Outputs

*Adarve, J. D., & Mahony, R. (2017). Spherepix: A Data Structure for Spherical Image Processing. IEEE Robotics and Automation Letters, 2(2), 483–490. http://doi.org/10.1109/LRA.2016.26
45119

Anderson, B. D. O., Shi, G., & Trumpf, J. (2017). Convergence and State Reconstruction of Time-Varying Multi-Agent Systems From Complete Observability Theory. IEEE Transactions on Automatic Control, 62(5), 2519–2523. http://doi.org/10.1109/TAC.2016.259
9274

Andert, F., Ammann, N., Krause, S., Lorenz, S., Bratanov, D., & Mejias, L. (2017). Optical-Aided Aircraft Navigation using Decoupled Visual SLAM with Range Sensor Augmentation. Journal of Intelligent & Robotic Systems, 1–19. http://doi.org/10.1007/s10846-016-0457-6

Armin, M. A., Barnes, N., Alvarez, J., Li, H., Grimpen, F., & Salvado, O. (2017, September 14). Learning Camera Pose from Optical Colonoscopy Frames Through Deep Convolutional Neural Network (CNN). Springer. https://doi.org/10.1007/978-3-319-67543-5_5

Ball, D., Ross, P., English, A., Milani, P., Richards, D., Bate, A., Upcroft, B., Wyeth, G., Corke, P. (2017). Farm Workers of the Future: Vision-Based Robotics for Broad-Acre Agriculture. IEEE Robotics & Automation Magazine, 1–1. http://doi.org/10.1109/MRA.2016.261
6541

*Bangura, M., & Mahony, R. (2017). Thrust Control for Multirotor Aerial Vehicles. IEEE Transactions on Robotics, 33(2), 390–405. http://doi.org/10.1109/TRO.2016.263
3562

Bawden, O., Kulk, J., Russell, R., McCool, C., English, A., Dayoub, F., Lehnert, C., and Perez, T. (2017). Robot for weed species plant-specific management. Journal of Field Robotics, 34(6), 1179–1199. http://doi.org/10.1002/rob.21727

Bewley, A., & Upcroft, B. (2017). Background Appearance Modeling with Applications to Visual Object Detection in an Open-Pit Mine. Journal of Field Robotics, 34(1), 53–73. http://doi.org/10.1002/rob.21667

Bongiorno, D. L., Bryson, M., Bridge, T. C. L., Dansereau, D. G., & Williams, S. B. (2017). Coregistered Hyperspectral and Stereo Image Seafloor Mapping from an Autonomous Underwater Vehicle. Journal of Field Robotics. http://doi.org/10.1002/rob.21713

Bruce, J., Jacobson, A., & Milford, M. (2017). Look No Further: Adapting the Localization Sensory Window to the Temporal Characteristics of the Environment. IEEE Robotics and Automation Letters, 2(4), 2209–2216. http://doi.org/10.1109/LRA.2017.272
4146

*Drory, A., Li, H., & Hartley, R. (2017). A learning-based markerless approach for full-body kinematics estimation in-natura from a single image. Journal of Biomechanics, 55, 1–10. http://doi.org/10.1016/j.jbiomech.2017
.01.028

*Drory, A., Li, H., & Hartley, R. (2017). Estimating the projected frontal surface area of cyclists from images using a variational framework and statistical shape and appearance models. Proceedings of the Institution of Mechanical Engineers, Part P: Journal of Sports Engineering and Technology. https://doi.org/10.1177/1754337117
705489

*Drory, A., Zhu, G., Li, H., & Hartley, R. (2017). Automated detection and tracking of slalom paddlers from broadcast image sequences using cascade classifiers and discriminative correlation filters. Computer Vision and Image Understanding, 159(June 2017), 116–127. http://doi.org/10.1016/J.CVIU.2016.1
2.002

Fan, C., Chen, Z., Jacobson, A., Hu, X., & Milford, M. (2017). Biologically-inspired visual place recognition with adaptive multiple scales. Robotics and Autonomous Systems, 96, 224–237. http://doi.org/10.1016/J.ROBOT.2017
.07.015

*Fernando, B., & Gould, S. (2017). Discriminatively Learned Hierarchical Rank Pooling Networks. International Journal of Computer Vision, 124(3), 335–355. http://doi.org/10.1007/s11263-017-1030-x

*Fernando, B., Gavves, E., Oramas M., J. O., Ghodrati, A., & Tuytelaars, T. (2017). Rank Pooling for Action Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(4), 773–787. http://doi.org/10.1109/TPAMI.2016
.2558148

Hinas, A., Roberts, J., & Gonzalez, F. (2017). Vision-Based Target Finding and Inspection of a Ground Target Using a Multirotor UAV System. Sensors, 17(12), 2929. http://doi.org/10.3390/s17122929

Hoang, T., Do, T.-T., Tan, D.-K. Le, & Cheung, N.-M. (2017). Enhance Feature Discrimination for Unsupervised Hashing. Retrieved from http://arxiv.org/abs/1704.01754

*Ila, V., Polok, L., Solony, M., & Svoboda, P. (2017). SLAM++ -A highly efficient and temporally scalable incremental SLAM framework. The International Journal of Robotics Research, 36(2), 210–230. http://doi.org/10.1177/027836491
7691110

Irons, J. L., Gradden, T., Zhang, A., He, X., Barnes, N., Scott, A. F., & McKone, E. (2017). Face identity recognition in simulated prosthetic vision is poorer than previously reported and can be improved by caricaturing. Vision Research, 137, 61–79. https://doi.org/10.1016/j.visres.2017
.06.002

Jaiprakash, A., O’Callaghan, W. B., Whitehouse, S. L., Pandey, A., Wu, L., Roberts, J., & Crawford, R. W. (2017). Orthopaedic surgeon attitudes towards current limitations and the potential for robotic and technological innovation in arthroscopic surgery. Journal of Orthopaedic Surgery, 25(1), 230949901668499. http://doi.org/10.1177/2309499016
684993

*James, J., Ford, J. J., & Molloy, T. L. (2017). Change Detection for Undermodelled Processes Using Mismatched Hidden Markov Model Test Filters. IEEE Control Systems Letters, 1(2), 238–243. http://doi.org/10.1109/LCSYS.2017
.2713825

Kim, J., Cheng, J., Guivant, J., & Nieto, J. (2017). Compressed fusion of GNSS and inertial navigation with simultaneous localization and mapping. IEEE Aerospace and Electronic Systems Magazine, 32(8), 22–36. https://doi.org/10.1109/MAES.2017
.8071552

*Kumar, S., Dai, Y., & Li, H. (2017). Spatio-temporal union of subspaces for multi-body non-rigid structure-from-motion. Pattern Recognition, 71, 428–443. http://doi.org/10.1016/J.PATCOG.2017
.05.014

Lai, T., Wang, H., Yan, Y., Xiao, G., & Suter, D. (2017). Efficient guided hypothesis generation for multi-structure epipolar geometry estimation. Computer Vision and Image Understanding, 154, 152–165. http://doi.org/10.1016/J.CVIU.2016
.10.003

Latif, Y., Huang, G., Leonard, J., & Neira, J. (2017). Sparse optimization for robust and efficient loop closing. Robotics and Autonomous Systems, 93, 13–26. http://doi.org/10.1016/J.ROBOT.2017
.03.016

*Lehnert, C., English, A., McCool, C., Tow, A. W., & Perez, T. (2017). Autonomous Sweet Pepper Harvesting for Protected Cropping Systems. IEEE Robotics and Automation Letters, 2(2), 872–879. http://doi.org/10.1109/LRA.2017
.2655622

Li, Z., Wu, L., Ren, H., & Yu, H. (2017). Kinematic comparison of surgical tendon-driven manipulators and concentric tube manipulators. Mechanism and Machine Theory, 107, 148–165. http://doi.org/10.1016/j.mechmach
theory.2016.09.018

*Lin, G., Shen, C., van den Hengel, A., & Reid, I. (2017). Exploring Context with Deep Structured models for Semantic Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1–1. http://doi.org/10.1109/TPAMI.2017
.2708714 *In Press

*Liu, F., Lin, G., Qiao, R., & Shen, C. (2017). Structured Learning of Tree Potentials in CRF for Image Segmentation. IEEE Transactions on Neural Networks and Learning Systems, PP(99), 1–7. http://doi.org/10.1109/TNNLS.2017
.2690453 *In Press

*Liu, L., Li, H., Dai, Y., & Pan, Q. (2017). Robust and Efficient Relative Pose With a Multi-Camera System for Autonomous Driving in Highly Dynamic Environments. IEEE Transactions on Intelligent Transportation Systems, 1–13. https://doi.org/10.1109/TITS.2017
.2749409 *In Press

*Liu, L., Shen, C., & Hengel, A. van den. (2017). Cross-Convolutional-Layer Pooling for Image Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(11), 2305–2313. http://doi.org/10.1109/TPAMI.2016
.2637921

Lu, H., Cao, Z., Xiao, Y., Zhuang, B., & Shen, C. (2017). TasselNet: counting maize tassels in the wild via local counts regression network. Plant Methods, 13(1), 79. http://doi.org/10.1186/s13007-017-0224-0

Ma, C., Yang, C.-Y., Yang, X., & Yang, M.-H. (2017). Learning a no-reference quality metric for single-image super-resolution. Computer Vision and Image Understanding, 158, 1–16. http://doi.org/10.1016/J.CVIU.2016
.12.009

*Marmol, A., Peynot, T., Eriksson, A., Jaiprakash, A., Roberts, J., & Crawford, R. (2017). Evaluation of Keypoint Detectors and Descriptors in Arthroscopic Images for Feature-Based Matching Applications. IEEE Robotics and Automation Letters, 2(4), 2135–2142. http://doi.org/10.1109/LRA.2017
.2714150

*McCool, C., Perez, T., & Upcroft, B. (2017). Mixtures of Lightweight Deep Convolutional Neural Networks: Applied to Agricultural Robotics. IEEE Robotics and Automation Letters, 2(3), 1344–1351. http://doi.org/10.1109/LRA.2017
.2667039

*McFadyen, A., Jabeur, M., & Corke, P. (2017). Image-Based Visual Servoing With Unknown Point Feature Correspondence. IEEE Robotics and Automation Letters, 2(2), 601–607. http://doi.org/10.1109/LRA.2016
.2645886

Molloy, Timothy L., Ford, Jason J., & Mejias, L. (2017). Detection of Aircraft Below The Horizon for Vision-Based Detect And Avoid in Unmanned Aircraft Systems. Journal of Field Robotics. http://doi.org/10.1002/rob.21719

*Nascimento, J. C., & Carneiro, G. (2017). Deep Learning on Sparse Manifolds for Faster Object Segmentation. IEEE Transactions on Image Processing, 26(10), 4978–4990. http://doi.org/10.1109/TIP.2017
.2725582

Nguyen, K., Fookes, C., Jillela, R., Sridharan, S., & Ross, A. (2017). Long Range Iris Recognition: A Survey. Pattern Recognition. http://doi.org/10.1016/j.patcog.2017
.05.021

Oakden-Rayner, L., Carneiro, G., Bessen, T., Nascimento, J. C., Bradley, A. P., & Palmer, L. J. (2017). Precision Radiology: Predicting longevity using feature engineering and deep learning methods in a radiomics framework. Scientific Reports, 7(1), 1648. http://doi.org/10.1038/s41598-017-01931-w

Parra Bustos, A., & Chin, T.-J. (2017). Guaranteed Outlier Removal for Point Cloud Registration with Correspondences. IEEE Transactions on Pattern Analysis and Machine Intelligence. http://doi.org/10.1109/TPAMI.2017
.2773482 *In Press

Petoe, M. A., McCarthy, C. D., Shivdasani, M. N., Sinclair, N. C., Scott, A. F., Ayton, L. N., … Blamey, P. J. (2017). Determining the Contribution of Retinotopic Discrimination to Localization Performance With a Suprachoroidal Retinal Prosthesis. Investigative Opthalmology & Visual Science, 58(7), 3231. https://doi.org/10.1167/iovs.16-21041

Ren, C. Y., Prisacariu, V. A., Kähler, O., Reid, I. D., Murray, D. W. (2017). Real-Time Tracking of Single and Multiple Objects from Depth-Colour Imagery Using 3D Signed Distance Functions. International Journal of Computer Vision, 124, 80–95. https://doi.org/10.1007/s11263-016-0978-2

*Sa, I., Lehnert, C., English, A., McCool, C., Dayoub, F., Upcroft, B., & Perez, T. (2017). Peduncle Detection of Sweet Pepper for Autonomous Crop Harvesting—Combined Color and 3-D Information. IEEE Robotics and Automation Letters, 2(2), 765–772. http://doi.org/10.1109/LRA.2017
.2651952

*Saleh, F., Aliakbarian, M. S., Salzmann, M., Petersson, L., Alvarez, J. M., & Gould, S. (2017). Incorporating Network Built-in Priors in Weakly-supervised Semantic Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, PP(0), 1–1. http://doi.org/10.1109/TPAMI.
2017.2713785 *In Press

Sandino, J., Wooler, A., & Gonzalez, F. (2017). Towards the Automatic Detection of Pre-Existing Termite Mounds through UAS and Hyperspectral Imagery. Sensors, 17(10), 2196. http://doi.org/10.3390/s17102196

Stacey, G., & Mahony, R. (2017). The Role of Symmetry in Rigidity Analysis: A Tool for Network Localisation and Formation Control. IEEE Transactions on Automatic Control. Retrieved from http://ieeexplore.ieee.org/document/80
23875/ *In Press

Stronks, H. C., Walker, J., Parker, D. J., & Barnes, N. (2017). Training Improves Vibrotactile Spatial Acuity and Intensity Discrimination on the Lower Back Using Coin Motors. Artificial Organs. https://doi.org/10.1111/aor.12882

*Teney, D., Wu, Q., & van den Hengel, A. (2017). Visual Question Answering: A Tutorial. IEEE Signal Processing Magazine, 34(6), 63–75. https://doi.org/10.1109/MSP.2017
.2739826

Trumpf, J., & Trentelman, H. L. (2017). A converse to the deterministic separation principle. Systems & Control Letters, 101, 2–9. http://doi.org/10.1016/j.sysconle.2016
.02.021

*Tsai, D., Dansereau, D. G., Peynot, T., & Corke, P. (2017). Image-Based Visual Servoing With Light Field Cameras. IEEE Robotics and Automation Letters, 2(2), 912–919. http://doi.org/10.1109/LRA.2017
.2654544

Villa, T. F., Jayaratne, E. R., Gonzalez, L. F., & Morawska, L. (2017). Determination of the vertical profile of particle number concentration adjacent to a motorway using an unmanned aerial vehicle. Environmental Pollution, 230, 134–142. http://doi.org/10.1016/j.envpol.2017
.06.033

Wu, L., & Ren, H. (2017). Finding the Kinematic Base Frame of a Robot by Hand-Eye Calibration Using 3D Position Data. IEEE Transactions on Automation Science and Engineering, 14(1), 314–324. http://doi.org/10.1109/TASE.2016
.2517674

*Zhang, Q., & Chin, T.-J. (2017). Coresets for Triangulation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1–1. http://doi.org/10.1109/TPAMI.2017
.2750672 *In Press

Zulqarnain Gilani, S., Mian, A., Shafait, F., & Reid, I. (2017). Dense 3D Face Correspondence. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1-1. http://doi.org/10.1109/TPAMI.2017
.2725279 *In Press

(2017). Infinite Variational Autoencoder for Semi-Supervised Learning. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 781–790). Honolulu, USA: IEEE. http://doi.org/10.1109/CVPR.2017.90

Bista, S. R., Giordano, P. R., & Chaumette, F. (2017). Combining Line Segments and Points for Appearance- based Indoor Navigation by Image Based Visual Servoing. In IROS 2017 – IEEE/RSJ International Conference on Intelligent Robots and Systems (pp. 2960–2967). Vancouver. Retrieved from https://hal.inria.fr/hal-01572353/

Bratanov, D., Mejias, L., & Ford, J. J. (2017). A vision-based sense-and-avoid system tested on a ScanEagle UAV. International Conference on Unmanned Aerial Systems (ICUAS) 2017. Retrieved from https://eprints.qut.edu.au/108459/

*Campbell, D., Petersson, L., Kneip, L., & Li, H. (2017). Globally-Optimal Inlier Set Maximisation for Simultaneous Camera Pose and Feature Correspondence. In 2017 IEEE International Conference on Computer Vision (ICCV) (pp. 1–10). Venice, Italy: IEEE. http://doi.org/10.1109/ICCV.2017.10

*Chen, Z., Jacobson, A., Sünderhauf, N., Upcroft, B., Liu, L., Shen, C., Reid, I., & Milford, M. (2017). Deep learning features at scale for visual place recognition. In 2017 IEEE International Conference on Robotics and Automation (ICRA) (pp. 3223–3230). Singapore: IEEE. http://doi.org/10.1109/ICRA.2017
.7989366

*Cherian, A., Koniusz, P., & Gould, S. (2017). Higher-Order Pooling of CNN Features via Kernel Linearization for Action Recognition. In 2017 IEEE Winter Conference on Applications of Computer Vision (WACV) (pp. 130–138). Santa Rosa, CA: IEEE. http://doi.org/10.1109/WACV.2017.22

*Cherian, A., Fernando, B., Harandi, M., & Gould, S. (2017). Generalized Rank Pooling for Activity Recognition. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 1581–1590). Honolulu, HI, USA: IEEE. http://doi.org/10.1109/CVPR.2017.172

Chirikjian, G. S., Mahony, R., Ruan, S., & Trumpf, J. (2017). Pose Changes From a Different Point of View. In ASME 2017 41st Mechanisms and Robotics Conference. Cleveland, Ohio: ASME. http://doi.org/10.1115/DETC2017-67725

*Dharmasiri, T., Spek, A., & Drummond, T. (2017). Joint prediction of depths, normals and surface curvature from RGB images using CNNs. In 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 1505–1512). Vancouver, Canada: IEEE. http://doi.org/10.1109/IROS.2017
.8205954

Fernando, T., Denman, S., Sridharan, S., & Fookes, C. (2017). Going deeper: Autonomous steering with neural memory networks. In IEEE Conference on Computer Vision and Pattern Recognition. Hawaii. Retrieved from https://eprints.qut.edu.au/114117/

*Garg, S., Jacobson, A., Kumar, S., & Milford, M. (2017). Improving Condition- and Environment-Invariant Place Recognition with Semantic Place Categorization. Retrieved from http://arxiv.org/abs/1706.07144

Gong, D., Tan, M., Zhang, Y., Hengel, A. van den, & Shi, Q. (2017). Self-Paced Kernel Estimation for Robust Blind Image Deblurring. In 2017 IEEE International Conference on Computer Vision (ICCV) (pp. 1670–1679). Venice, Italy: IEEE. http://doi.org/10.1109/ICCV.2017.184

Gonzalez, F., & Johnson, S. (2017). Standard operating procedures for UAV or drone based monitoring of wildlife. In Proceedings of Unmanned Aircraft Systems for Remote Sensing) UAS4RS 2017. Hobart, Tasmania. Retrieved from https://eprints.qut.edu.au/108859/

Hall, D., Dayoub, F., Perez, T., & McCool, C. (2017). A Transplantable System for Weed Classification by Agricultural Robotics. Retrieved from http://www.ferasdayoub.com/wp-content/uploads/2014/12/IROS17
_0494_FI.pdf

*Harandi, M., Salzmann, M., & Hartley, R. (2017). Joint Dimensionality Reduction and Metric Learning: A Geometric Take. In Proceedings of the 34th International Conference on Machine Learning (ICML). Sydney, Australia.

*Harwood, B., G, V. K. B., Carneiro, G., Reid, I., & Drummond, T. (2017). Smart Mining for Deep Metric Learning. In 2017 IEEE International Conference on Computer Vision (ICCV) (pp. 2840–2848). Venice, Italy: IEEE. http://doi.org/10.1109/ICCV.2017.307

*Henein, M., Abello, M., Ila, V., & Mahony, R. (2017). Exploring The Effect of Meta-Structural Information on the Global Consistency of SLAM. Retrieved from http://viorelaila.net/wp-content/uploads/2016/06/Henein17
iros.compressed.pdf

*Hua, M.-D., Hamel, T., Mahony, R., & Allibert, G. (2017). Explicit Complementary Observer Design on Special Linear Group SL(3) for Homography Estimation using Conic Correspondences. In 56th IEEE Conference on Decision and Control (CDC). Melbourne, Australia. Retrieved from https://hal.archives-ouvertes.fr/hal-01628177/

*Hua, M.-D., Hamel, T., Mahony, R., & Allibert, G. (2017). SL(3) for Homography Estimation using Conic Correspondences. Retrieved from https://hal.archives-ouvertes.fr/hal-01628177/document

*Hua, M.-D., Trumpf, J., Hamel, T., Mahony, R., & Morin, P. (2017). Point and line feature-based observer design on SL(3) for Homography estimation and its application to image stabilization. In International Conference on Robotics and Automation (ICRA). Singapore. Retrieved from https://hal.archives-ouvertes.fr/hal-01628175/

*Ila, Viorela, Polok, Lukas, Solony, Marek, Istenic, K. (2017). Fast Incremental Bundle Adjustment with Covariance Recovery. In International Conference on 3D Vision (3DV).

*Istenic, K., Ila, V., Polok, L., Gracias, N., & Garcia, R. (2017). Mission-time 3D reconstruction with quality estimation. In OCEANS 2017 – Aberdeen (pp. 1–9). Aberdeen, UK: IEEE. http://doi.org/10.1109/OCEANSE.2017
.8084708

*James, J., Ford, J. J., & Molloy, T. L. (2017). Quickest detection of intermittent signals with estimated anomaly times. In 2017 Asian Control Conference – ASCC 2017. Gold Coast. Retrieved from https://eprints.qut.edu.au/112160/

*Ji, P., Li, H., Dai, Y., & Reid, I. (2017). “Maximizing Rigidity” Revisited: A Convex Programming Approach for Generic 3D Shape Reconstruction from Multiple Perspective Views. In 2017 IEEE International Conference on Computer Vision (ICCV) (pp. 929–937). Venice, Italy: IEEE. http://doi.org/10.1109/ICCV.2017.106

*Ji, P., Zhang, T., Li, H., & Salzmann EPFL -CVLab Ian Reid, M. (2017). Deep Subspace Clustering Networks. In 31st Conference on Neural Information Processing Systems (NIPS 2017). Long Beach, CA, USA. Retrieved from http://papers.nips.cc/paper/6608-deep-subspace-clustering-networks.pdf

*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 IEEE International Conference of Computer Vision (ICCV) (pp. 939–948). Venice. Retrieved from http://openaccess.thecvf.com/content
_ICCV_2017_workshops/w17/html/
Johnston_Scaling_CNNs_for_ICCV_
2017_paper.html

*Khosravian, A., Chin, T.-J., Reid, I., & Mahony, R. (2017). A discrete-time attitude observer on SO(3) for vision and GPS fusion. In 2017 IEEE International Conference on Robotics and Automation (ICRA) (pp. 5688–5695). Singapore: IEEE. http://doi.org/10.1109/ICRA.2017
.7989669

*Kiani, K. A., & Drummond, T. (2017). Solving Robust Regularization Problems Using Iteratively Re-weighted Least Squares. In 2017 IEEE Winter Conference on Applications of Computer Vision (WACV) (pp. 483–492). Santa Rosa, CA: IEEE. http://doi.org/10.1109/WACV.2017.60

*Kim, J.-H., Latif, Y., & Reid, I. (2017). RRD-SLAM: Radial-distorted rolling-shutter direct SLAM. In 2017 IEEE International Conference on Robotics and Automation (ICRA) (pp. 5148–5154). Singapore: IEEE. http://doi.org/10.1109/ICRA.2017
.7989602

*Kumar, S., Dai, Y., & Li, H. (2017). Monocular Dense 3D Reconstruction of a Complex Dynamic Scene from Two Perspective Frames. In 2017 IEEE International Conference on Computer Vision (ICCV) (pp. 4659–4667). Venice, Italy: IEEE. http://doi.org/10.1109/ICCV.2017.498

*Le, H., Chin, T.-J., & Suter, D. (2017). An Exact Penalty Method for Locally Convergent Maximum Consensus. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 379–387). Honolulu, USA: IEEE. http://doi.org/10.1109/CVPR.2017.48

*Le, H., Chin, T.-J., & Suter, D. (2017). RATSAC – Random Tree Sampling for Maximum Consensus Estimation. In 2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA) (pp. 1–8). Sydney, Australia: IEEE. http://doi.org/10.1109/DICTA.2017
.8227480

*Leitner, J., Tow, A. W., Sünderhauf, N., Dean, J. E., Durham, J. W., Cooper, M., Eich, M., Lehnert, C., Mangels, R., McCool, C., Kujala, Peter T., Nicholson, L., Pham, T., Sergeant, J., Wu, L., Zhang, F., Upcroft, B., and Corke, P. (2017). The ACRV picking benchmark: A robotic shelf picking benchmark to foster reproducible research. In 2017 IEEE International Conference on Robotics and Automation (ICRA) (pp. 4705–4712). Singapore: IEEE. http://doi.org/10.1109/ICRA.2017
.7989545

*Li, H., Wang, P., & Shen, C. (2017). Towards End-to-End Text Spotting with Convolutional Recurrent Neural Networks. In 2017 IEEE International Conference on Computer Vision (ICCV) (pp. 5248–5256). Venice, Italy: IEEE. http://doi.org/10.1109/ICCV.2017.560

*Liu, L., Li, H., & Dai, Y. (2017). Efficient Global 2D-3D Matching for Camera Localization in a Large-Scale 3D Map. In 2017 IEEE International Conference on Computer Vision (ICCV) (pp. 2391–2400). Venice, Italy: IEEE. http://doi.org/10.1109/ICCV.2017.260

Lu, H., Zhang, L., Cao, Z., Wei, W., Xian, K., Shen, C., & Hengel, A. van den. (2017). When Unsupervised Domain Adaptation Meets Tensor Representations. In 2017 IEEE International Conference on Computer Vision (ICCV) (pp. 599–608). Venice, Italy: IEEE. http://doi.org/10.1109/ICCV.2017.72

*Maicas, G., Carneiro, G., & Bradley, A. P. (2017). Globally optimal breast mass segmentation from DCE-MRI using deep semantic segmentation as shape prior. In 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017) (pp. 305–309). Melbourne, Australia: IEEE. http://doi.org/10.1109/ISBI.2017.795
0525

*Menikdiwela, M., Li, H., Nguyen, C., Shaw, S., (2017), CNN-based small object detection and visualization with feature activation mapping, IVCNZ2017, Canterbury, New Zealand

*Meyer, B. J., & Drummond, T. (2017). Improved semantic segmentation for robotic applications with hierarchical conditional random fields. In 2017 IEEE International Conference on Robotics and Automation (ICRA) (pp. 5258–5265). Singapore: IEEE. http://doi.org/10.1109/ICRA.2017.7989617

*Nguyen, C. V., Milford, M., & Mahony, R. (2017). 3D tracking of water hazards with polarized stereo cameras. In 2017 IEEE International Conference on Robotics and Automation (ICRA) (pp. 5251–5257). Singapore: IEEE. http://doi.org/10.1109/ICRA.2017
.7989616

*Rezatofighi, S. H., G, V. K. B., Milan, A., Abbasnejad, E., Dick, A., & Reid, I. (2017). DeepSetNet: Predicting Sets with Deep Neural Networks. In 2017 IEEE International Conference on Computer Vision (ICCV) (pp. 5257–5266). Venice, Italy: IEEE. http://doi.org/10.1109/ICCV.2017.561

*Rezazadegan, F., Shirazi, S., Upcroft, B., & Milford, M. (2017). Action recognition: From static datasets to moving robots. In 2017 IEEE International Conference on Robotics and Automation (ICRA) (pp. 3185–3191). Singapore: IEEE. http://doi.org/10.1109/ICRA.2017
.7989361

*Sadegh Aliakbarian, M., Sadat Saleh, F., Salzmann, M., Fernando, B., Petersson, L., & Andersson, L. (2017). Encouraging LSTMs to Anticipate Actions Very Early. In The IEEE International Conference on Computer Vision (ICCV), 2017 (pp. 280–289). Venice, Italy. Retrieved from http://openaccess.thecvf.com/content
_iccv_2017/html/Aliakbarian_Encouraging
_LSTMs_to_ICCV_2017_paper.html

*Song, Y., Ma, C., Gong, L., Zhang, J., Lau, R. W. H., & Yang, M.-H. (2017). CREST: Convolutional Residual Learning for Visual Tracking. In 2017 IEEE International Conference on Computer Vision (ICCV) (pp. 2574–2583). Venice, Italy: IEEE. http://doi.org/10.1109/ICCV.2017.279

*Spek, A., Drummond, T. (2017) A compact parametric solution to depth sensor calibration. In 28th British Machine Vision Conference (BMVC). London: https://bmvc2017.london/proceedings/

*Spek, A., & Drummond, T. (2017). Joint pose and principal curvature refinement using quadrics. In 2017 IEEE International Conference on Robotics and Automation (ICRA) (pp. 3968–3975). Singapore: IEEE. http://doi.org/10.1109/ICRA.2017
.7989456

* Sünderhauf, N., Pham, T. T., Latif, Y., Milford, M., & Reid, I. (2017). Meaningful maps with object-oriented semantic mapping. In 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 5079–5085). Vancouver, BC, Canada: IEEE. http://doi.org/10.1109/IROS.2017
.8206392

*Wang, J., Cherian, A., & Porikli, F. (2017). Ordered Pooling of Optical Flow Sequences for Action Recognition. In 2017 IEEE Winter Conference on Applications of Computer Vision (WACV) (pp. 168–176). Santa Rosa, CA: IEEE. http://doi.org/10.1109/WACV.2017.26

*Wang, P., Liu, L., Shen, C., Huang, Z., Hengel, A. van den, & Shen, H. T. (2017). Multi-attention Network for One Shot Learning. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 6212–6220). Honolulu, HI, USA: IEEE. http://doi.org/10.1109/CVPR.2017.658

*Weberruss, J., Kleeman, L., Boland, D., & Drummond, T. (2017). FPGA acceleration of multilevel ORB feature extraction for computer vision. In 2017 27th International Conference on Field Programmable Logic and Applications (FPL) (pp. 1–8). Ghent, Belgium: IEEE. http://doi.org/10.23919/FPL.2017
.8056856

*Weerasekera, C. S., Latif, Y., Garg, R., & Reid, I. (2017). Dense monocular reconstruction using surface normals. In 2017 IEEE International Conference on Robotics and Automation (ICRA) (pp. 2524–2531). IEEE. https://doi.org/10.1109/ICRA.2017
.7989293

*Xiao, Z., Li, H., Zhou, D., Dai, Y., & Dai, B. (2017). Accurate extrinsic calibration between monocular camera and sparse 3D Lidar points without markers. In 2017 IEEE Intelligent Vehicles Symposium (IV) (pp. 424–429). Los Angeles, CA, USA: IEEE. http://doi.org/10.1109/IVS.2017
.7995755

*Yang, J., Ren, P., Zhang, D., Chen, D., Wen, F., Li, H., & Hua, G. (2017). Neural Aggregation Network for Video Face Recognition. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 5216–5225). Honolulu, HI, USA: IEEE. http://doi.org/10.1109/CVPR.2017.554

*Zhou, Y., Kneip, L., & Li, H. (2017). Semi-dense visual odometry for RGB-D cameras using approximate nearest neighbour fields. In 2017 IEEE International Conference on Robotics and Automation (ICRA) (pp. 6261–6268). Singapore: IEEE. http://doi.org/10.1109/ICRA.2017
.7989742

*Zhuang, B., Liu, L., Shen, C., & Reid, I. (2017). Towards Context-Aware Interaction Recognition for Visual Relationship Detection. In The IEEE International Conference on Computer Vision (ICCV) (pp. 589–598). Venice, Italy. Retrieved from http://openaccess.thecvf.com/content_
iccv_2017/html/Zhuang_Towards_Context-Aware_Interaction_ICCV_2017_paper.html

*Zuo, Y., & Drummond, T. (2017). Fast Residual Forests: Rapid Ensemble Learning for Semantic Segmentation. In Proceedings of the 1st Annual Conference on Robot Learning, in PMLR 78 (pp. 27–36). Retrieved from http://proceedings.mlr.press/v78/
zuo17a.html

*Abbasnejad, M. E., Shi, Q., Abbasnejad, I., Hengel, A. van den, & Dick, A. (2017). Bayesian Conditional Generative Adverserial Networks. Retrieved from http://arxiv.org/abs/1706.05477

*Anderson, P., He, X., Buehler, C., Teney, D., Johnson, M., Gould, S., & Zhang, L. (2017). Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering. Retrieved from http://arxiv.org/abs/1707.07998

*Anderson, P., Wu, Q., Teney, D., Bruce, J., Johnson, M., Sünderhauf, N., Reid, I., Gould, S., and Hengel, A. van den. (2017). Vision-and-Language Navigation: Interpreting visually-grounded navigation instructions in real environments. Retrieved from http://arxiv.org/abs/1711.07280

*Bruce, J., Sünderhauf, N., Mirowski, P., Hadsell, R., & Milford, M. (2017). One-Shot Reinforcement Learning for Robot Navigation with Interactive Replay. Retrieved from http://arxiv.org/abs/1711.10137

Chen, Y., Shen, C., Wei, X.-S., Liu, L., & Yang, J. (2017). Adversarial Learning of Structure-Aware Fully Convolutional Networks for Landmark Localization. Retrieved from http://arxiv.org/abs/1711.00253

Chen, Y., Tai, Y., Liu, X., Shen, C., & Yang, J. (2017). FSRNet: End-to-End Learning Face Super-Resolution with Facial Priors. Retrieved from http://arxiv.org/abs/1711.10703

*Chen, Z., Jacobson, A., Sünderhauf, N., Upcroft, B., Liu, L., Shen, C., Reid, I., Milford, M. (2017). Deep Learning Features at Scale for Visual Place Recognition. Retrieved from http://arxiv.org/abs/1701.05105

*Cherian, A., & Gould, S. (2017). Second-order Temporal Pooling for Action Recognition. Retrieved from http://arxiv.org/abs/1704.06925

*Cherian, A., Sra, S., & Hartley, R. (2017). Sequence Summarization Using Order-constrained Kernelized Feature Subspaces. Retrieved from https://arxiv.org/pdf/1705.08583.pdf

Cherian, A., Stanitsas, P., Harandi, M., Morellas, V., & Papanikolopoulos, N. (2017). Learning Discriminative αβ-divergence for Positive Definite Matrices (Extended Version) *. Retrieved from https://arxiv.org/pdf/1708.01741.pdf

*Cruz, R. S., Fernando, B., Cherian, A., & Gould, S. (2017). DeepPermNet: Visual Permutation Learning. Retrieved from https://arxiv.org/abs/1704.02729

*Dayoub, F., Sünderhauf, N., & Corke, P. (2017). Episode-Based Active Learning with Bayesian Neural Networks. Retrieved from http://arxiv.org/abs/1703.07473

*Deng, R., Zhao, T., Shen, C., & Liu, S. (2017). Relative Depth Order Estimation Using Multi-scale Densely Connected Convolutional Networks. Retrieved from http://arxiv.org/abs/1707.08063

*Do, T.-T., Nguyen, A., Reid, I., Caldwell, D. G., & Tsagarakis, N. G. (2017). AffordanceNet: An End-to-End Deep Learning Approach for Object Affordance Detection. Retrieved from http://arxiv.org/abs/1709.07326

Do, T.-T., Tan, D.-K. Le, Hoang, T., & Cheung, N.-M. (2017). Compact Hash Code Learning with Binary Deep Neural Network. Retrieved from http://arxiv.org/abs/1712.02956

Do, T.-T., Tan, D.-K. Le, Pham, T. T., & Cheung, N.-M. (2017). Simultaneous Feature Aggregating and Hashing for Large-scale Image Search. Retrieved from http://arxiv.org/abs/1704.00860

*Eriksson, A., Olsson, C., Kahl, F., & Chin, T.-J. (2017). Rotation Averaging and Strong Duality. Retrieved from https://arxiv.org/abs/1705.01362

*Gale, W., Carneiro, G., Oakden-Rayner, L., Palmer, L. J., & Bradley, A. P. (2017). Detecting hip fractures with radiologist-level performance using deep neural networks. Retrieved from https://arxiv.org/

Guo, G., Wang, H., Shen, C., Yan, Y., & Liao, H.-Y. M. (2017). Automatic Image Cropping for Visual Aesthetic Enhancement Using Deep Neural Networks and Cascaded Regression. Retrieved from https://arxiv.org/abs/1712.09048

Hall, D., Dayoub, F., Kulk, J., & McCool, C. (2017). Towards Unsupervised Weed Scouting for Agricultural Robotics. Retrieved from http://arxiv.org/abs/1702.01247

*Han, T., Wang, J., Cherian, A., & Gould, S. (2017). Human Action Forecasting by Learning Task Grammars. Retrieved from https://arxiv.org/pdf/1709.06391.pdf

Jacobson, A., Scheirer, W., & Milford, M. (2017). Deja vu: Scalable Place Recognition Using Mutually Supportive Feature Frequencies. Retrieved from http://arxiv.org/abs/1707.06393

*Ji, P., Reid, I., Garg, R., Li, H., & Salzmann, M. (2017). Non-Linear Subspace Clustering with Learned Low-Rank Kernels. Retrieved from http://arxiv.org/abs/1707.04974

*Khosravian, A., Chin, T.-J., & Reid, I. (2017). A Branch-and-Bound Algorithm for Checkerboard Extraction in Camera-Laser Calibration. Retrieved from http://arxiv.org/abs/1704.00887

*Latif, Y., Garg, R., Milford, M., & Reid, I. (2017). Addressing Challenging Place Recognition Tasks using Generative Adversarial Networks. Retrieved from http://arxiv.org/abs/1709.08810

*Leal-Taixé, L., Milan, A., Schindler, K., Cremers, D., Reid, I., & Roth, S. (2017). Tracking the Trackers: An Analysis of the State of the Art in Multiple Object Tracking. Retrieved from http://arxiv.org/abs/1704.02781

*Li, H., Wang, P., & Shen, C. (2017). Towards End-to-End Car License Plates Detection and Recognition with Deep Neural Networks. Retrieved from http://arxiv.org/abs/1709.08828

Liu, W., Chen, X., Shen, C., Yu, J., Wu, Q., & Yang, J. (2017). Robust Guided Image Filtering. Retrieved from https://arxiv.org/pdf/1703.09379.pdf

Ma, C., Huang, J.-B., Yang, X., & Yang, M.-H. (2017). Adaptive Correlation Filters with Long-Term and Short-Term Memory for Object Tracking. Retrieved from http://arxiv.org/abs/1707.02309

Ma, C., Huang, J.-B., Yang, X., & Yang, M.-H. (2017). Robust Visual Tracking via Hierarchical Convolutional Features. Retrieved from https://arxiv.org/pdf/1707.03816.pdf

*Ma, C., Shen, C., Dick, A., & Van Den Hengel, A. (2017). Visual Question Answering with Memory-Augmented Networks. Retrieved from https://arxiv.org/pdf/1707.04968.pdf

*McTaggart, M., Morrison, D., Tow, A. W., Smith, R., Kelly-Boxall, N., Milan, A., Pham, T., Zhuang, Z., Leitner, J., Reid, I., Corke, P., and Lehnert, C. (2017). Cartman: Cartesian Manipulator for Warehouse Automation in Cluttered Environments. Retrieved from http://arxiv.org/abs/1710.00967

*Meyer, B. J., Harwood, B., & Drummond, T. (2017). Nearest Neighbour Radial Basis Function Solvers for Deep Neural Networks. Retrieved from http://arxiv.org/abs/1705.09780

*Milan, A., Pham, T., Vijay, K., Morrison, D., Tow, A. W., Liu, L., Erskine, J., Grinover, R., Gurman, A., Hunn, T., Kelly-Boxall, K., Lee, D., McTaggart, M., Rallos, G., Razjigaev, A., Rowntree, T., Shen, T., Smith, R., Wade-McCue, S., Zhuang, Z., Lehnert, C., Lin, G., Reid, I., Corke, P., and Leitner, J. (2017). Semantic Segmentation from Limited Training Data. Retrieved from https://arxiv.org/abs/1709.07665

*Miller, D., Nicholson, L., Dayoub, F., & Sünderhauf, N. (2017). Dropout Sampling for Robust Object Detection in Open-Set Conditions. Retrieved from http://arxiv.org/abs/1710.06677

*Morrison, D., Tow, A. W., McTaggart, M., Smith, R., Kelly-Boxall, N., Wade-McCue, S., Erskine, J., Grinover, R., Gurman, A., Hunn, T., Lee, D., Milan, A., Pham, T., Rallos, G., Razjigaev, A., Rowntree, T., Kumar, V., Zhuang, Z., Lehnert, C., Reid, I., Corke, P., and Leitner, J. (2017). Cartman: The low-cost Cartesian Manipulator that won the Amazon Robotics Challenge. Retrieved from https://arxiv.org/abs/1709.06283

*Nguyen, A., Do, T.-T., Caldwell, D. G., & Tsagarakis, N. G. (2017). Real-Time Pose Estimation for Event Cameras with Stacked Spatial LSTM Networks. Retrieved from https://arxiv.org/pdf/1708.09011.pdf

Nguyen, H. Van, Chesser, M., Rezatofighi, S. H., & Ranasinghe, D. C. (2017). Real-Time Localization and Tracking of Multiple Radio-Tagged Animals with an Autonomous Aerial Vehicle System. Retrieved from https://arxiv.org/pdf/1712.01491.pdf

*Pan, L., Dai, Y., Liu, M., & Porikli, F. (2017). Depth Map Completion by Jointly Exploiting Blurry Color Images and Sparse Depth Maps. Retrieved from http://arxiv.org/abs/1711.09501

*Pham, T. T., Do, T.-T., Sünderhauf, N., & Reid, I. (2017). SceneCut: Joint Geometric and Object Segmentation for Indoor Scenes. Retrieved from https://arxiv.org/pdf/1709.07158.pdf

*Qiao, R., Liu, L., Shen, C., & Van Den Hengel, A. (2017). Visually Aligned Word Embeddings for Improving Zero-shot Learning. Retrieved from https://arxiv.org/pdf/1707.05427.pdf

*Rezatofighi, S. H., Milan, A., Shi, Q., Dick, A., & Reid, I. (2017). Joint Learning of Set Cardinality and State Distribution. Retrieved from https://arxiv.org/pdf/1709.04093.pdf

*Rezazadegan, F., Shirazi, S., & Davis, L. S. (2017). A Real-time Action Prediction Framework by Encoding Temporal Evolution. Retrieved from https://arxiv.org/abs/1709.07894

*Saha, S. K., Fernando, B., Cuadros, J., Xiao, D., & Kanagasingam, Y. (2017). Deep Learning for Automated Quality Assessment of Color Fundus Images in Diabetic Retinopathy Screening. Retrieved from http://arxiv.org/abs/1703.02511

*Shen, T., Lin, G., Liu, L., Shen, C., & Reid, I. (2017). Weakly Supervised Semantic Segmentation Based on Co-segmentation. Retrieved from http://arxiv.org/abs/1705.09052

*Shen, T., Lin, G., Shen, C., & Reid, I. (2017). Learning Multi-level Region Consistency with Dense Multi-label Networks for Semantic Segmentation. Retrieved from http://arxiv.org/abs/1701.07122

*Shigematsu, R., Feng, D., You, S., & Barnes, N. (2017). Learning RGB-D Salient Object Detection using background enclosure, depth contrast, and top-down features. Retrieved from https://arxiv.org/pdf/1705.03607.pdf

*Spek, A., Li, W.H., Drummond, T., (2017). A Fast Method for Computing Principal Curvatures from Range Images. Retrieved from arXiv preprint arXiv:1707.00385

*Sünderhauf, N., & Milford, M. (2017). Dual Quadrics from Object Detection Bounding Boxes as Landmark Representations in SLAM. Retrieved from http://arxiv.org/abs/1708.00965

Tan, D.-K. Le, Do, T.-T., & Cheung, N.-M. (2017). Supervised Hashing with End-to-End Binary Deep Neural Network. Retrieved from http://arxiv.org/abs/1711.08901

*Teney, D., Anderson, P., He, X., & Hengel, A. van den. (2017). Tips and Tricks for Visual Question Answering: Learnings from the 2017 Challenge. Retrieved from http://arxiv.org/abs/1708.02711

*Tow, A., Sünderhauf, N., Shirazi, S., Milford, M., & Leitner, J. (2017). What Would You Do? Acting by Learning to Predict. Retrieved from http://arxiv.org/abs/1703.02658

*Toyer, S., Cherian, A., Han, T., & Gould, S. (2017). Human Pose Forecasting via Deep Markov Models. Retrieved from https://arxiv.org/pdf/1707.09240.pdf

*Wade-McCue, S., Kelly-Boxall, N., McTaggart, M., Morrison, D., Tow, A. W., Erskine, J., Grinover, R., Gurman, A., Hunn, T., Lee, D., Milan, A., Pham, T., Rallos, G., Razjigaev, A., Rowntree, T., Smith, R., Kumar, Vijay., Zhuang, Z., Lehnert, C., Reid, I., Corke, P., and Leitner, J. (2017). Design of a Multi-Modal End-Effector and Grasping System: How Integrated Design helped win the Amazon Robotics Challenge. Retrieved from http://arxiv.org/abs/1710.01439

*Wang, J., Cherian, A., Porikli, F., & Gould, S. (2017). Action Representation Using Classifier Decision Boundaries. Retrieved from http://arxiv.org/abs/1704.01716

*Wang, X., Sekercioglu, Y.A., Drummond, T., Fremont, V., Natalizio, E., Fantoni, I. (2017). Relative Pose Based Redundancy Removal: Collaborative RGB-D Data Transmission in Mobile Visual Sensor Networks.  Retrieved from arXiv preprint arXiv:1707.05978

*Wang, X., You, M., & Shen, C. (2017). Adversarial Generation of Training Examples for Vehicle License Plate Recognition. Retrieved from http://arxiv.org/abs/1707.03124

Wei, X.-S., Zhang, C.-L., Li, Y., Xie, C.-W., Wu, J., Shen, C., & Zhou, Z.-H. (2017). Deep Descriptor Transforming for Image Co-Localization. Retrieved from http://arxiv.org/abs/1705.02758

Wei, X.-S., Zhang, C.-L., Wu, J., Shen, C., & Zhou, Z.-H. (2017). Unsupervised Object Discovery and Co-Localization by Deep Descriptor Transforming. Retrieved from https://arxiv.org/pdf/1707.06397.pdf

*Wu, Q., Wang, P., Shen, C., Reid, I., & Van Den Hengel, A. (2017). Are You Talking to Me? Reasoned Visual Dialog Generation through Adversarial Learning. Retrieved from https://arxiv.org/pdf/1711.07613.pdf

*Wu, Z., Shen, C., & Van Den Hengel, A. (2017). Real-time Semantic Image Segmentation via Spatial Sparsity. Retrieved from https://arxiv.org/pdf/1712.00213.pdf

Yu, L., Jacobson, A., & Milford, M. (2017). Rhythmic Representations: Learning Periodic Patterns for Scalable Place Recognition at a Sub-Linear Storage Cost. Retrieved from http://arxiv.org/abs/1712.07315

*Zhang, F., Leitner, J., Milford, M., & Corke, P. (2017). Sim-to-real Transfer of Visuo-motor Policies for Reaching in Clutter: Domain Randomization and Adaptation with Modular Networks. Retrieved from https://arxiv.org/abs/1709.05746

*Zhang, F., Leitner, J., Milford, M., & Corke, P. I. (2017). Tuning Modular Networks with Weighted Losses for Hand-Eye Coordination. Retrieved from http://arxiv.org/abs/1705.05116

*Zhang, J., Dai, Y., Porikli, F., He, M., Multi-Scale Salient Object Detection with Pyramid Spatial Pooling, APSIPA ASC 2017

*Zhang, J., Wu, Q., Shen, C., Zhang, J., Lu, J., & Van Den Hengel, A. (2017). Asking the Difficult Questions: Goal-Oriented Visual Question Generation via Intermediate Rewards. Retrieved from https://arxiv.org/pdf/1711.07614.pdf

Zhang, L., Wei, W., Shi, Q., Shen, C., Hengel, A. van den, & Zhang, Y. (2017). Beyond Low Rank: A Data-Adaptive Tensor Completion Method. Retrieved from http://arxiv.org/abs/1708.01008

*Zhong, Y., Dai, Y., Li, H.,, Self-Supervised Learning for Stereo Matching with Self-Improving Ability, September 2017, https://arxiv.org/pdf/1709.00930.pdf

*Zhuang, B., Wu, Q., Shen, C., Reid, I., & Hengel, A. van den. (2017). Care about you: towards large-scale human-centric visual relationship detection. Retrieved from http://arxiv.org/abs/1705.0989