Professor Anton Van Den Hengel

Director, Centre for Augmented Reasoning

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

Appointments

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

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

Journals

Year	Citation
2023	Damirchi, H., Opazo, C. R., Abbasnejad, E., Teney, D., Shi, J. Q., Gould, S., & Hengel, A. V. D. (2023). Zero-shot Retrieval: Augmenting Pre-trained Models with Search Engines.. CoRR, abs/2311.17949.
2023	Hollis-Sando, L., Pugh, C., Franke, K., Zerner, T., Tan, Y., Carneiro, G., . . . Bacchi, S. (2023). Deep learning in the marking of medical student short answer question examinations: Student perceptions and pilot accuracy assessment. FOCUS ON HEALTH PROFESSIONAL EDUCATION-A MULTIDISCIPLINARY JOURNAL, 24(1), 38-48.
2022	Manchin, A., Sherrah, J., Wu, Q., & van den Hengel, A. (2022). Program Generation from Diverse Video Demonstrations. BMVC 2022 - 33rd British Machine Vision Conference Proceedings.
2022	Shevchenko, V., Abbasnejad, E., Dick, A. R., Hengel, A. V. D., & Teney, D. (2022). EBMs vs. CL: Exploring Self-Supervised Visual Pretraining for Visual Question Answering.. CoRR, abs/2206.14355.
2022	Liao, Z., Liao, K., Shen, H., Boxel, M. F. V., Prijs, J., Jaarsma, R. L., . . . Verjans, J. W. (2022). CNN Attention Guidance for Improved Orthopedics Radiographic Fracture Classification.. CoRR, abs/2203.10690.
2022	Parvaneh, A., Abbasnejad, E., Wu, Q., Shi, Q. J., & Hengel, A. V. D. (2022). Show, Price and Negotiate: A Negotiator With Online Value Look-Ahead.. IEEE Trans. Multim., 24, 1426-1434.
2022	He, T., Shen, C., & Hengel, A. V. D. (2022). Dynamic Convolution for 3D Point Cloud Instance Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(5), 5697-5711. DOI Scopus2 WoS1
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 Scopus18 WoS12
2022	Shevchenko, V., Abbasnejad, E., Dick, A., Hengel, A. V. D., & Teney, D. (2022). EBMs vs. CL: Exploring Self-Supervised Visual Pretraining for Visual Question Answering.
2022	Yin, W., Liu, Y., Shen, C., Hengel, A. V. D., & Sun, B. (2022). The devil is in the labels: Semantic segmentation from sentences.
2022	Sun, W., Gong, D., Shi, J. Q., van den Hengel, A., & Zhang, Y. (2022). Video super-resolution via mixed spatial-temporal convolution and selective fusion. Pattern Recognition, 126, 1-14. DOI Scopus10 WoS6
2022	Liao, Z., Liao, K., Shen, H., Van Boxel, M. F., Prijs, J., Jaarsma, R. L., . . . Verjans, J. W. (2022). CNN Attention Guidance for Improved Orthopedics Radiographic Fracture Classification.. IEEE J Biomed Health Inform, 26(7), 3139-3150. DOI Scopus5
2022	Barreto, R. A., Kellett, J., Pierce, J. C., & Van den Hengel, A. (2022). I'm in Love with My Car: Psychological Attachment to Cars, Automated Vehicles and the Driverless Future. Journal of Transport Economics and Policy, 56(1), 28-55.
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 Scopus1
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 Scopus19 WoS9
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, 1-16. DOI Scopus19 WoS17
2021	Moghaddam, M. K., Abbasnejad, E., Wu, Q., Shi, J., & Hengel, A. V. D. (2021). Learning for Visual Navigation by Imagining the Success.
2021	Pang, G., Shen, C., Cao, L., & Hengel, A. V. D. (2021). Deep Learning for Anomaly Detection: A Review. ACM Computing Surveys, 54(2), 1-36. DOI Scopus1016 WoS775
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 Scopus14 WoS7
2020	Gao, C., Zhu, Q., Wang, P., Li, H., Liu, Y., Van den Hengel, A., & Wu, Q. (2020). Structured Multimodal Attentions for TextVQA. IEEE Transactions on Pattern Analysis and Machine Intelligence, 14(8), 1. DOI Scopus19
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	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 Scopus395 WoS230 Europe PMC58
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 Scopus21 WoS15 Europe PMC3
2020	Teney, D., Abbasnejad, E., & Hengel, A. V. D. (2020). Learning What Makes a Difference from Counterfactual Examples and Gradient Supervision.. CoRR, abs/2004.09034.
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 Scopus61 WoS46 Europe PMC2
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 Scopus14 WoS13
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 Scopus19 WoS9
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	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 WoS2
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 Scopus41 WoS28
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 Scopus22 WoS25
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 Scopus18 WoS15 Europe PMC1
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	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 Scopus15 WoS11 Europe PMC8
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 Scopus883 WoS590
2019	Manchin, A., Abbasnejad, E., & Hengel, A. V. D. (2019). Reinforcement Learning with Attention that Works: A Self-Supervised Approach.. CoRR, abs/1904.03367.
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 Scopus48 WoS32
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, 12071-12078. WoS4
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 Scopus18 WoS13
2019	Wang, Y., Gong, D., Yang, J., Shi, Q., Hengel, A. V. D., Xie, D., & Zeng, B. (2019). An Effective Two-Branch Model-Based Deep Network for Single Image Deraining.
2018	Snaauw, G., Gong, D., Maicas, G., Hengel, A. V. D., Niessen, W. J., Verjans, J., & Carneiro, G. (2018). End-to-End Diagnosis and Segmentation Learning from Cardiac Magnetic Resonance Imaging.. CoRR, abs/1810.10117.
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 Scopus81 WoS71 Europe PMC4
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 Scopus65 WoS80
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 Scopus61 WoS53
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 Scopus288 WoS196 Europe PMC8
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 Scopus12 WoS10
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 Scopus16 WoS13
2017	Abbasnejad, M., Shi, Q., Abbasnejad, I., Hengel, A., & Dick, A. (2017). Bayesian Conditional Generative Adverserial Networks.. CoRR, abs/1706.05477.
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 Scopus3 WoS4
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 Scopus264 WoS173 Europe PMC5
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. DOI
2017	Liu, L., Wang, P., Shen, C., Wang, L., Van Den Hengel, A., Wang, C., & Shen, H. T. (2017). Compositional model based Fisher vector coding for image classiﬁcation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(12), 2335-2348. DOI Scopus53 WoS37 Europe PMC3
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 Scopus51 WoS43
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 Scopus168 WoS152
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 Scopus37 WoS28
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 Scopus228 WoS156
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 Scopus34 WoS24
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 Scopus10 WoS7
2017	Teney, D., Wu, Q., & Van Den Hengel, A. (2017). Visual Question Answering: a tutorial. IEEE Signal Processing Magazine, 34(6), 63-75. DOI Scopus29 WoS17
2017	Teney, D., & Hengel, A. V. D. (2017). Visual Question Answering as a Meta Learning Task.. CoRR, abs/1711.08105.
2017	Zhang, L., Wei, W., Shi, Q., Shen, C., Hengel, A. V. D., & Zhang, Y. (2017). Beyond Low Rank: A Data-Adaptive Tensor Completion Method.
2016	Torres, R. L. S., Ranasinghe, D. C., Shi, Q., & Hengel, A. V. D. (2016). Learning from Imbalanced Multiclass Sequential Data Streams Using Dynamically Weighted Conditional Random Fields.
2016	Guo, Y., Chen, J., Du, Q., Hengel, A. V. D., Shi, Q., & Tan, M. (2016). The Shallow End: Empowering Shallower Deep-Convolutional Networks through Auxiliary Outputs.
2016	Wu, L., Shen, C., & Hengel, A. V. D. (2016). Deep Recurrent Convolutional Networks for Video-based Person Re-identification: An End-to-End Approach.
2016	Wu, Z., Shen, C., & Hengel, A. V. D. (2016). Bridging Category-level and Instance-level Semantic Image Segmentation.
2016	Wu, Z., Shen, C., & Hengel, A. V. D. (2016). High-performance Semantic Segmentation Using Very Deep Fully Convolutional Networks.
2016	Wang, P., Liu, L., Shen, C., Hengel, A. V. D., & Shen, H. T. (2016). Hi Detector, What's Wrong with that Object? Identifying Irregular Object From Images by Modelling the Detection Score Distribution.
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 Scopus116 WoS93
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 Scopus96 WoS81 Europe PMC6
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 Scopus46 WoS39
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 Scopus66 WoS54 Europe PMC6
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 Scopus68 WoS58 Europe PMC5
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 WoS2
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 Scopus148 WoS136 Europe PMC23
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 Scopus24 WoS18 Europe PMC7
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 Scopus96 WoS65
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 Scopus37 WoS32
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 Scopus8 WoS6
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 Scopus81 WoS68 Europe PMC7
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 Scopus5 WoS4
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 Scopus139 WoS127 Europe PMC13
2015	Tan, M., Xiao, S., Gao, J., Xu, D., Hengel, A. V. D., & Shi, Q. (2015). Scalable Nuclear-norm Minimization by Subspace Pursuit Proximal Riemannian Gradient.
2014	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 WoS6 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 Scopus3 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 Scopus33 WoS28 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 Scopus5 WoS3
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 Scopus88 WoS71 Europe PMC6
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 Scopus20 WoS17
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 Scopus14 WoS14
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 Scopus11 WoS12 Europe PMC3
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 Scopus84 WoS65 Europe PMC7
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 Scopus28 WoS24
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 Scopus22 WoS21
2013	Shen, C., Li, H., & Van Den Hengel, A. (2013). Fully corrective boosting with arbitrary loss and regularization. Neural Networks, 48, 44-58. DOI Scopus8 WoS7 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 Scopus33 WoS31 Europe PMC6
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 Scopus29 WoS22
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 Scopus665 WoS588
2013	Zhang, Z., Shi, Q., Zhang, Y., Shen, C., & Hengel, A. V. D. (2013). Constraint Reduction using Marginal Polytope Diagrams for MAP LP Relaxations.
2012	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 Scopus10 WoS9
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 Scopus57 WoS45 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. Scopus75 WoS58
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 Scopus9 WoS6
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 WoS2
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
2009	Van Der Vlies, A. E., Koedam, E. L. G. E., Pijnenburg, Y. A. L., Twisk, J. W. R., Scheltens, P., & Van Der Flier, W. M. (2009). Most rapid cognitive decline in APOE 4 negative Alzheimer's disease with early onset. Psychological Medicine, 39(11), 1907-1911. DOI Scopus82 WoS115 Europe PMC54
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 Scopus32 WoS30
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	Keunen, R. W. M., Hoogenboezem, R., Wijnands, R., Van Den Hengel, A. C. M., & Ackerstaff, R. G. A. (2008). Introduction of an embolus detection system based on analysis of the transcranial Doppler audio-signal. Journal of Medical Engineering and Technology, 32(4), 296-304. DOI Scopus7
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 Scopus95 WoS74
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 Scopus73 WoS52 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 Scopus57 WoS45
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 Scopus43 WoS35
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 Scopus143 WoS114
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 Scopus11 WoS8
-	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.
-	Wu, L., Shen, C., & Hengel, A. V. D. (n.d.). PersonNet: Person Re-identification with Deep Convolutional Neural Networks.
-	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.

Book Chapters

Year	Citation
2023	Liao, Z., van den Hengel, A., & Verjans, J. W. (2023). Medical visual question answering. In A. C. Chang, A. Limon, R. Brisk, F. Lopez-Jimenez, & L. Y. Sun (Eds.), Intelligence-Based Cardiology and Cardiac Surgery: Artificial Intelligence and Human Cognition in Cardiovascular Medicine (pp. 157-162). Elsevier. DOI
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 Scopus2
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
2024	Chen, L., Zhang, Y., Song, Y., Van Den Hengel, A., & Liu, L. (2024). Domain Generalization via Rationale Invariance. In Proceedings of the IEEE International Conference on Computer Vision (pp. 1751-1760). Paris, France: IEEE. DOI Scopus1
2023	McDonnell, M. D., Gong, D., Parvaneh, A., Abbasnejad, E., & Hengel, A. V. D. (2023). RanPAC: Random Projections and Pre-trained Models for Continual Learning.. In A. Oh, T. Naumann, A. Globerson, K. Saenko, M. Hardt, & S. Levine (Eds.), NeurIPS.
2023	Li, P., Purkait, P., Ajanthan, T., Abdolshah, M., Garg, R., Husain, H., . . . Van Den Hengel, A. (2023). Semi-Supervised Semantic Segmentation under Label Noise via Diverse Learning Groups. In 2023 IEEE/CVF International Conference on Computer Vision (ICCV) (pp. 1229-1238). Online: IEEE. DOI
2023	Pang, G., Shen, C., Jin, H., & van den Hengel, A. (2023). Deep Weakly-supervised Anomaly Detection. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (pp. 13 pages). Online: ACM. DOI Scopus6 WoS2
2023	Shu, Y., Van Den Hengel, A., & Liu, L. (2023). Learning Common Rationale to Improve Self-Supervised Representation for Fine-Grained Visual Recognition Problems. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Vol. 2023-June (pp. 11392-11401). Online: IEEE. DOI Scopus1
2023	Zhu, T., Ferenczi, B., Purkait, P., Drummond, T., Rezatofighi, H., & Van Den Hengel, A. (2023). Knowledge Combination to Learn Rotated Detection without Rotated Annotation. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Vol. 2023-June (pp. 15518-15527). CANADA, Vancouver: IEEE COMPUTER SOC. DOI
2022	Pang, G., Li, J., Van Den Hengel, A., Cao, L., & Dietterich, T. G. (2022). ANDEA: Anomaly and Novelty Detection, Explanation, and Accommodation. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 4892-4893). DC, Washington: ACM. DOI Scopus1 WoS1
2022	Yan, Q., Gong, D., Liu, Y., Van Den Hengel, A., & Shi, J. Q. (2022). Learning Bayesian Sparse Networks with Full Experience Replay for Continual Learning. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Vol. 2022-June (pp. 109-118). Online: IEEE. DOI Scopus16 WoS5
2022	Long, A., Yin, W., Ajanthan, T., Nguyen, V., Purkait, P., Garg, R., . . . Van Den Hengel, A. (2022). Retrieval Augmented Classification for Long-Tail Visual Recognition. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Vol. 2022-June (pp. 6949-6959). Online: IEEE. DOI Scopus24 WoS2
2022	He, T., Yin, W., Shen, C., & van den Hengel, A. (2022). PointInst3D: Segmenting 3D Instances by Points. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 13663 LNCS (pp. 286-302). Online: Springer. DOI Scopus5
2022	Parvaneh, A., Abbasnejad, E., Teney, D., Haffari, R., Hengel, A. V. D., & Shi, Q. J. (2022). Active Learning by Feature Mixing.. In CoRR Vol. abs/2203.07034.
2022	Teney, D., Abbasnejad, E., Lucey, S., & Hengel, A. V. D. (2022). Evading the Simplicity Bias: Training a Diverse Set of Models Discovers Solutions with Superior OOD Generalization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2022) Vol. 2022-June (pp. 16740-16751). New Orleans, Louisiana: IEEE. DOI Scopus17 WoS5
2022	Pang, G., Pham, N. T. A., Baker, E., Bentley, R., & van den Hengel, A. (2022). Deep Depression Prediction on Longitudinal Data via Joint Anomaly Ranking and Classification. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 13281 LNAI (pp. 236-248). SW Jiaotong Univ, Chengdu, PEOPLES R CHINA: Springer International Publishing. DOI
2022	Mao, W., Ge, Y., Shen, C., Tian, Z., Wang, X., Wang, Z., & den Hengel, A. V. (2022). Poseur: Direct Human Pose Regression with Transformers. In S. Avidan, G. Brostow, M. Cisse, G. M. Farinella, & T. Hassner (Eds.), Computer Vision - ECCV 2022. Vol. 13666 LNCS (pp. 72-88). Tel Aviv, Israel: Springer, Cham. DOI Scopus25 WoS16
2022	Ma, R., Pang, G., Chen, L., & Van Den Hengel, A. (2022). Deep graph-level anomaly detection by glocal knowledge distillation. In Proceedings of the 15th ACM International Conference on Web Search and Data Mining (WSDM 2022) (pp. 704-714). Online: ACM. DOI Scopus34 WoS7
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 Scopus5 WoS1
2022	Parvaneh, A., Abbasnejad, E., Teney, D., Haffari, R., Van Den Hengel, A., & Shi, J. Q. (2022). Active Learning by Feature Mixing. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Vol. 2022-June (pp. 12227-12236). New Orleans, LA, USA: IEEE. DOI Scopus32 WoS13
2022	Qi, Y., Pan, Z., Hong, Y., Yang, M. H., Van Den Hengel, A., & Wu, Q. (2022). The Road to Know-Where: An Object-and-Room Informed Sequential BERT for Indoor Vision-Language Navigation. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV 2021) (pp. 1635-1644). online: IEEE. DOI Scopus39 WoS2
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 Scopus5
2021	Teney, D., Abbasnejad, E., & van den Hengel, A. (2021). Unshuffling Data for Improved Generalization in Visual Question Answering. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV 2021) Vol. abs/2002.11894 (pp. 1397-1407). Los Alamitos, CA: IEEE. DOI Scopus22 WoS5
2021	He, T., Shen, C., & van den Hengel, A. (2021). DyCO3D: Robust Instance Segmentation of 3D Point Clouds through Dynamic Convolution. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 354-363). online: IEEE. DOI Scopus56 WoS19
2021	Pang, G., Van Den Hengel, A., Shen, C., & Cao, L. (2021). Toward Deep Supervised Anomaly Detection: Reinforcement Learning from Partially Labeled Anomaly Data. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1298-1308). online: ACM. DOI Scopus53 WoS27
2021	Pang, G., Li, J., Van Den Hengel, A., Cao, L., & Dietterich, T. G. (2021). Anomaly and Novelty Detection, Explanation, and Accommodation (ANDEA). In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 4145-4146). ELECTR NETWORK: ACM. DOI Scopus3
2021	Shen, H., Liao, K., Liao, Z., Doornberg, J., Qiao, M., Van Den Hengel, A., & Verjans, J. W. (2021). Human-AI interactive and continuous sensemaking: A case study of image classification using scribble attention maps. In Proceedings of the Conference on Human Factors in Computing Systems (CHI'21) (pp. 1-8). New York, NY: Association for Computing Machinery. DOI Scopus4 WoS3
2021	Teney, D., Abbasnejad, E., & Hengel, A. V. D. (2021). Unshuffling Data for Improved Generalization in Visual Question Answering.. In ICCV (pp. 1397-1407). IEEE.
2021	Teney, D., Abbasnejad, E., Lucey, S., & Hengel, A. V. D. (2021). Evading the Simplicity Bias: Training a Diverse Set of Models Discovers Solutions with Superior OOD Generalization.. In CoRR Vol. abs/2105.05612.
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 Scopus37
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 Scopus147
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.), Proceedings of the 11th International Conference of the CLEF Initiative (CLEF 2020), as published in CEUR Workshop Proceedings Vol. 2696 (pp. 1-14). online: CEUR-WS. Scopus6
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 Scopus3
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), as published in Lecture Notes in Computer Science Vol. 12355 (pp. 580-599). Switzerland: Springer. DOI Scopus39
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. Scopus17
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 Scopus57
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 Scopus142
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. In H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, & H. Lin (Eds.), Proceedings of the 34th Conference on Neural Information Processing Systems (NeruIPS 2020) Vol. abs/2005.09241 (pp. 1-11). San Francisco, CA, United States: Morgan Kaufmann. Scopus54
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 Scopus7 WoS3
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 Scopus12
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 Scopus80
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. R., 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 Scopus920 WoS683
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 Scopus218 WoS116
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 Proceedings of the 27th ACM International Conference on Multimedia (ACM Multimedia 2019), MM '19 (pp. 1543-1551). online: Association for Computing Machinery. DOI Scopus4 WoS1
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 Scopus32 WoS21
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 Scopus13 WoS6
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 Scopus15 WoS5
2019	Yan, Q., Gong, D., Shi, Q., Van Den Hengel, A., Shen, C., Reid, I., & Zhang, Y. (2019). Attention-guided network for ghost-free high dynamic range imaging. In Proceedings: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Vol. 2019-June (pp. 1751-1760). online: IEEE. DOI Scopus181 WoS142
2019	Wang, P., Wu, Q., Cao, J., Shen, C., Gao, L., & Hengel, A. V. D. (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 Scopus194 WoS99
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 Scopus19 WoS12
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 Scopus38 WoS27
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 Scopus9 WoS4
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. Scopus29 WoS15
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 Scopus97 WoS54
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	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 Scopus7 WoS4
2018	Anderson, P., Wu, Q., Teney, D., Bruce, J., Johnson, M., Sünderhauf, N., . . . Hengel, A. V. D. (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 Scopus670 WoS1140
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	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 Scopus11 WoS14
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 Scopus104 WoS52
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 Scopus83 WoS62
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 Scopus243 WoS158
2018	Anderson, P., Wu, Q., Teney, D., Bruce, J., Johnson, M., Sünderhauf, N., . . . Hengel, A. V. D. (2018). Vision-and-Language Navigation: Interpreting Visually-Grounded Navigation Instructions in Real Environments.. In CVPR (pp. 3674-3683). Computer Vision Foundation / IEEE Computer Society. DOI
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 IEEE/CVF Conference on Computer Vision and Pattern Recognition Vol. abs/1708.02711 (pp. 4223-4232). Online: IEEE Computer Society. DOI
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 Scopus275 WoS167
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 Scopus50 WoS31
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 Scopus67 WoS34
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 Scopus75 WoS44
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 Scopus24 WoS8
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 Scopus24 WoS22
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 Scopus66 WoS41
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 Scopus31 WoS23
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 Scopus241 WoS180
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. Scopus11 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	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 Scopus121 WoS60
2016	Teney, D., Liu, L., & Hengel, A. V. D. (2016). Graph-Structured Representations for Visual Question Answering.. In CoRR Vol. abs/1609.05600.
2016	Zhang, L., Wei, W., Zhang, Y., Shen, C., Van Den Hengel, A., & Shi, Q. (2016). Cluster sparsity field for hyperspectral imagery denoising. In B. Leibe, J. Matas, N. Sebe, & M. Welling (Eds.), Proceedings of the 14th European Conference on Computer Vision Vol. 9909 (pp. 631-647). Amsterdam, Netherlands: Springer International Publishing AG. DOI Scopus15 WoS13
2016	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 Scopus35 WoS25
2016	Tan, M., Yan, Y., Wang, L., Van Den Hengel, A., Tsang, I., & Shi, Q. (2016). Learning sparse confidence-weighted classifier on very high dimensional data. In Proceedings of the 30th AAAI Conference on Artificial Intelligence Vol. 3 (pp. 2080-2086). Phoenix, AZ: AAAI Press. Scopus4 WoS2
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 Scopus10 WoS7
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 Scopus44 WoS24
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 Scopus64 WoS58
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 Scopus280 WoS183
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 Scopus145 WoS103
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 Scopus672 WoS276
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 Scopus399 WoS278
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
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 Scopus3 WoS3
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 Scopus3
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 Scopus15 WoS12
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 Scopus209 WoS7
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 Scopus1488 WoS952
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 Scopus6
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 Scopus71 WoS48
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 Scopus12 WoS4
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 Scopus39 WoS28
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 Scopus403 WoS320
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 Scopus156 WoS101
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 Scopus499 WoS383
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 Scopus16 WoS12
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. Scopus33
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. Scopus67 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 Scopus370 WoS295
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 Scopus137 WoS119
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 Scopus168 WoS121
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 Scopus16 WoS12
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 Scopus200 WoS148
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 Scopus13 WoS13
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 Scopus41 WoS24
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. Scopus111
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 Scopus202 WoS161
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 Scopus38 WoS20
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 Scopus215 WoS168
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 Scopus22 WoS10
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 Scopus83 WoS60
2013	Gu, S. -M., Li, X., Wu, W. -Z., & Nian, H. (2013). MULTI-GRANULATION ROUGH SETS IN MULTI-SCALE INFORMATION SYSTEMS. In PROCEEDINGS OF 2013 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS (ICMLC), VOLS 1-4 (pp. 108-113). Tianjin, PEOPLES R CHINA: IEEE. WoS75
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	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 Scopus10
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 Scopus40 WoS28
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 Scopus28 WoS24
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 Scopus73 WoS44
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 Scopus39 WoS28
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 Scopus268 WoS192
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	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 Scopus10
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 Scopus22
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 Scopus181 WoS136
2010	Chen, Y., Ding, X., King, M. W., & Li, Y. -L. (2010). Study on Biostability of Poly (trimethylene terephthalate) Filament to Hydrolytic Degradation in Normal Saline. In 2010 INTERNATIONAL FORUM ON BIOMEDICAL TEXTILE MATERIALS, PROCEEDINGS (pp. 8-12). Donghua Univ, Songjiang Campus, Shanghai, PEOPLES R CHINA: DONGHUA UNIV PRESS. WoS2
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 Scopus6
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 Scopus25
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 Scopus34 WoS20
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. Scopus65
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 Scopus27 WoS15
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 Scopus117
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 Scopus31 WoS7
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 Scopus9 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. J. (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. ACM. 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. Scopus9
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 Scopus11
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 Scopus23
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 Scopus10 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 Scopus39 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 Scopus11 WoS6
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 Scopus9 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 WoS2
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. L. Rosin, & D. Marshall (Eds.), Proceedings of the 13th British Machine Vision Conference (BMVC 2002) (pp. 468-476). 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 Scopus46 WoS32
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. J., 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. F. 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. J., 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
2022	Dorraki, M., Liao, Z., Abbott, D., Psaltis, P. J., Baker, E., Bidargaddi, N., . . . Verjans, J. W. (2022). Cardiovascular disease risk prediction via machine learning using mental health data. Poster session presented at the meeting of EUROPEAN HEART JOURNAL. OXFORD UNIV PRESS.
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: Anderson L, Van Den Hengel A, Sherrah J, Ward B. Title: AI BIBLE (working title). Description: N/A. Extent: indeterminate.
2021	Authors: Hajdu T, Anderson L, Van Den Hengel A, Sherrah J, Ward B. Title: Untitled Work. Description: N/A. Extent: unknown.
2020	Authors: Hajdu T, Van Den Hengel A, Anderson L. Title: Art Intelligence Annual Artificial Artist in Residence: Laurie Anderson. Description: N/A. Extent: Indeterminate.

Curated or Produced Public Exhibition or Events

Year	Citation
2023	Anderson, L., Hajdu, T., & Van Den Hengel, A. (2023). Laurie Anderson: LOOKING INTO A MIRROR SIDEWAYS (No. Of Pieces: public exhibition) [public exhibition]. stockholm, sweden.
2022	Hajdu, T., Van Den Hengel, A., & Anderson, L. (2022). Art of the Possible / Creative Revolutionaries: A Conversation With Laurie Anderson (No. Of Pieces: 90 minutes) [presentation]. Adelaide: https://artofthepossible.com.au/stream-ai-talk-with-laurie-anderson/.
2021	Hajdu, T., Van Den Hengel, A., Sherrah, J., Dalby, P., & Verjans, J. (2021). Adelaide Festival of Ideas: Art of Artificial Intelligence (No. Of Pieces: 1 hour) [panel discussion]. Adelaide: https://adelaidefestivalofideas.com.au.
2021	Anderson, L., Thomas, H., & Van Den Hengel, A. (2021). Laurie Anderson, Norton Lecture, Spending the War Without You, "The Road" (No. Of Pieces: >60 min) [>]. https://mahindrahumanities.fas.harvard.edu/norton-lectures?fbclid=IwAR0p0zO4y4U1MRWn65i7IGUS9p0CIQFtMIjRONrR6CyYc0D8E6FbXtoaHnw: Mahindra Humanities Center, Harvard University.
2020	Hajdu, T., Anderson, L., & Van Den Hengel, A. (2020). Art Intelligence Hackathon (No. Of Pieces: 8 hours) [Exhibition]. Adelaide.

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.

Preprint

Year	Citation
2024	McDonnell, M. D., Gong, D., Abbasnejad, E., & Hengel, A. V. D. (2024). Premonition: Using Generative Models to Preempt Future Data Changes in Continual Learning.
2024	Chowdhury, T. F., Liao, K., Phan, V. M. H., To, M. -S., Xie, Y., Hung, K., . . . Liao, Z. (2024). CAPE: CAM as a Probabilistic Ensemble for Enhanced DNN Interpretation.
2023	Damirchi, H., Rodríguez-Opazo, C., Abbasnejad, E., Teney, D., Shi, J. Q., Gould, S., & Hengel, A. V. D. (2023). Zero-shot Retrieval: Augmenting Pre-trained Models with Search Engines.
2023	McDonnell, M. D., Gong, D., Parvaneh, A., Abbasnejad, E., & Hengel, A. V. D. (2023). RanPAC: Random Projections and Pre-trained Models for Continual Learning..
2022	Liu, Y., Zhang, Z., Gong, D., Gong, M., Huang, B., Hengel, A. V. D., . . . Shi, J. Q. (2022). Identifying Weight-Variant Latent Causal Models.

Current Higher Degree by Research Supervision (University of Adelaide)

Date	Role	Research Topic	Program	Degree Type	Student Load	Student Name
2020	Principal Supervisor	Scalable deep learning for scene understanding	Doctor of Philosophy	Doctorate	Full Time	Mr James Paul Bockman

Past Higher Degree by Research Supervision (University of Adelaide)

Date	Role	Research Topic	Program	Degree Type	Student Load	Student Name
2018 - 2022	Co-Supervisor	Developing a System for Free-Form Visual Question Answering	Doctor of Philosophy	Doctorate	Full Time	Miss Violetta Shevchenko
2018 - 2023	Co-Supervisor	Explainable Reinforcement Learning via Rule Extraction in Complex Visual Environments	Doctor of Philosophy	Doctorate	Full Time	Mr Anthony Manchin
2015 - 2019	Co-Supervisor	Real-Time Structure and Object Aware Semantic SLAM	Doctor of Philosophy	Doctorate	Full Time	Mr Mehdi Hosseinzadeh
2015 - 2021	Co-Supervisor	General and Fine-Grained Video Understanding using Machine Learning & Standardised Neural Network Architectures	Doctor of Philosophy	Doctorate	Full Time	Hayden James Faulkner
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

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: Director, Centre for Augmented Reasoning
Phone: 83135309
Email: anton.vandenhengel@adelaide.edu.au
Fax: 83134366
Campus: North Terrace
Building: Australian Institute for Machine Learning, floor 1
Org Unit: Australian Institute for Machine Learning - Operations

Professor Anton Van Den Hengel

Appointments

Awards and Achievements

Education

Research Interests

Journals

Book Chapters

Conference Papers

Conference Items

Original Creative Works

Curated or Produced Public Exhibition or Events

Internet Publications

Preprint

Current Higher Degree by Research Supervision (University of Adelaide)

Past Higher Degree by Research Supervision (University of Adelaide)

Board Memberships

Editorial Boards

Offices Held

Connect With Me

External Profiles

Other Links