). End-To-End Diagnosis And Segmentation Learning From Cardiac Magnetic Resonance Imaging.. In ISBI (pp. 802-805). IEEE.
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	Teney, D., & Hengel, A. V. D. (2019). Actively Seeking and Learning From Live Data.. In CVPR (pp. 1940-1949). Computer Vision Foundation / IEEE.
2019	Gong, D., Liu, L., Le, V., Saha, B., Mansour, M., Venkatesh, S., & Van Den Hengel, A. (2019). Memorizing normality to detect anomaly: Memory-augmented deep autoencoder for unsupervised anomaly detection. In Proceedings of the IEEE International Conference on Computer Vision Vol. 2019-October (pp. 1705-1714). online: IEEE. DOI Scopus34 WoS8
2019	Manchin, A., Abbasnejad, E., & Van Den Hengel, A. (2019). Reinforcement learning with attention that works: a self-supervised approach. In T. Gedeon, K. W. Wong, & M. Lee (Eds.), Neural Information Processing: 26th International Conference, ICONIP 2019. Proceedings, Part V Vol. 1143 CCIS (pp. 223-230). Switzerland: Springer. DOI Scopus4
2019	Abbasnejad, M., 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 Scopus5 WoS1
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 Scopus4
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 Scopus15 WoS7
2019	Wang, P., Wu, Q., Cao, J., Shen, C., Gao, L., & Hengel, A. (2019). Neighbourhood watch: Referring expression comprehension via language-guided graph attention networks. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Vol. 2019-June (pp. 1960-1968). online: IEEE. DOI Scopus27 WoS6
2019	Duan, X., Wu, Q., Gan, C., Zhang, Y., Huang, W., Van Den Hengel, A., & Zhu, W. (2019). Watch, reason and code: Learning to represent videos using program. In MM 2019 - Proceedings of the 27th ACM International Conference on Multimedia (pp. 1543-1551). online: ACM. DOI Scopus1 WoS1
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	Li, H., Wang, P., Shen, C., & Van Den Hengel, A. (2019). Visual question answering as reading comprehension. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Vol. 2019-June (pp. 6312-6321). online: IEEE. DOI Scopus10 WoS3
2019	Pang, G., Shen, C., & Van Den Hengel, A. (2019). Deep anomaly detection with deviation networks. In KDD '19: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 353-362). New York: Association of Computing Machinery. DOI Scopus11 WoS4
2019	Snaauw, G., Gong, D., Maicas, G., van den Hengel, A., Niessen, W. J., Verjans, J., & Carneiro, G. (2019). End-to-end diagnosis and segmentation learning from cardiac magnetic resonance imaging. In 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019): Proceedings Vol. 2019-April (pp. 802-805). online: IEEE. DOI Scopus7 WoS6
2019	Abdi, M., Lim, C., Mohamed, S., Nahavandi, S., Abbasnejad, E., & Van Den Hengel, A. (2019). Discriminative clustering of high-dimensional data using generative modeling. In 2018 IEEE 61st International Midwest Symposium on Circuits and Systems (MWSCAS) Vol. 2018-August (pp. 799-802). Windsor, Canada: IEEE. DOI
2019	Teney, D., & Hengel, A. V. D. (2019). Actively seeking and learning from live data. In Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2019) Vol. 2019-June (pp. 1940-1949). online: Computer Vision Foundation / IEEE. DOI Scopus1
2018	Zhuang, B., Wu, Q., Shen, C., Reid, I., & Van Den Hengel, A. (2018). HCVRD: A benchmark for large-scale human-centered visual relationship detection. In 32nd AAAI Conference on Artificial Intelligence, AAAI 2018 (pp. 7631-7638). New Orleans: Association for the Advancement of Artificial Intelligence. Scopus11 WoS3
2018	Wu, Q., Wang, P., Shen, C., Reid, I., & Hengel, A. (2018). Are you talking to me? Reasoned visual dialog generation through adversarial learning. In Proceedings: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2018) (pp. 6106-6115). Salt Lake City, UT: IEEE. DOI Scopus34 WoS9
2018	Siegersma, K. R., Zreik, M., Coroller, T. P., Dweck, M. R., Everett, R. J., Treibel, T., . . . Verjans, J. W. H. (2018). Prediction of the risk of valve surgery and adverse events in patients with aortic stenosis: myocardial tissue characterization with radiomics. In EUROPEAN HEART JOURNAL Vol. 39 (pp. 1129-1130). Munich, GERMANY: OXFORD UNIV PRESS.
2018	Siegersma, K. R., Zreik, M., Coroller, T., Dweck, M. R., Everett, R. J., Treibel, T., . . . Verjans, J. W. H. (2018). Discrimination of fibrotic myocardium from healthy myocardium patients with aortic stenosis: a radiomics approach with machine learning models. In EUROPEAN HEART JOURNAL Vol. 39 (pp. 971-972). Munich, GERMANY: OXFORD UNIV PRESS.
2018	Zhang, J., Wu, Q., Shen, C., Zhang, J., Lu, J., & van den Hengel, A. (2018). Goal-oriented visual question generation via intermediate rewards. In V. Ferrari, M. Hebert, C. Sminchisescu, & Y. Weiss (Eds.), Computer Vision - ECCV 2018: Proceedings, Part V Vol. Lecture Notes in Computer Science; vol. 11209 (pp. 189-204). Munich: Springer. DOI Scopus3 WoS3
2018	Zhuang, B., Wu, Q., Shen, C., Reid, I., & van den Hengel, A. (2018). Parallel attention: a unified framework for visual object discovery through dialogs and queries. In Proceedings: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2018) (pp. 4252-4261). Salt Lake City, UT: IEEE. DOI Scopus18 WoS5
2018	Teney, D., & Van Den Hengel, A. (2018). Visual Question Answering as a meta learning task. In V. Ferrari, M. Hebert, C. Sminchisescu, & Y. Weiss (Eds.), Computer Vision - ECCV 2018: Proceedings, Part XV Vol. 11219 LNCS (pp. 229-245). Munich: Springer. DOI Scopus1
2018	Anderson, P., Wu, Q., Teney, D., Bruce, J., Johnson, M., Sünderhauf, N., . . . Hengel, A. (2018). Vision-and-language navigation: interpreting visually-grounded navigation instructions in real environments. In Proceedings: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2018) Vol. abs/1711.07280 (pp. 3674-3683). Salt Lake City, UT: IEEE. DOI Scopus121 WoS31
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	Ma, C., Shen, C., Dick, A., Wu, Q., Wang, P., Van Den Hengel, A., & Reid, I. (2018). Visual Question Answering with memory-augmented network. In Proceedings: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2018) (pp. 6975-6984). Salt Lake City, Utah: IEEE. DOI Scopus27 WoS9
2018	Teney, D., Anderson, P., He, X., & Van Den Hengel, A. (2018). Tips and tricks for visual question answering: learnings from the 2017 challenge. In Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2018) (pp. 4223-4232). Salt Lake City, USA: IEEE. DOI Scopus72 WoS27
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. Scopus6 WoS4
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.
2017	Wang, P., Wu, Q., Shen, C., Dick, A., & Van Den Hengel, A. (2017). Explicit knowledge-based reasoning for visual question answering. In C. Sierra (Ed.), Proceedings of the twenty-sixth International Joint Conference on Artificial Intelligence Vol. 0 (pp. 1290-1296). online: IJCAI. DOI Scopus30
2017	Zhu, M., Dick, A., & van den Hengel, A. (2017). Large-scale camera network topology estimation by lighting variation. In J. Blanc-Talon, R. Penne, W. Philips, D. Popescu, & P. Scheunders (Eds.), Advanced Concepts for Intelligent Vision Systems: proceedings Vol. 10617 LNCS (pp. 455-467). Antwerp, Belgium: Springer. DOI
2017	Abbasnejad, M., Dick, A., & van den Hengel, A. (2017). Infinite variational autoencoder for semi-supervised learning. In Proceedings: 30th IEEE Conference on Computer Vision and Pattern Recognition Vol. 2017-January (pp. 781-790). Honolulu: IEEE. DOI Scopus26 WoS13
2017	Wang, P., Wu, Q., Shen, C., & van den Hengel, A. (2017). The VQA-machine: learning how to use existing vision algorithms to answer new questions. In Proceedings: 30th IEEE Conference on Computer Vision and Pattern Recognition Vol. 2017-January (pp. 3909-3918). Honolulu: IEEE. DOI Scopus42 WoS12
2017	Wang, P., Liu, L., Shen, C., Huang, Z., van den Hengel, A., & Shen, H. (2017). Multi-attention network for one shot learning. In Proceedings of the 30th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017) Vol. 2017-January (pp. 6212-6220). Online: IEEE. DOI Scopus39 WoS17
2017	Li, Y., Lin, G., Zhuang, B., Liu, L., Shen, C., & van den Hengel, A. (2017). Sequential person recognition in photo albums with a recurrent network. In Proceedings: 30th IEEE Conference on Computer Vision and Pattern Recognition Vol. 2017-January (pp. 5660-5668). online: IEEE. DOI Scopus20 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 Scopus16 WoS14
2017	Lu, H., Zhang, L., Cao, Z., Wei, W., Xian, K., Shen, C., & Hengel, A. (2017). When Unsupervised Domain Adaptation Meets Tensor Representations. In Proceedings: 2017 IEEE International Conference on Computer Vision Vol. 2017-October (pp. 599-608). Venice, Italy: IEEE. DOI Scopus29 WoS14
2017	Johnston, A., Garg, R., Carneiro, G., Reid, I., & van den Hengel, A. (2017). Scaling CNNs for high resolution volumetric reconstruction from a single image. In Proceedings of the IEEE International Conference on Computer Vision Workshop (ICCVW 2017) Vol. 2018-January (pp. 930-939). Piscataway, NJ: IEEE. DOI Scopus17 WoS9
2017	Teney, D., Liu, L., & van den Hengel, A. (2017). Graph-structured representations for visual question answering. In Proceedings of the 30th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017) Vol. 2017-January (pp. 3233-3241). Online: IEEE. DOI Scopus79 WoS17
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 Scopus96 WoS19
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 WoS11
2016	Li, Y., Liu, L., Shen, C., & van den Hengel, A. (2016). Image co-localization by mimicking a good detector’s confidence score distribution. In B. Leibe, J. Matas, N. Sebe, & M. Welling (Eds.), Proceedings of the 14th European Conference on Computer Vision, Part II Vol. 9906 LNCS (pp. 19-34). Amsterdam, Netherlands: Springer International Publishing. DOI Scopus24 WoS19
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. Scopus2 WoS2
2016	Teney, D., Liu, L., & Hengel, A. V. D. (2016). Graph-Structured Representations for Visual Question Answering.. In CoRR Vol. abs/1609.05600.
2016	Wang, P., Liu, L., Shen, C., Huang, Z., Van Den Hengel, A., & Shen, H. (2016). What's wrong with that object? Identifying images of unusual objects by modelling the detection score distribution. In Proceedings of the 29th IEEE Computer Society Conference on Computer Vision and Pattern Recognition Vol. 2016-December (pp. 1573-1581). Las Vegas, NV: IEEE. DOI Scopus6 WoS3
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 Scopus28 WoS14
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 Scopus42 WoS30
2016	Wu, Q., Wang, P., Shen, C., Dick, A., & Van Den Hengel, A. (2016). Ask me anything: free-form visual question answering based on knowledge from external sources. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Vol. 2016-December (pp. 4622-4630). Las Vegas, NV: IEEE. DOI Scopus153 WoS68
2016	Qiao, R., Liu, L., Shen, C., & van den Hengel, A. (2016). Less is more: zero-shot learning from online textual documents with noise suppression. In Proceedings of the 29th IEEE Computer Society Conference on Computer Vision and Pattern Recognition Vol. 2016-December (pp. 2249-2257). Las Vegas, NV: IEEE. DOI Scopus76 WoS42
2016	Lin, G., Shen, C., Van Den Hengel, A., & Reid, I. (2016). Efficient piecewise training of deep structured models for semantic segmentation. In Proceedings of the 29th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2016) Vol. 2016-December (pp. 3194-3203). Las Vegas, NV: IEEE. DOI Scopus411 WoS14
2016	Wu, Q., Shen, C., Liu, L., Dick, A., & Van Den Hengel, A. (2016). What value do explicit high level concepts have in vision to language problems?. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Vol. 2016-December (pp. 203-212). Las Vegas, NV: IEEE. DOI Scopus256 WoS146
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
2015	Ward, B., Bastian, J., van den Hengel, A., Pooley, D., Rajendra, B., Berger, B., & Tester, M. (2015). A model-based approach to recovering the structure of a plant from images. In L. Agapito, M. Bronstein, & C. Rother (Eds.), Proceedings of the 13th European Conference on Computer Vision Workshops (ECCV 2014), as published in Lecture Notes in Computer Science Vol. 8928 (pp. 215-230). Switzerland: Springer International Publishing. DOI Scopus2 WoS2
2015	Zhu, M., Dick, A., & Van Den Hengel, A. (2015). Camera network topology estimation by lighting variation. In Proceedings of the 2015 International Conference on Digital Image Computing: Techniques and Applications (pp. 238-243). Adelaide, Australia: IEEE. DOI Scopus2
2015	Lin, G., Shen, C., Reid, I., & Van Den Hengel, A. (2015). Deeply learning the messages in message passing inference. In C. Cortes, N. Lawrence, D. Lee, M. Sugiyama, & R. Garnett (Eds.), Advances in Neural Information Processing Systems 28: 29th Annual Conference on Neural Information Processing Systems 2015 Vol. 2015-January (pp. 361-369). Montreal: Neural Information Processing Systems. Scopus27
2015	Szpak, Z., Chojnacki, W., & Van Den Hengel, A. (2015). Robust multiple homography estimation: an ill-solved problem. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Vol. 07-12-June-2015 (pp. 2132-2141). Boston, MA: IEEE. DOI Scopus12 WoS8
2015	Paisitkriangkrai, S., Sherrah, J., Janney, P., & Van Den Hengel, A. (2015). Effective semantic pixel labelling with convolutional networks and Conditional Random Fields. In Proceedings of the Computer Society Conference on Computer Vision and Pattern Recognition Workshops Vol. 2015-October (pp. 36-43). Boston, MA: IEEE. DOI Scopus136 WoS14
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 Scopus622 WoS328
2015	Faulkner, H., Shehu, E., Szpak, Z., Chojnacki, W., Tapamo, J., Dick, A., & Van Den Hengel, A. (2015). A study of the region covariance descriptor: impact of feature selection and image transformations. In Proceedings of the International Conference on Digital Image Computing: Techniques and Applications (pp. 1-8). Adelaide, SA.: IEEE. DOI Scopus5
2015	Li, Y., Liu, L., Shen, C., & Van Den Hengel, A. (2015). Mid-level deep pattern mining. In Proceedings of the 2015 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Vol. 07-12-June-2015 (pp. 971-980). Boston, MA: IEEE. DOI Scopus59 WoS35
2015	Wang, P., Shen, C., & Van Den Hengel, A. (2015). Efficient SDP inference for fully-connected CRFs based on low-rank decomposition. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Vol. 07-12-June-2015 (pp. 3222-3231). Boston, MA: IEEE. DOI Scopus9 WoS2
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 Scopus26 WoS15
2015	Paisitkriangkrai, S., Shen, C., & Van Den Hengel, A. (2015). Learning to rank in person re-identification with metric ensembles. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Vol. 07-12-June-2015 (pp. 1846-1855). Boston, MA: IEEE. DOI Scopus319 WoS202
2015	Liu, L., Shen, C., & van den Hengel, A. (2015). The treasure beneath convolutional layers: cross-convolutional-layer pooling for image classification. In Proceedings of the 2015 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Vol. 07-12-June-2015 (pp. 4749-4757). Boston, MA: IEEE. DOI Scopus115 WoS67
2015	Li, B., Shen, C., Dai, Y., Van Den Hengel, A., & He, M. (2015). Depth and surface normal estimation from monocular images using regression on deep features and hierarchical CRFs. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Vol. 07-12-June-2015 (pp. 1119-1127). Boston, MA: IEEE. DOI Scopus285 WoS189
2015	Van Den Hengel, A., Russell, C., Dick, A., Bastian, J., Pooley, D., Fleming, L., & Agapito, L. (2015). Part-based modelling of compound scenes from images. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Vol. 07-12-June-2015 (pp. 878-886). Boston, MA: IEEE. DOI Scopus9 WoS5
2014	Li, B., Dai, Y., He, M., & Van Den Hengel, A. (2014). A relaxation method to articulated trajectory reconstruction from monocular image sequence. In Proceedings: 2014 IEEE China Summit & International Conference on Signal and Information Processing (pp. 389-393). Xi'an, China: IEEE. DOI Scopus2
2014	Liu, L., Shen, C., Wang, L., Van Den Hengel, A., & Wang, C. (2014). Encoding high dimensional local features by sparse coding based fisher vectors. In Proceedings of the 27th International Conference on Neural Information Processing Systems Vol. 2 (pp. 1143-1151). Online: MIT Press. Scopus56 WoS1
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 Scopus294 WoS180
2014	Paisitkriangkrai, S., Shen, C., & Van Den Hengel, A. (2014). Strengthening the effectiveness of pedestrian detection with spatially pooled features. In Computer Vision - ECCV 2014, 13th European Conference, Zurich, Switzerland, September 6-12, 2014: Proceedings, Part IV Vol. 8692 LNCS (pp. 546-561). Zurich, Switzerland: Springer International Publishing. DOI Scopus112 WoS90
2013	Lin, G., Shen, C., Suter, D., & Van Den Hengel, A. (2013). A general two-step approach to learning-based hashing. In Proceedings of the 2013 IEEE International Conference on Computer Vision, ICCV 2013 (pp. 1-13). USA: IEEE Computer Society. DOI Scopus138 WoS97
2013	Li, Y., Shen, C., Jia, W., & Van Den Hengel, A. (2013). Leveraging surrounding context for scene text detection. In Proceedings of the IEEE 2013 International Conference on Image Processing, ICIP 2013 (pp. 2264-2268). USA: IEEE. DOI Scopus14 WoS11
2013	Li, X., Li, Y., Shen, C., Dick, A., & Van Den Hengel, A. (2013). Contextual hypergraph modeling for salient object detection. In Proceedings of the 2013 IEEE International Conference on Computer Vision, ICCV 2013 (pp. 3328-3335). USA: IEEE Computer Society. DOI Scopus160 WoS118
2013	Lin, G., Shen, C., & Van Den Hengel, A. (2013). Approximate constraint generation for efficient structured boosting. In Proceedings of the 2013 IEEE International Conference on Image Processing (pp. 4287-4291). USA: IEEE. DOI
2013	Zhang, C., Bastian, J., Shen, C., Van Den Hengel, A., & Shen, T. (2013). Extended depth-of-field via focus stacking and graph cuts. In Proceedings of the 2013 IEEE Conference on Image Processing, ICIP 2013 (pp. 1272-1276). USA: IEEE. DOI Scopus9 WoS10
2013	Paisitkriangkrai, S., Shen, C., & Van Den Hengel, A. (2013). Efficient pedestrian detection by directly optimizing the partial area under the ROC curve. In Proceedings of the IEEE International Conference on Computer Vision, ICCV 2013 (pp. 1057-1064). USA: IEEE. DOI Scopus35 WoS18
2013	Li, X., Lin, G., Shen, C., Van Den Hengel, A., & Dick, A. (2013). Learning hash functions using column generation. In Proceedings of the 30th International Conference on Machine Learning, IMLS 2013 (pp. 1-9). online: IMCL. Scopus96
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 Scopus185 WoS136
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 Scopus25 WoS10
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 Scopus177 WoS130
2013	Wang, P., Shen, C., & Van Den Hengel, A. (2013). A fast semidefinite approach to solving binary quadratic problems. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 1312-1319). United States: IEEE. DOI Scopus18 WoS7
2013	Li, X., Shen, C., Dick, A., & Van Den Hengel, A. (2013). Learning compact binary codes for visual tracking. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 2419-2426). United States: IEEE. DOI Scopus74 WoS55
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. Scopus29
2012	Szpak, Z., Chojnacki, W., & Van Den Hengel, A. (2012). A comparison of ellipse fitting methods and implications for multiple-view geometry estimation. In Proceedings of Digital Image Computing Techniques and Applications, DICTA 2012 (pp. 1-8). USA: IEEE. DOI Scopus9
2012	Lin, G., Shen, C., Van Den Hengel, A., & Suter, D. (2012). Fast training of effective multi-class boosting using coordinate descent optimization. In Proceedings of the 11th Asian Conference on Computer Vision, ACCV 2012 Vol. 7725 LNCS (pp. 782-793). Germany: Springer-Verlag. DOI
2012	Szpak, Z., Chojnacki, W., & Van Den Hengel, A. (2012). Guaranteed ellipse fitting with the Sampson distance. In Proceedings of the12th European Conference on Computer Vision, ECCV 2012 Vol. 7576 LNCS (pp. 87-100). Germany: Springer-Verlag. DOI Scopus29 WoS21
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 Scopus24 WoS21
2012	Paisitkriangkrai, S., Shen, C., & Van Den Hengel, A. (2012). Sharing features in multi-class boosting via group sparsity. In Proceedings of 2012 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2012 (pp. 2128-2135). USA: IEEE. DOI Scopus6 WoS3
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 Scopus67 WoS41
2011	Li, X., Dick, A., Wang, H., Shen, C., & Van Den Hengel, A. (2011). Graph mode-based contextual kernels for robust SVM tracking. In Proceedings of IEEE International Conference on Computer Vision (ICCV), 2011 (pp. 1156-1163). USA: IEEE. DOI Scopus34 WoS27
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 Scopus251 WoS172
2011	Chojnacki, W., & Van Den Hengel, A. (2011). A dimensionality result for multiple homography matrices. In 2011 IEEE International Conference on Computer Vision (pp. 2104-2109). 345 E 47TH ST, NEW YORK, NY 10017 USA: IEEE. DOI Scopus5 WoS4
2010	Chen, Y., Dick, A., & Van Den Hengel, A. (2010). Image retrieval with a visual thesaurus. In Proceedings of 2010 International Conference on Digital Image Computing: Techniques and Applications (DICTA 2010) (pp. 8-14). USA: IEEE. DOI Scopus2
2010	Van Den Hengel, A. (2010). Image-based modelling for augmenting reality. In Proceedings of 2010 International Symposium on Ubiquitous Virtual Reality, ISUVR 2010 (pp. 1-4). USA: IEEE. DOI Scopus2
2010	Bastian, J., Ward, B., Hill, R., Van Den Hengel, A., & Dick, A. (2010). Interactive modelling for AR applications. In Proceedings of IEEE International Symposium on Mixed and Augmented Reality 2010 Science and Technology (pp. 199-205). USA: IEEE. DOI Scopus19
2010	Eriksson, A., & Van Den Hengel, A. (2010). Efficient computation of robust low-rank matrix approximations in the presence of missing data using the L₁ norm. In Proceedings of 2010 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR) (pp. 771-778). 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA: IEEE COMPUTER SOC. DOI Scopus154 WoS106
2010	Chojnacki, W., Szpak, Z., Brooks, M., & Van Den Hengel, A. (2010). Multiple homography estimation with full consistency constraints. In Proceedings of DICTA 2010 (pp. 480-485). USA: IEEE. DOI Scopus9
2009	Eriksson, A., & Van Den Hengel, A. (2009). Optimization on the manifold of multiple homographies. In Proceedings of the 2009 IEEE ICCV Workshops (pp. 242-249). USA: IEEE. DOI Scopus5
2009	Van Den Hengel, A., Hill, R., Ward, B., Cichowski, A., Detmold, H., Madden, C., . . . Bastian, J. (2009). Automatic camera placement for large scale surveillance networks. In Proceedings of WACV 2009 (pp. 1-6). USA: IEEE. DOI Scopus22
2009	Van Den Hengel, A., Hill, R., Ward, B., & Dick, A. (2009). In situ image-based modeling. In Proceedings of the 8th International Symposium on Mixed & Augmented Reality 2009 (pp. 107-110). USA: IEEE. DOI Scopus32 WoS18
2009	Shen, C., Kim, J., Wang, L., & Van Den Hengel, A. (2009). Positive Semidefinite Metric Learning with Boosting. In Proceedings of NIPS 2009 (pp. 1651-1660). online: NIPS. Scopus58
2009	Hill, R., Madden, C., Van Den Hengel, A., Detmold, H., & Dick, A. (2009). Measuring latency for video surveillance systems. In Proceedings of 2009 Digital Image Computing: Techniques and Applications (pp. 89-95). USA: IEEE. DOI Scopus21 WoS11
2009	Chojnacki, W., Hill, R., Van Den Hengel, A., & Brooks, M. (2009). Multi-projective Parameter Estimation for Sets of Homogeneous Matrices. In Proceedings of 2009 Digital Image Computing: Techniques and Applications (pp. 119-124). California: IEEE. DOI Scopus2 WoS1
2009	Cichowski, A., Madden, C., Detmold, H., Dick, A., Van Den Hengel, A., & Hill, R. (2009). Tracking hand-off in large surveillance networks. In Proceeding of the 24th International Conference Image and Vision Computing New Zealand (VCNZ 2009) (pp. 276-281). USA: IEEE. DOI Scopus9 WoS6
2009	Detmold, H., Van Den Hengel, A., Dick, A., Madden, C., Cichowski, A., & Hill, R. (2009). Surprisal-aware scheduling of PTZ cameras. In Proceedings of ICDSC 2009 (pp. 1-8). USA: IEEE. DOI Scopus2
2009	Van Den Hengel, A., Detmold, H., Madden, C., Dick, A., Cichowski, A., & Hill, R. (2009). A framework for determining overlap in large scale networks. In Proceedings of ICDSC 2009 (pp. 1-8). USA: IEEE. DOI Scopus2
2009	Cichowski, A., Madden, C., Van Den Hengel, A., Hill, R., Detmold, H., & Dick, A. (2009). Contradiction and correlation for camera overlap estimation. In Carlo Regazzoni (Ed.), Proceedings of the 2009 6th IEEE International Conference on Advanced Video & Signal Based Surveillance (pp. 1-6). USA: IEEE. DOI Scopus1
2008	Van Den Hengel, A., Hill, R., Detmold, H., & Dick, A. (2008). Searching in space and time: a system for forensic analysis of large video repositories. In Proceedings of the 1st international conference on forensic application and techniques in telecommunications, information and multimedia workshop (pp. 1-6). Australia: IEEE. DOI Scopus1
2008	Detmold, H., Van Den Hengel, A., Dick, A., Cichowski, A., Hill, R., Kocadag, E., . . . Munro, D. (2008). Estimating camera overlap in large and growing networks. In Proceedings of the ICDSC 2008 (pp. 1-9). USA: IEEE. DOI Scopus10
2008	Hill, R., Van Den Hengel, A., Dick, A., Cichowski, A., & Detmold, H. (2008). Empirical evaluation of the exclusion approach to estimating camera overlap. In Proceedings of the ICDSC 2008 (pp. 1-9). USA: IEEE. DOI Scopus8
2007	Van Den Hengel, A., Detmold, H., Dick, A., & Hill, R. (2007). Preparing for post-catastrophe video processing. In Priyan Mendis (Ed.), Proceedings of the 2007 RNSA Security Technology Conference (pp. 1-8). Australia: Australian Homeland Security Research Centre.
2007	Van Den Hengel, A., Dick, A., Thormaehlen, T., Ward, B., & Torr, P. (2007). A shape hierarchy for 3D modelling from video. In Andrew Rohl (Ed.), Proceedings of GRAPHITE 2007 (pp. 63-70). Australia: ACM. DOI Scopus2 WoS5
2007	Pooley, D., Brooks, M., & Van Den Hengel, A. (2007). RATSAC: An adaptive method for accelerated robust estimation and its application to video synchronisation. In M. Bottema (Ed.), Proceedings of DICTA 2007 (pp. 294-300). CDROM: IEEE. DOI Scopus1
2007	Van Den Hengel, A., Dick, A., Thormaehlen, T., Ward, B., & Torr, P. (2007). Interactive 3D model completion. In M. Bottema (Ed.), Proceedings of DICTA 2007 (pp. 175-181). CDROM: IEEE. DOI Scopus3
2007	Flint, A., Dick, A., & Van Den Hengel, A. (2007). Thrift: Local 3D structure recognition. In M. Bottema (Ed.), Proceedings of DICTA 2007 (pp. 182-188). CDROM: IEEE. DOI Scopus99
2007	Detmold, H., Van Den Hengel, A., Dick, A., Cichowski, A., Hill, R., Kocadag, E., . . . Munro, D. (2007). Topology estimation for thousand-camera surveillance networks. In B. Rinner, & W. Wolf (Eds.), Proceedings of ICDSC-07 (pp. 195-202). CDROM: IEEE. DOI Scopus27
2007	Van Den Hengel, A., Dick, A., Detmold, H., Cichowski, A., & Hill, R. (2007). Finding camera overlap in large surveillance networks. In Yasushi Yagi (Ed.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 4843 LNCS (PART 1) Vol. 4843 LNCS (pp. 375-384). Germany: Springer. DOI Scopus7 WoS7
2007	Kumar, P., Brooks, M., & Van Den Hengel, A. (2007). An adaptive Bayesian technique for tracking multiple objects. In Ashish Ghosh (Ed.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 4815 LNCS Vol. 4815 LNCS (pp. 657-665). Germany: Springer. DOI Scopus2 WoS1
2007	Van Den Hengel, A., Chojnacki, W., & Brooks, M. (2007). Determining the translational speed of a camera from time-varying optical flow. In B. Jahne (Ed.), Complex motion [electronic resource]: first international workshop, IWCM 2004, Gunzburg, Germany, October 12-14, 2004: revised papers Vol. 3417 LNCS (pp. 1-8). Germany: Springer. DOI Scopus2 WoS2
2007	Chojnacki, W., van den Hengel, A., & Brooks, M. (2007). Generalised Principal Component Analysis: exploiting inherent parameter constraints. In J. Braz, A. Ranchordas, H. Araujo, & J. Jorge (Eds.), Advances in computer graphics and computer vision: International Conferences VISAPP and GRAPP 2006 Vol. 4 CCIS (pp. 217-228). Setubal, Portugal: Springer. DOI Scopus2 WoS1
2007	van den Hengel, A., Dick, A., Thormählen, T., Ward, B., & Torr, P. H. S. (2007). VideoTrace. In ACM SIGGRAPH 2007 papers on - SIGGRAPH '07. ACM Press. DOI
2006	Van Den Hengel, A., Dick, A., Thormaehlen, T., Ward, B., & Torr, P. (2006). Building models of regular scenes from structure-and-motion. In M. Chandler, E. Trucco, & R. Fisher (Eds.), Proceedings of British Machine Vision Conference 2006 (pp. CDROM1-CDROM9). CDROM: BMVA. Scopus8
2006	Chojnacki, W., Van Den Hengel, A., & Brooks, M. (2006). Constrained generalised principal component analysis. In A. Ranchordas, H. Araujo, & B. Encarnacao (Eds.), Proceedings of VISAPP 2006 Vol. 1 (pp. CDROM206-CDROM212). CDROM: INSTICC.
2006	Van Den Hengel, A., Dick, A., Thormaehlen, T., Ward, B., & Torr, P. (2006). Rapid interactive modelling from video with graph cuts. In D. Fellner, & C. Hansen (Eds.), Proceedings of Eurographics 2006 (pp. CDROM1-CDROM4). CDROM: Eurographics Association.
2006	Detmold, H., Dick, A., Falkner, K., Munro, D., Van Den Hengel, A., & Morrison, R. (2006). Middleware for video surveillance networks. In V. Cahill, & S. Michiels (Eds.), Proceedings of MIdSens'06 Vol. 218 (pp. CDROM31-CDROM36). CDROM: ACM Press. DOI Scopus17
2006	Van Den Hengel, A., Dick, A., Thormaehlen, T., Torr, P., & Ward, B. (2006). Fitting multiple models to multiple images with minimal user interaction. In R. Horaud, C. Schnorr, P. Torr, & J. Tsotsos (Eds.), Proceedings of WRUPKV 2006 (pp. CDROM1-CDROM15). CDROM: University of Ljubljana.
2006	Van Den Hengel, A., Dick, A., Thormaehlen, T., Ward, B., & Torr, P. (2006). Hierarchical model fitting to 2D and 3D data. In E. Banissi, M. Sarfraz, M. Huang, & Q. Wu (Eds.), Proceedings of Computer Graphics, Imaging and Visualisation Vol. 2006 (pp. 359-364). USA: IEEE. DOI Scopus1
2006	Detmold, H., Dick, A., Falkner, K., Munro, D., Van Den Hengel, A., & Morrison, R. (2006). Scalable surveillance software architecture. In M. Piccardi, T. Hintz, I. Pavlidis, C. Regazzoni, & X. He (Eds.), Proceedings of AVSS 2006 (pp. CDROM1-CDROM6). CDROM: IEEE. DOI Scopus10
2006	Van Den Hengel, A., Dick, A., & Hill, R. (2006). Activity topology estimation for large networks of cameras. In M. Piccardi, T. Hintz, I. Pavlidis, C. Regazzoni, & X. He (Eds.), Proceedings of AVSS 2006 (pp. CDROM1-CDROM6). CDROM: IEEE. DOI Scopus21
2005	Bastian, J., & Van Den Hengel, A. (2005). Computing surface-based photo-consistency on graphics hardware. In B. Lovell, A. Maeder, T. Caelli, & S. Ourselin (Eds.), Proceedings of the 8th Biennial Conference of the Australian Pattern Recognition Society: 'Digital Image Computing: Techniques and Applications' Vol. 2005 (pp. CD ROM 1-CD ROM 8). CD ROM: IEEE Computer Society. DOI Scopus1
2005	Brooks, M., Dick, A., & Van Den Hengel, A. (2005). Towards intelligent networked video surveillance for the detection of suspicious behaviours. In P. Mendis, J. Lai, & E. Dawson (Eds.), Proceedings of the 2005 Science, Engineering and Technology Summit (pp. 153-161). Curtin, ACT: The Australian Homeland Security Research Centre.
2005	Shen, C., Van Den Hengel, A., & Brooks, M. (2005). Visual tracking via efficient kernel discriminant subspace learning. In C. Regazzoni, & F. De Natale (Eds.), Proceedings of the IEEE International Conference on Image Processing Vol. 2 (pp. 1-4). USA: IEEE. DOI Scopus7
2005	Shen, C., Brooks, M., & Van Den Hengel, A. (2005). Augmented particle filtering for efficient visual tracking. In C. Regazzoni, & F. De Natale (Eds.), Proceedings of the IEEE International Conference on Image Processing Vol. 3 (pp. 1-4). USA: IEEE. DOI Scopus7 WoS3
2005	Shen, C., Brooks, M., & Van Den Hengel, A. (2005). Fast global kernel density mode seeking with application to localisation and tracking. In S. Ma, & H. Shum (Eds.), Proceedings of the Tenth IEEE International Conference on Computer Vision Vol. II (pp. 1516-1523). Los Alamitos, California: IEEE. DOI Scopus35 WoS18
2005	Hill, R., & Van Den Hengel, A. (2005). Experiences with simulated robot soccer as a teaching tool. In Third International Conference on Information Technology and Applications: 4-7 July 2005, Sydney, Australia: proceedings Vol. I (pp. 387-390). Online: IEEE Computer Society. DOI Scopus5 WoS2
2004	Shen, C., Van Den Hengel, A., Dick, A., & Brooks, M. (2004). Enhanced importance sampling: Unscented auxiliary particle filtering for visual tracking. In G. Webb, & X. Yu (Eds.), Proceedings of the 17th Australasian Joint Conference on Artificial Intelligence 2004 Vol. 3339 (pp. 180-191). Berlin, Germany: Springer. DOI Scopus9 WoS5
2004	Shen, C., Van Den Hengel, A., Dick, A., & Brooks, M. (2004). 2D articulated tracking with dynamic Bayesian networks. In D. Wei, H. Wang, Z. Peng, A. Kara, & Y. He (Eds.), Proceedings of the 4th International Conference on Computer and Information Technology 2004 (pp. 1-7). Los Alamitos, California, USA: IEEE. DOI Scopus8 WoS6
2003	Chojnacki, W., Brooks, M., Van Den Hengel, A., & Gawley, D. (2003). FNS, CFNS, and HEIV: extending three vision parameter estimation methods. In C. Sun, H. Talbot, S. Ourselin, & T. Adriaansen (Eds.), Digital Image Computing: Techniques and Applications - Proceedings of the VIIth Biennial Australian Pattern Recognition Society Conference - DICTA 2003 (pp. 449-458). Victoria, Australia: CSIRO Publishing.
2003	Chojnacki, W., Brooks, M., Van Den Hengel, A., & Gawley, D. (2003). FNS and HEIV: relating two vision parameter estimation frameworks. In M. Feretti (Ed.), Proceedings of the 12th International Conference on Image Analysis and Processing 2003 (pp. 152-157). California, USA: IEEE. DOI Scopus2 WoS1
2003	Chojnacki, W., Brooks, M., Van Den Hengel, A., & Gawley, D. (2003). A statistical rationalisation of Hartley's normalised eight-point algorithm. In M. Feretti (Ed.), Proceedings of the 12th International Conference on Image Analysis and Processing 2003 (pp. 334-339). California, USA: IEEE. DOI Scopus4
2003	Van Den Hengel, A., Hill, R., & Brooks, M. (2003). Incorporating constraints into the design of locally identifiable calibration patterns. In L. Torres (Ed.), Proceedings of the IEEE International Conference on Image Processing 2003 Vol. 1 (pp. CDROM 1-CDROM 4). CDROM: IEEE. DOI Scopus1 WoS1
2003	Pooley, D., Brooks, M., Van Den Hengel, A., & Chojnacki, W. (2003). A voting scheme for estimating the synchrony of moving-camera videos. In L. Torres (Ed.), Proceedings of the 2003 IEEE International Conference on Image Processing Vol. 1 (pp. CDROM 1-CDROM 4). CDROM: IEEE. DOI Scopus20 WoS11
2003	Shen, C., Van Den Hengel, A., & Dick, A. (2003). Probabilistic multiple cue intergration for particle filter based tracking. In C. Sun, H. Talbot, S. Ourselin, & T. Adriaansen (Eds.), Proceedings of the 7th Biennial Australian Pattern Recognition Society Conference - DICTA 2003 (pp. 399-408). Australia: CSIRO.
2003	Bastian, J., & Van Den Hengel, A. (2003). Computing image-based reprojection error on graphics hardware. In C. Sun, H. Talbot, S. Ourselin, & T. Adriaansen (Eds.), Proceedings of the 7th Biennial Australian Pattern Recognition Society Conference - DICTA 2003 (pp. 663-676). Australia: CSIRO.
2002	Chojnacki, W., Brooks, M., Van Den Hengel, A., & Gawley, D. (2002). A new approach to constrained parameter estimation applicable to some computer vision problems. In D. Suter (Ed.), Proceedings of the Statistical Methods in Video Processing Workshop (pp. 43-48). Victoria, Australia: Monash University.
2002	Van Den Hengel, A., Chojnacki, W., Brooks, M., & Gawley, D. (2002). A new constrained parameter estimator: experiments in fundamental matrix computation. In P. Rosin, & D. Marshall (Eds.), Proceedings of the 13th British Machine Vision Conference (pp. 468-476). UK: BMVA.
2002	Hengel, A. V. D., Brooks, M. J., Chojnacki, W., & Gawley, D. (2002). A new constrained parameter estimator: experiments in fundamental matrix computation. In P. L. Rosin, & A. D. Marshall (Eds.), Proceedings of the British Machine Vision Conference 2002, BMVC 2002 (pp. 468-476). Cardiff, UK: British Machine Vision Association. DOI
2001	Brooks, M., Chojnacki, W., Gawley, D., & Van Den Hengel, A. (2001). Is covariance information useful in estimating vision parameters?. In S. El-Hakim, & A. Gruen (Eds.), Proceedings of SPIE - The International Society for Optical Engineering Vol. 4309 (pp. 195-203). PO BOX 10 BELLINGHAM WASHINGTON USA: THE SOCIETY OF PHOTO-OPTICAL INSTRUMENTATION ENGINEERS. DOI
2001	Chojnacki, W., Brooks, M., Van Den Hengel, A., & Gawley, D. (2001). A fast MLE-based method for estimating the fundamental matrix. In P. Pitas (Ed.), Proceedings of the International Conference on Image Processing Vol. 2 (pp. CDROM 1-CDROM 4). CD-ROM: THE INSTITUTE OF ELECTRICAL AND ELECTRONICS ENGINEERS. DOI Scopus2
2001	Brooks, M., Chojnacki, W., Gawley, D., & Van Den Hengel, A. (2001). What value covariance information in estimating vision parameters?. In Bob Werner (Ed.), Proceedings of the Eighth IEEE International Conference on Computer Vision Vol. 1 (pp. 302-308). LOS ALAMITOS, CALIFORNIA, USA: IEEE COMPUTER SOCIETY. DOI Scopus39 WoS28
2001	Chojnacki, W., Brooks, M. J., Hengel, A. V. D., & Gawley, D. (2001). A fast MLE-based method for estimating the fundamental matrix.. In ICIP (2) (pp. 189-192). IEEE.
2000	Chojnacki, W., Brooks, M., Van Den Hengel, A., & Gawley, D. (2000). Estimating vision parameters given data with covariances. In M. Mirmehdi, & B. Thomas (Eds.), Proceedings of the 11th British Machine Vision Conference 2000 (pp. 182-191). Bristol, UK: ILES Central Press.
2000	Chojnacki, W., Brooks, M., & Van Den Hengel, A. (2000). A simplified treatment of Kanatani's renormalisation method. In J. Wang (Ed.), Proceedings of ICARV 2000 - Sixth International Conference on Control, Automation, Robotics and Vision (pp. CD). Singapore: School of Electrical & Electronic Engineering, NTU.
2000	Chojnacki, W., Brooks, M., & Van Den Hengel, A. (2000). A framework for understanding renormalisation-type methods in computer vision. In J. Blanc Talon, D. Popescu, & G. Lasker (Eds.), Proceedings of Advanced Concepts for Intelligent Vision Systems ACIVS - 2000 (pp. 13-19). Ontario, Canada: The International Institute for Advanced Studies in Systems Research and Cybernetics.
2000	Chojnacki, W., Brooks, M., Van Den Hengel, A., & Gawley, D. (2000). A fast MLE-based method for estimating the fundamental matrix. In C. Sun, P. Ogunbona, W. Li, & R. Beare (Eds.), Proceedings of APRS/IEEE Workshop on Stereo Image and Video Processing (pp. 33-36). Australia: Australian Pattern Recognition Society.
1998	Brooks, M., Chojnacki, W., Dick, A., van den Hengel, A., Kanatani, K., & Ohta, N. (1998). Incorporating optical flow uncertainty information into a self-calibration procedure for a moving camera. In S. ElHakim, & A. Gruen (Eds.), Proceedings of SPIE Vol. 3641 Videometrics VI (pp. 183-192). San Jose, CA: SPIE. DOI Scopus1
1998	Brooks, M. J., Chojnacki, W., van den Hengel, A., & Baumela, L. (1998). Robust techniques for the estimation of structure from motion in the uncalibrated case. In H. Burkhardt, & B. Neumann (Eds.), Computer Vision - ECCV'98: proceedings vol.1 Vol. 1406 (pp. 281-295). Freiburg, Germany: Springer. DOI Scopus3
1995	Brooks, M., Chojnacki, W., & van den Hengel, A. (1995). Solving the shape-from-shading problem on the CM-5. In Proceedings: CAMP '95 Conference on Computer Architectures for Machine Perception (pp. 196-201). Como, Italy: IEEE. DOI

Conference Items

Year	Citation
2006	Thormaehlen, T., Dick, A., Van Den Hengel, A., Ward, B., & Torr, P. (2006). Regular Scene Reconstruction from Image Sequences. Poster session presented at the meeting of [Video Proceedings for] IEEE Computer Society Conference on Computer Vision and Pattern Recognition. New York, USA: IEEE Computer Society.

Original Creative Works

Year	Citation
2021	Authors: Hajdu T, Anderson L, Van Den Hengel A. Title: Untitled AI Piece. Description: N/A. Extent: unknown.
2021	Authors: Hajdu T, Van Den Hengel A, Anderson L. Title: Annual Artificial Artist Artist in Residence, March 2020-June 2021. Description: N/A. Extent: indeterminate.

Internet Publications

Year Citation
2017 Van Den Hengel, A. J. (2017). Can machines really tell us if we’re sick?. The Conversation.

Year	Citation
2017	Van Den Hengel, A. J. (2017). Can machines really tell us if we’re sick?. The Conversation.

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Current Higher Degree by Research Supervision (University of Adelaide)

Date	Role	Research Topic	Program	Degree Type	Student Load	Student Name
2020	Principal Supervisor	Machine Learning	Doctor of Philosophy	Doctorate	Full Time	Mr Clint David Gamlin
2020	Principal Supervisor	Scalable deep learning for scene understanding	Doctor of Philosophy	Doctorate	Full Time	Mr James Paul Bockman
2019	Co-Supervisor	Improved Weather Nowcasting with Deep Learning	Doctor of Philosophy	Doctorate	Full Time	Mr Anthony Charles Meehan
2018	Co-Supervisor	Developing a System for Free-Form Visual Question Answering	Doctor of Philosophy	Doctorate	Full Time	Miss Violetta Shevchenko
2018	Co-Supervisor	Deep Learning for Vision and Language Navigation	Doctor of Philosophy	Doctorate	Full Time	Mr Anthony Manchin

Past Higher Degree by Research Supervision (University of Adelaide)

Date	Role	Research Topic	Program	Degree Type	Student Load	Student Name
2015 - 2019	Co-Supervisor	Real-Time Structure and Object Aware Semantic SLAM	Doctor of Philosophy	Doctorate	Full Time	Mr Mehdi Hosseinzadeh
2014 - 2017	Co-Supervisor	Deep Visual Representation for Weakly-supervised and Structured Output Tasks	Doctor of Philosophy	Doctorate	Full Time	Mr Yao Li
2013 - 2018	Co-Supervisor	Mid-level Representations for Action Recognition and Zero-shot Learning	Doctor of Philosophy	Doctorate	Full Time	Mr Ruizhi Qiao
2013 - 2017	Co-Supervisor	Dynamic Scene Understanding with Applications to Traffic Monitoring	Doctor of Philosophy	Doctorate	Full Time	Mr Qichang Hu
2012 - 2014	Co-Supervisor	Hypergraph Modeling for Saliency Detection and Beyond	Master of Engineering Science	Master	Full Time	Mr Yao Li
2012 - 2018	Principal Supervisor	Preventing Falls in Hospitals with Body Worn Batteryless Sensor Enabled RFID	Doctor of Philosophy	Doctorate	Full Time	Mr Roberto Luis Shinmoto Torres
2011 - 2015	Co-Supervisor	Learning Structured Prediction Models in Computer Vision	Doctor of Philosophy	Doctorate	Full Time	Miss Fayao Liu
2010 - 2014	Principal Supervisor	Markov Random Fields with Unknown Heterogeneous Graphs	Doctor of Philosophy	Doctorate	Full Time	Mr Zhenhua Wang
2009 - 2013	Principal Supervisor	Constrained Parameter Estimation in Multiple View Geometry	Doctor of Philosophy	Doctorate	Full Time	Mr Zygmunt Ladyslaw Szpak
2005 - 2013	Principal Supervisor	Interactive 3D Reconstruction from Video	Doctor of Philosophy	Doctorate	Full Time	Dr Ben Ward
2003 - 2006	Co-Supervisor	Robust Visual Tracking in Image Sequences	Doctor of Philosophy	Doctorate	Full Time	Prof Chunhua Shen
2000 - 2008	Co-Supervisor	Reconstructing 3D Geometry from Multiple Images via Inverse Rendering	Doctor of Philosophy	Doctorate	Full Time	Mr John Bastian
2000 - 2008	Principal Supervisor	The Automated Synchronisation of Independently Moving Cameras	Doctor of Philosophy	Doctorate	Full Time	Dr Daniel Pooley

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Board Memberships

Date Role Board name Institution name Country
2012 - ongoing Advisory Board Member Italian Institute of Technology Standing Committee of External Evaluators Italian Institute of Technology Italy
Editorial Boards

Date Role Editorial Board Name Institution Country
2010 - ongoing Associate Editor IPSJ Transactions on Computer Vision and Applications — —

Date	Role	Board name	Institution name	Country
2012 - ongoing	Advisory Board Member	Italian Institute of Technology Standing Committee of External Evaluators	Italian Institute of Technology	Italy

Date	Role	Editorial Board Name	Institution	Country
2010 - ongoing	Associate Editor	IPSJ Transactions on Computer Vision and Applications	—	—

Offices Held

Date	Office Name	Institution	Country
2016 - 2016	Area Chair	Asian Conference on Computer Vision 2016	—
2016 - 2016	Area Chair	International Conference on Pattern Recognition 2016	—
2014 - ongoing	Program Lead, Analytics and Decision Support	Data 2 Decisions CRC	Australia

Position: AIML Director
Phone: 83135309
Email: anton.vandenhengel@adelaide.edu.au
Fax: 8313 4366
Campus: North Terrace
Building: Australian Institute for Machine Learning, floor 1
Room: 1 28
Org Unit: Australian Institute for Machine Learning

Professor Anton Van Den Hengel

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Current Higher Degree by Research Supervision (University of Adelaide)

Past Higher Degree by Research Supervision (University of Adelaide)

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