Anton Van Den Hengel

Research Interests

Computer Vision Knowledge Representation and Machine Learning

Prof Anton Van Den Hengel

Director, CAR

School of Computer Science and Information Technology

College of Engineering and Information Technology

Anton van den Hengel is the Chief Scientist at the Australian Institute for Machine Learning (AIML) and Director of the Centre for Augmented Reasoning. He is a Professor of Computer Science at the University of Adelaide, a Chief Investigator of the NHMRC Centre of Research Excellence on Healthy Housing, a Fellow of the Australian Academy of Technology and Engineering and a Fellow of the Royal Society of South Australia. The Centre for Augmented Reasoning (CAR), established in 2021, represents a $20m investment by the Australian Government in AI research. Professor van den Hengel was also the founder of AIML, Australia’s largest machine learning research group. Professor van den Hengel has been a CI on over $80m in research funding from sources, including Google, Facebook, Canon, BHP Billiton, and the ARC. Anton was a Director of Applied Science within Amazon for four years where he formed the Australian arm of Amazon’s International Machine Learning group.Professor van den Hengel has won several awards, including the 2021 Australasian AI Outstanding Service Award, the Pearcey Foundation Entrepreneur Award, the SA Science Excellence Award for Research Collaboration, the CVPR Best Paper prize in 2010 and in 2025 was elected to the CORE Academy to recognise his significant and cumulative contribution to the development of the computing disciplines in Australasia. According to Google Scholar, he has authored over 440 publications, has over 37,000 citations and an h-index of 89. He has had 8 patents commercialised, formed 5 start-ups, and had a medical technology achieve first-in-class FDA approval. Current research interests include vision and language problems, image-based modelling, and semantic reconstruction.

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

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 -

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

Year Citation
2026 Mohammadi, B., Abbasnejad, E., Qi, Y., Wu, Q., Van Den Hengel, A., & Shi, J. Q. (2026). Parameter-efficient action planning with large language models for vision-and-language navigation. Pattern Recognition, 172, 11 pages.
DOI
2025 Liu, Y., Zhang, Z., Gong, D., Gong, M., Huang, B., van den Hengel, A., . . . Shi, J. Q. (2025). Latent Covariate Shift: Unlocking Partial Identifiability for Multi-Source Domain Adaptation. Transactions on Machine Learning Research, 2025-April.
Scopus1
2025 Shu, Y., Liu, Y., Cao, X., Chen, Q., Zhang, B., Zhou, Z., . . . Liu, L. (2025). Seeing Beyond Labels: Source-Free Domain Adaptation via Hypothesis Consolidation of Prediction Rationale. Transactions on Machine Learning Research, 2025-June.
2025 Huy, T. D., Huynh, D. A., Xie, Y., Qi, Y., Chen, Q., Nguyen, P. L., . . . Phan, V. M. H. (2025). Seeing the Trees for the Forest: Rethinking Weakly-Supervised Medical Visual Grounding.. CoRR, abs/2505.15123.
2025 Shinnick, Z., Jiang, L., Saratchandran, H., Hengel, A. V. D., & Teney, D. (2025). Transformers Pretrained on Procedural Data Contain Modular Structures for Algorithmic Reasoning.. CoRR, abs/2505.22308.
2025 Malik, J. S., Qiao, H., Pang, G., & van den Hengel, A. (2025). Deep learning for hate speech detection: a comparative study. International Journal of Data Science and Analytics, 20(4), 3053-3068.
DOI Scopus7 WoS7
2025 Kovoor, J. G., Stretton, B., Gupta, A. K., Beath, A., Jacob, M. O., Kefalianos, J. M., . . . Adelaide Score Advisory Group. (2025). The Adelaide Score: prospective implementation of an artificial intelligence system to improve hospital and cost efficiency. ANZ Journal of Surgery, 95(3), 342-349.
DOI Scopus2 WoS2 Europe PMC2
2025 Khan, E., Lambrakis, K., Briffa, T., Cullen, L. A., Karnon, J., Papendick, C., . . . Chew, D. P. (2025). Re-engineering the clinical approach to suspected cardiac chest pain assessment in the emergency department by expediting research evidence to practice using artficial intelligence. (RAPIDx AI) - a cluster randomised study design. American Heart Journal, 285, 106-118.
DOI Scopus1 Europe PMC1
2025 Albert, P., Zhang, F. Z., Saratchandran, H., Rodriguez-Opazo, C., van den Hengel, A., & Abbasnejad, E. (2025). RANDLORA: FULL-RANK PARAMETER-EFFICIENT FINE-TUNING OF LARGE MODELS. 13th International Conference on Learning Representations Iclr 2025, abs/2502.00987, 42143-42155.
Scopus1
2025 Huy, T. D., Tran, S. K., Nguyen, P., Tran, N. H., Sam, T. B., Hengel, A. V. D., . . . Phan, V. M. H. (2025). Interactive Medical Image Analysis with Concept-based Similarity Reasoning.. CoRR, abs/2503.06873.
2024 Chen, Q., Zhao, R., Wang, S., Phan, V. M. H., Hengel, A. V. D., Verjans, J., . . . Wu, Q. (2024). A Survey of Medical Vision-and-Language Applications and Their Techniques.. CoRR, abs/2411.12195.
2024 Cao, H., Zou, J., Liu, Y., Zhang, Z., Abbasnejad, E., Hengel, A. V. D., & Shi, J. Q. (2024). InvariantStock: Learning Invariant Features for Mastering the Shifting Market. Transactions on Machine Learning Research, 2024.
2024 Jiang, H., Li, Z., Hu, Y., Yin, B., Yang, J., Hengel, A. V. D., . . . Qi, Y. (2024). Dual Prototype Contrastive Network for Generalized Zero-Shot Learning. IEEE Transactions on Circuits and Systems for Video Technology, 35(2), 1111-1122.
DOI Scopus2 WoS2
2024 Chowdhury, T. F., Vu, M. H. P., Liao, K., To, M. -S., Xie, Y., van den Hengel, A., . . . Liao, Z. (2024). AdaCBM: An Adaptive Concept Bottleneck Model for Explainable and Accurate Diagnosis. MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION - MICCAI 2024, PT X, 15010, 35-45.
DOI WoS3
2024 Dorraki, M., Liao, Z., Abbott, D., Psaltis, P. J., Baker, E., Bidargaddi, N., . . . Verjans, J. W. (2024). Improving Cardiovascular Disease Prediction With Machine Learning Using Mental Health Data: A Prospective UK Biobank Study. JACC: Advances, 3(9.2), 101180-1-101180-9.
DOI Scopus13 WoS8 Europe PMC4
2024 Yin, W., Liu, Y., Shen, C., Sun, B., & van den Hengel, A. (2024). Scaling Up Multi-domain Semantic Segmentation with Sentence Embeddings. International Journal of Computer Vision, 132(9), 4036-4051.
DOI Scopus4 WoS4
2024 Liu, X., Li, G., Qi, Y., Han, Z., Hengel, A. V. D., Sebe, N., . . . Huang, Q. (2024). Consistency-Aware Anchor Pyramid Network for Crowd Localization. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1-15.
DOI Scopus8
2024 Chowdhury, T. F., Liao, K., Hieu Phan, V. M., To, M. S., Xie, Y., Hung, K., . . . Liao, Z. (2024). CAPE: CAM as a Probabilistic Ensemble for Enhanced DNN Interpretation. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, abs/2404.02388, 11072-11081.
DOI Scopus1
2024 Opazo, C. R., Abbasnejad, E., Teney, D., Marrese-Taylor, E., Damirchi, H., & Hengel, A. V. D. (2024). Synergy and Diversity in CLIP: Enhancing Performance Through Adaptive Backbone Ensembling.. CoRR, abs/2405.17139, 101914-101958.
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.
DOI WoS4
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 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 Scopus12 WoS10
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 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 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 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 Scopus30 WoS29
2022 Yin, W., Liu, Y., Shen, C., Sun, B., & Hengel, A. V. D. (2022). Scaling up Multi-domain Semantic Segmentation with Sentence Embeddings.
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 Scopus14 WoS13
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 Scopus20 WoS14 Europe PMC7
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.
Scopus1 WoS1
2022 Parvaneh, A., Abbasnejad, E., Wu, Q., Shi, Q., & Van Den Hengel, A. (2022). Show, price and negotiate: a negotiator with online value look-ahead. IEEE Transactions on Multimedia, 24, 1426-1434.
DOI Scopus2 WoS1
2021 Yan, Q., Gong, D., Shi, J. Q., van den Hengel, A., Shen, C., Reid, I., & Zhang, Y. (2021). Dual-attention-guided network for ghost-free high dynamic range imaging. International Journal of Computer Vision, 130(1), 19 pages.
DOI Scopus44 WoS35
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 Scopus21 WoS17
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 Scopus2135 WoS1675
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 Scopus31 WoS26 Europe PMC1
2020 Teney, D., Kafle, K., Shrestha, R., Abbasnejad, E., Kanan, C., & van den Hengel, A. (2020). On the Value of Out-of-Distribution Testing: An Example of Goodhart's Law. ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 33, NEURIPS 2020, 33, 11 pages.
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 Scopus6 WoS6
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 Scopus74 WoS68
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 Scopus35 WoS44 Europe PMC3
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 Scopus694 WoS551 Europe PMC188
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 Scopus24 WoS21 Europe PMC1
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 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 Scopus100 WoS80 Europe PMC13
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 Scopus32 WoS15
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 Scopus27 WoS20 Europe PMC8
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 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 Scopus28 WoS33
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 Scopus21 WoS17 Europe PMC8
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 Scopus18 WoS15 Europe PMC12
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 Scopus1353 WoS1070
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 Scopus59 WoS52
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 Scopus19 WoS19
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.
WoS12
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 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 Scopus99 WoS90 Europe PMC25
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 Scopus73 WoS88
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 Scopus71 WoS61
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 Scopus352 WoS263 Europe PMC29
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 Scopus13 WoS11
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 Scopus18 WoS17
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 WoS5
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 Scopus403 WoS321 Europe PMC35
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 Scopus56 WoS44 Europe PMC7
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 Scopus52 WoS45
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 Scopus184 WoS165
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 Scopus47 WoS43 Europe PMC5
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 Scopus325 WoS244
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 Scopus39 WoS33
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 Scopus13 WoS8
2017 Teney, D., Wu, Q., & Van Den Hengel, A. (2017). Visual Question Answering: a tutorial. IEEE Signal Processing Magazine, 34(6), 63-75.
DOI Scopus35 WoS23
2017 Zhuang, B., Wu, Q., Shen, C., Reid, I., & Hengel, A. V. D. (2017). Care about you: towards large-scale human-centric visual relationship
detection.
2017 Qiao, R., Liu, L., Shen, C., & Hengel, A. V. D. (2017). Visually Aligned Word Embeddings for Improving Zero-shot Learning.
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 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 Scopus126 WoS107
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 Scopus105 WoS81 Europe PMC12
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 Scopus52 WoS45
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 Scopus75 WoS66 Europe PMC10
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 Scopus75 WoS68 Europe PMC13
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 Scopus179 WoS179 Europe PMC40
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 Scopus28 WoS22 Europe PMC10
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 Scopus108 WoS77
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 Scopus43 WoS40
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 Scopus10 WoS9 Europe PMC1
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 Scopus83 WoS71 Europe PMC16
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 Scopus147 WoS135 Europe PMC28
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 PMC3
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 WoS29 Europe PMC9
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 Scopus90 WoS72 Europe PMC8
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 Scopus21 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 Scopus17 WoS15
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 Scopus87 WoS68 Europe PMC9
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 Scopus23 WoS22
2013 Shen, C., Li, H., & Van Den Hengel, A. (2013). Fully corrective boosting with arbitrary loss and regularization. Neural Networks, 48, 44-58.
DOI Scopus10 WoS7 Europe PMC3
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 Scopus35 WoS33 Europe PMC8
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 Scopus31 WoS24
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 Scopus693 WoS625
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 Scopus63 WoS51 Europe PMC12
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.
Scopus80 WoS64
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 Scopus2 WoS1
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 Scopus11 WoS8
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 Scopus88 WoS123 Europe PMC79
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 Scopus33 WoS31
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 Scopus31
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 Scopus8 Europe PMC1
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 Scopus96 WoS76
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 WoS54 Europe PMC6
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 PMC3
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 Scopus60 WoS47
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 Scopus45 WoS37
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 Scopus150 WoS122
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 Scopus12 WoS8

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 Scopus3
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

Year Citation
2025 Albert, P., Zhang, F. Z., Saratchandran, H., Opazo, C. R., Hengel, A. V. D., & Abbasnejad, E. (2025). RandLoRA: Full rank parameter-efficient fine-tuning of large models.. In ICLR. OpenReview.net.
2025 Zhang, Z., Ng, I., Gong, D., Liu, Y., Gong, M., Huang, B., . . . Shi, J. Q. (2025). ANALYTIC DAG CONSTRAINTS FOR DIFFERENTIABLE DAG LEARNING. In 13th International Conference on Learning Representations Iclr 2025 (pp. 63845-63870).
2025 Huy, T. D., Tran, S. K., Nguyen, P., Tran, N. H., Sam, T. B., Hengel, A. V. D., . . . Phan, V. M. H. (2025). Interactive Medical Image Analysis with Concept-based Similarity Reasoning.. In CVPR (pp. 30797-30806). IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR): Computer Vision Foundation / IEEE.
2025 Opazo, C. R., Abbasnejad, E., Teney, D., Damirchi, H., Marrese-Taylor, E., & Hengel, A. V. D. (2025). Synergy and Diversity in CLIP: Enhancing Performance Through Adaptive Backbone Ensembling.. In ICLR. OpenReview.net.
2025 Zhang, X., Gong, D., Duan, Z., Van Den Hengel, A., & Liu, L. (2025). Let Your Video Listen to Your Music! - Beat-Aligned, Content-Preserving Video Editing with Arbitrary Music. In Mm 2025 Proceedings of the 33rd ACM International Conference on Multimedia Co Located with mm 2025 (pp. 12140-12149). ACM.
DOI
2025 Cong, G., Li, L., Pan, J., Zhang, Z., Beheshti, A., Van Den Hengel, A., . . . Huang, Q. (2025). FlowDubber: Movie Dubbing with LLM-based Semantic-aware Learning and Flow Matching based Voice Enhancing. In Mm 2025 Proceedings of the 33rd ACM International Conference on Multimedia Co Located with mm 2025 (pp. 905-914). ACM.
DOI
2025 Bao, S., Xue, Z., Chen, Q., Ou, S., Beheshti, A., Sheng, Q. Z., . . . Qi, Y. (2025). CausalMVC: Causal Content-Style Representation Learning for Deep Multi-View Clustering. In Mm 2025 Proceedings of the 33rd ACM International Conference on Multimedia Co Located with mm 2025 (pp. 1598-1606). ACM.
DOI
2024 Li, P., Purkait, P., Ajanthan, T., Abdolshah, M., Garg, R., Husain, H., . . . Van Den Hengel, A. (2024). Semi-Supervised Semantic Segmentation under Label Noise via Diverse Learning Groups. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV 2023) (pp. 1229-1238). online: IEEE.
DOI Scopus18 WoS14
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 Scopus33 WoS25
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.. In CVPR (pp. 11072-11081). Seattle, WA, USA: IEEE.
2024 Ramasinghe, S., Shevchenko, V., Avraham, G., & van den Hengel, A. (2024). BLiRF: Band Limited Radiance Fields for Dynamic Scene Modeling. In Proceedings of the AAAI Conference on Artificial Intelligence Vol. 38 (pp. 4641-4649). Online: Association for the Advancement of Artificial Intelligence (AAAI).
DOI Scopus3
2024 Silva, A., Moskvyak, O., Long, A., Garg, R., Gould, S., Avraham, G., & Van Den Hengel, A. (2024). LipAT: Beyond Style Transfer for Controllable Neural Simulation of Lipstick using Cosmetic Attributes. In Proceedings - 2024 IEEE Winter Conference on Applications of Computer Vision, WACV 2024 (pp. 8031-8040). Online: IEEE.
DOI
2024 Chowdhury, T. F., Phan, V. M. H., Liao, K., To, M. -S., Xie, Y., Hengel, A. V. D., . . . Liao, Z. (2024). AdaCBM: An Adaptive Concept Bottleneck Model for Explainable and Accurate Diagnosis.. In M. G. Linguraru, Q. Dou, A. Feragen, S. Giannarou, B. Glocker, K. Lekadir, & J. A. Schnabel (Eds.), MICCAI (10) Vol. 15010 (pp. 35-45). Marrakesh, Morocco: Springer.
2024 Ramasinghe, S., Shevchenko, V., Avraham, G., Husain, H., & van den Hengel, A. (2024). IMPROVING THE CONVERGENCE OF DYNAMIC NERFS VIA OPTIMAL TRANSPORT. In 12th International Conference on Learning Representations, ICLR 2024. Hybrid, Vienna: International Conference on Learning Representations, ICLR.
Scopus2
2024 Liu, Y., Zhang, Z., Gong, D., Gong, M., Huang, B., van den Hengel, A., . . . Shi, J. Q. (2024). IDENTIFIABLE LATENT POLYNOMIAL CAUSAL MODELS THROUGH THE LENS OF CHANGE. In 12th International Conference on Learning Representations, ICLR 2024. Online: ICLR.
Scopus9
2024 Cong, G., Qi, Y., Li, L., Beheshti, A., Zhang, Z., van den Hengel, A., . . . Huang, Q. (2024). StyleDubber: Towards Multi-Scale Style Learning for Movie Dubbing. In Proceedings of the Annual Meeting of the Association for Computational Linguistics (pp. 6767-6779). Hybrid, Bangkok: Association for Computational Linguistics (ACL).
DOI Scopus9 WoS2
2024 Liu, X., Li, G., Qi, Y., Yan, Z., Han, Z., Van Den Hengel, A., . . . Huang, Q. (2024). Weakly Supervised Video Individual Counting. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 19228-19237). Seattle, WA, USA: IEEE.
DOI Scopus3 WoS2
2024 Monteil, J., Vaskovych, V., Lu, W., Majumder, A., & van den Hengel, A. (2024). MARec: Metadata Alignment for cold-start Recommendation. In 18th ACM Conference on Recommender Systems (pp. 401-410). Bari, Italy: ACM.
DOI Scopus3 WoS3
2024 Zhang, Z., Li, L., Cong, G., Yin, H., Gao, Y., Yan, C., . . . Qi, Y. (2024). From Speaker to Dubber: Movie Dubbing with Prosody and Duration Consistency Learning. In Proceedings of the 32nd ACM International Conference on Multimedia (pp. 7523-7532). Melbourne VIC Australia: ACM.
DOI Scopus39 WoS10
2024 Yang, X., Zuo, Y., Ramasinghe, S., Bazzani, L., Avraham, G., & van den Hengel, A. (2024). ViewFusion: Towards Multi-View Consistency via Interpolated Denoising. In 2024 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR) (pp. 9870-9880). WA, Seattle: IEEE COMPUTER SOC.
DOI Scopus5 WoS1
2024 Zhang, F. Z., Albert, P., Rodriguez-Opazo, C., van den Hengel, A., & Abbasnejad, E. (2024). Knowledge Composition using Task Vectors with Learned Anisotropic Scaling. In Advances in Neural Information Processing Systems Vol. 37. Vancouver, Canada: Neural information processing systems foundation.
Scopus4
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.), Proceedings of the 37th Annual Conference on Neural Information Processing Systems as (NeurIPS, 2023) published in Advances in Neural Information Processing Systems Vol. 36 (pp. 32 pages). Online: Neural Information Processing Systems Foundation.
Scopus85 WoS20
2023 Husain, H., Nguyen, V., & van den Hengel, A. (2023). Distributionally Robust Bayesian Optimization with φ-divergences. In Advances in Neural Information Processing Systems Vol. 36 (pp. 13 pages). Online: Neural information processing systems foundation.
Scopus10 WoS2
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 Scopus85 WoS53
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 Scopus20 WoS12
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). Vancouver, BC, Canada: IEEE COMPUTER SOC.
DOI Scopus19 WoS11
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 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 Scopus18 WoS15
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 Scopus96 WoS66
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 Scopus10 WoS9
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 Scopus114 WoS86
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 Scopus68 WoS35
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 Scopus4 WoS3
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 Scopus54 WoS38
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 Scopus103 WoS69
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 Scopus2 WoS2
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 Scopus54 WoS28
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 Scopus79 WoS65
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 Scopus112 WoS76
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 Scopus6 WoS4
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 Scopus13 WoS11
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 Scopus105 WoS73
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 Scopus12 WoS9
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) (pp. 1397-1407). Los Alamitos, CA: IEEE.
DOI Scopus53 WoS33
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 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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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2018 Ehsan Abbasnejad, M., Dick, A., Shi, Q., & Van Den Hengel, A. (2018). Active learning from noisy tagged images. In British Machine Vision Conference 2018 Bmvc 2018.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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 Scopus16 WoS15
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 Scopus43 WoS26
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.
Scopus113
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 Scopus204 WoS160
2013 Wang, Z., Shi, Q., Shen, C., & Van Den Hengel, A. (2013). Bilinear programming for human activity recognition with unknown MRF graphs. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 1690-1697). United States of America: IEEE.
DOI Scopus45 WoS28
2013 Shen, F., Shen, C., Shi, Q., Van Den Hengel, A., & Tang, Z. (2013). Inductive hashing on manifolds. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 1562-1569). United States of America: IEEE.
DOI Scopus219 WoS171
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 Scopus24 WoS12
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.
Scopus34
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 Scopus12
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 Scopus44 WoS34
2012 Yao, R., Shi, Q., Shen, C., Zhang, Y., & Van Den Hengel, A. (2012). Robust tracking with weighted online structured learning. In Proceedings of the 2012 European Conference on Computer Vision, ECCV 2012 Vol. 7574 LNCS (pp. 158-172). Germany: Springer-Verlag.
DOI Scopus30 WoS25
2012 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 WoS46
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 Scopus41 WoS30
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 Scopus270 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 Scopus193 WoS147
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 Scopus28
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 WoS21
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.
Scopus66
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 Scopus29 WoS18
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 Scopus128
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 (pp. 86). 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 Vol. 1 (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 Scopus40 WoS19
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 Scopus5 WoS1
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 Scopus49 WoS35
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

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.

Year Citation
2021 Authors: Anderson L, Van Den Hengel A, Sherrah J, Ward B. Title: AI BIBLE (working title). Extent: indeterminate.
2021 Authors: Hajdu T, Anderson L, Van Den Hengel A, Sherrah J, Ward B. Title: Untitled Work. Extent: unknown.
2020 Authors: Hajdu T, Van Den Hengel A, Anderson L. Title: Art Intelligence Annual Artificial Artist in Residence: Laurie Anderson. Extent: Indeterminate.

Year Citation
2023 Anderson, L. (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.

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

Year Citation
2025 Huy, T. D., Tran, S. K., Nguyen, P., Tran, N. H., Sam, T. B., Hengel, A. V. D., . . . Phan, V. M. H. (2025). Interactive Medical Image Analysis with Concept-based Similarity
Reasoning.
2025 Albert, P., Zhang, F. Z., Saratchandran, H., Rodriguez-Opazo, C., Hengel, A. V. D., & Abbasnejad, E. (2025). RandLoRA: Full-rank parameter-efficient fine-tuning of large models.
2025 Cai, Y., Liu, Y., Gao, E., Jiang, T., Zhang, Z., Hengel, A. V. D., & Shi, J. Q. (2025). On the Value of Cross-Modal Misalignment in Multimodal Representation
Learning.
2025 Huy, T. D., Huynh, D. A., Xie, Y., Qi, Y., Chen, Q., Nguyen, P. L., . . . Phan, V. M. H. (2025). Seeing the Trees for the Forest: Rethinking Weakly-Supervised Medical
Visual Grounding.
2025 Shinnick, Z., Jiang, L., Saratchandran, H., Hengel, A. V. D., & Teney, D. (2025). Transformers Pretrained on Procedural Data Contain Modular Structures
for Algorithmic Reasoning.
2024 Chen, Q., Zhao, R., Wang, S., Phan, V. M. H., Hengel, A. V. D., Verjans, J., . . . Wu, Q. (2024). A Survey of Medical Vision-and-Language Applications and Their
Techniques.
2024 Zhang, F. Z., Albert, P., Rodriguez-Opazo, C., Hengel, A. V. D., & Abbasnejad, E. (2024). Knowledge Composition using Task Vectors with Learned Anisotropic
Scaling.
2024 Rodriguez-Opazo, C., Abbasnejad, E., Teney, D., Damirchi, H., Marrese-Taylor, E., & Hengel, A. V. D. (2024). Synergy and Diversity in CLIP: Enhancing Performance Through Adaptive
Backbone Ensembling.
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.
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.
2021 Moghaddam, M. K., Abbasnejad, E., Wu, Q., Shi, J., & Hengel, A. V. D. (2021). Learning for Visual Navigation by Imagining the Success.
2020 Teney, D., Abbasnejad, E., & Hengel, A. V. D. (2020). Unshuffling Data for Improved Generalization..
2019 Parvaneh, A., Abbasnejad, E., Wu, Q., & Shi, J. (2019). Show, Price and Negotiate: A Hierarchical Attention Recurrent Visual Negotiator..
2017 Abbasnejad, M. E., Shi, Q., Abbasnejad, I., Hengel, A. V. D., & Dick, A. R. (2017). Bayesian Conditional Generative Adverserial Networks..
2017 Wu, Z., Shen, C., & Hengel, A. V. D. (2017). Real-time Semantic Image Segmentation via Spatial Sparsity.
2016 Wu, L., Shen, C., & Hengel, A. V. D. (2016). PersonNet: Person Re-identification with Deep Convolutional Neural
Networks.
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.
2012 Shen, C., Paisitkriangkrai, S., & Hengel, A. V. D. (2012). A Direct Approach to Multi-class Boosting and Extensions.
2010 Shen, C., Wang, P., & Hengel, A. V. D. (2010). Optimally Training a Cascade Classifier.

Date Role Research Topic Program Degree Type Student Load Student Name
2025 Principal Supervisor Transformers for Generative Multimodal AI Master of Philosophy Master Full Time Mr Zachary Liptak Shinnick
2025 Co-Supervisor The project studies how human-like Al features influence perceptions of machine consciousness and human behaviors. It develops experimental tools, a mind perception measure, and publishes methods, datasets, and findings to advance understandings of human-Al interaction. Doctor of Philosophy Doctorate Full Time Mr Oliver Craig Lack
2025 Principal Supervisor Transformers for Generative Multimodal AI Master of Philosophy Master Full Time Mr Zachary Liptak Shinnick
2025 Co-Supervisor The project studies how human-like Al features influence perceptions of machine consciousness and human behaviors. It develops experimental tools, a mind perception measure, and publishes methods, datasets, and findings to advance understandings o Doctor of Philosophy Doctorate Full Time Mr Oliver Craig Lack
2024 Co-Supervisor Investigating Human-AI Interactions: An Empirical Foundation to Understand and Measure Human Perceptions of Machine Consciousness - Master Full Time Mr Oliver Craig Lack
2020 Principal Supervisor Scalable deep learning for scene understanding Doctor of Philosophy Doctorate Part Time Mr James Paul Bockman
2020 Principal Supervisor Scalable deep learning for scene understanding Doctor of Philosophy Doctorate Full Time Mr James Paul Bockman

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

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 - -

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

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