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2018 Conference Papers [56]

Seeing Deeply and Bidirectionally: A Deep Learning Approach for Single Image Reflection Removal

Yang, J., Gong, D., Liu, L., & Shi, Q. (2018). Seeing Deeply and Bidirectionally: A Deep Learning Approach for Single Image Reflection Removal. Retrieved from http://openaccess.thecvf.com/content_ECCV_2018/papers/Jie_Yang_Seeing_Deeply_and_ECCV_2018_paper.pdf

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Tips and Tricks for Visual Question Answering: Learnings from the 2017 Challenge

Teney, D., Anderson, P., He, X., & Hengel, A. van den. (2018). Tips and Tricks for Visual Question Answering: Learnings from the 2017 Challenge. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 4223–4232). IEEE. http://doi.org/10.1109/CVPR.2018.00444

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Deblurring Natural Image Using Super-Gaussian Fields

Liu Y., Dong W., Gong D., Zhang L., Shi Q. (2018) Deblurring Natural Image Using Super-Gaussian Fields. In: Ferrari V., Hebert M., Sminchisescu C., Weiss Y. (eds) Computer Vision – ECCV 2018. ECCV 2018. Lecture Notes in Computer Science, vol 11205. Springer.

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Adversarial Training of Variational Auto-Encoders for High Fidelity Image Generation

Khan, S. H., Hayat, M., & Barnes, N. (2018). Adversarial Training of Variational Auto-Encoders for High Fidelity Image Generation. In 2018 IEEE Winter Conference on Applications of Computer Vision (WACV) (pp. 1312–1320). Lake Tahoe, United States: IEEE. https://doi.org/10.1109/WACV.2018.00148

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Semi-dense 3D Reconstruction with a Stereo Event Camera

*Zhou Y., Gallego G., Rebecq H., Kneip L., Li H., Scaramuzza D. (2018) Semi-dense 3D Reconstruction with a Stereo Event Camera. In: Ferrari V., Hebert M., Sminchisescu C., Weiss Y. (eds) Computer Vision – ECCV 2018. ECCV 2018. Lecture Notes in Computer Science, vol 11205. Springer, Cham

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3D Geometry-Aware Semantic Labeling of Outdoor Street Scenes

*Zhong, Y., Dai, Y., & Li, H. (2018). 3D Geometry-Aware Semantic Labeling of Outdoor Street Scenes. In 2018 24th International Conference on Pattern Recognition (ICPR) (pp. 2343–2349). IEEE. http://doi.org/10.1109/ICPR.2018.8545378

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Open-World Stereo Video Matching with Deep RNN

*Zhong Y., Li H., Dai Y. (2018) Open-World Stereo Video Matching with Deep RNN. In: Ferrari V., Hebert M., Sminchisescu C., Weiss Y. (eds) Computer Vision – ECCV 2018. ECCV 2018. Lecture Notes in Computer Science, vol 11206. Springer.

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Deep Unsupervised Saliency Detection: A Multiple Noisy Labeling Perspective

*Zhang, J., Zhang, T., Daf, Y., Harandi, M., & Hartley, R. (2018). Deep Unsupervised Saliency Detection: A Multiple Noisy Labeling Perspective. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 9029–9038). Salt Lake City, United States: IEEE. http://doi.org/10.1109/CVPR.2018.00941

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Robust Visual Odometry in Underwater Environment

*Zhang, J., Ila, V., & Kneip, L. (2018). Robust Visual Odometry in Underwater Environment. In 2018 OCEANS - MTS/IEEE Kobe Techno-Oceans (OTO) (pp. 1–9). Kobe, Japan: IEEE. http://doi.org/10.1109/OCEANSKOBE.2018.8559452

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Goal-Oriented Visual Question Generation via Intermediate Rewards

*Zhang J., Wu Q., Shen C., Zhang J., Lu J., van den Hengel A. (2018) Goal-Oriented Visual Question Generation via Intermediate Rewards. In: Ferrari V., Hebert M., Sminchisescu C., Weiss Y. (eds) Computer Vision – ECCV 2018. ECCV 2018. Lecture Notes in Computer Science, vol 11209. Springer, Cham

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Super-Resolving Very Low-Resolution Face Images with Supplementary Attributes

*Yu, X., Fernando, B., Hartley, R., & Porikli, F. (2018). Super-Resolving Very Low-Resolution Face Images with Supplementary Attributes. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 908–917). Salt Lake City, United States: IEEE. http://doi.org/10.1109/CVPR.2018.00101

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Face Super-Resolution Guided by Facial Component Heatmaps

*Yu X., Fernando B., Ghanem B., Porikli F., Hartley R. (2018) Face Super-Resolution Guided by Facial Component Heatmaps. In: Ferrari V., Hebert M., Sminchisescu C., Weiss Y. (eds) Computer Vision – ECCV 2018. ECCV 2018. Lecture Notes in Computer Science, vol 11213. Springer, Cham

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Learning Discriminative Video Representations Using Adversarial Perturbations

*Wang J., Cherian A. (2018) Learning Discriminative Video Representations Using Adversarial Perturbations. In: Ferrari V., Hebert M., Sminchisescu C., Weiss Y. (eds) Computer Vision – ECCV 2018. ECCV 2018. Lecture Notes in Computer Science, vol 11208. Springer.

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Structure from Recurrent Motion: From Rigidity to Recurrency

*Li, X., Li, H., Joo, H., Liu, Y., & Sheikh, Y. (2018). Structure from Recurrent Motion: From Rigidity to Recurrency. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 3032–3040). Salt Lake City, United States: IEEE. http://doi.org/10.1109/CVPR.2018.00320

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Kernel Support Vector Machines and Convolutional Neural Networks

Jiang, S., Hartley, R., & Fernando, B. (2018). Kernel Support Vector Machines and Convolutional Neural Networks. In 2018 Digital Image Computing: Techniques and Applications (DICTA) (pp. 1–7). Canberra, Australia: IEEE. http://doi.org/10.1109/DICTA.2018.8615840

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Semi-Supervised SLAM: Leveraging Low-Cost Sensors on Underground Autonomous Vehicles for Position Tracking

Jacobson, A., Zeng, F., Smith, D., Boswell, N., Peynot, T., & Milford, M. (2018). Semi-Supervised SLAM: Leveraging Low-Cost Sensors on Underground Autonomous Vehicles for Position Tracking. In 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 3970–3977). Madrid, Spain: IEEE. http://doi.org/10.1109/IROS.2018.8593750

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Parallel Attention: A Unified Framework for Visual Object Discovery Through Dialogs and Queries

Zhuang, B., Wu, Q., Shen, C., Reid, I., & Hengel, A. van den. (2018). Parallel Attention: A Unified Framework for Visual Object Discovery Through Dialogs and Queries. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 4252–4261). Salt Lake City, United States: IEEE. http://doi.org/10.1109/CVPR.2018.00447

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Towards Effective Low-Bitwidth Convolutional Neural Networks

Zhuang, B., Shen, C., Tan, M., Liu, L., & Reid, I. (2018). Towards Effective Low-Bitwidth Convolutional Neural Networks. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 7920–7928). Salt Lake City, United States: IEEE. http://doi.org/10.1109/CVPR.2018.00826

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Are You Talking to Me? Reasoned Visual Dialog Generation Through Adversarial Learning

Wu, Q., Wang, P., Shen, C., Reid, I., & Hengel, A. van den. (2018). Are You Talking to Me? Reasoned Visual Dialog Generation Through Adversarial Learning. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 6106–6115). Salt Lake City, United States: IEEE. http://doi.org/10.1109/CVPR.2018.00639

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Bayesian Semantic Instance Segmentation in Open Set World

Pham T., Vijay Kumar B.G., Do TT., Carneiro G., Reid I. (2018) Bayesian Semantic Instance Segmentation in Open Set World. In: Ferrari V., Hebert M., Sminchisescu C., Weiss Y. (eds) Computer Vision – ECCV 2018. ECCV 2018. Lecture Notes in Computer Science, vol 11214. Springer.

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Training Medical Image Analysis Systems like Radiologists

Maicas G., Bradley A.P., Nascimento J.C., Reid I., Carneiro G. (2018) Training Medical Image Analysis Systems like Radiologists. In: Frangi A., Schnabel J., Davatzikos C., Alberola-López C., Fichtinger G. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2018. MICCAI 2018. Lecture Notes in Computer Science, vol 11070. Springer.

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Visual Question Answering with Memory-Augmented Networks

Ma, C., Shen, C., Dick, A., Wu, Q., Wang, P., Hengel, A. van den, & Reid, I. (2018). Visual Question Answering with Memory-Augmented Networks. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 6975–6984). Salt Lake City, United States: IEEE. http://doi.org/10.1109/CVPR.2018.00729

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Deep Regression Tracking with Shrinkage Loss

Lu X., Ma C., Ni B., Yang X., Reid I., Yang MH. (2018) Deep Regression Tracking with Shrinkage Loss. In: Ferrari V., Hebert M., Sminchisescu C., Weiss Y. (eds) Computer Vision – ECCV 2018. ECCV 2018. Lecture Notes in Computer Science, vol 11218. Springer.

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Efficient Dense Point Cloud Object Reconstruction Using Deformation Vector Fields

Li K., Pham T., Zhan H., Reid I. (2018) Efficient Dense Point Cloud Object Reconstruction Using Deformation Vector Fields. In: Ferrari V., Hebert M., Sminchisescu C., Weiss Y. (eds) Computer Vision – ECCV 2018. ECCV 2018. Lecture Notes in Computer Science, vol 11216. Springer, Cham

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Multi-modal Cycle-Consistent Generalized Zero-Shot Learning

Felix R., Vijay Kumar B.G., Reid I., Carneiro G. (2018) Multi-modal Cycle-Consistent Generalized Zero-Shot Learning. In: Ferrari V., Hebert M., Sminchisescu C., Weiss Y. (eds) Computer Vision – ECCV 2018. ECCV 2018. Lecture Notes in Computer Science, vol 11210. Springer.

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AffordanceNet: An End-to-End Deep Learning Approach for Object Affordance Detection

Do, T.-T., Nguyen, A., & Reid, I. (2018). AffordanceNet: An End-to-End Deep Learning Approach for Object Affordance Detection. In 2018 IEEE International Conference on Robotics and Automation (ICRA) (pp. 1–5). Brisbane, Australia: IEEE. http://doi.org/10.1109/ICRA.2018.8460902

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Visual Grounding via Accumulated Attention

Deng, C., Wu, Q., Wu, Q., Hu, F., Lyu, F., & Tan, M. (2018). Visual Grounding via Accumulated Attention. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 7746–7755). Salt Lake City, United States: IEEE. http://doi.org/10.1109/CVPR.2018.00808

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Vision Based Forward Sensitive Reactive Control for a Quadrotor VTOL

Stevens, J.-L., & Mahony, R. (2018). Vision Based Forward Sensitive Reactive Control for a Quadrotor VTOL. In 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 5232–5238). Madrid, Spain: IEEE. http://doi.org/10.1109/IROS.2018.8593606

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Calibrating Light-Field Cameras Using Plenoptic Disc Features

O’brien, S., Trumpf, J., Ila, V., & Mahony, R. (2018). Calibrating Light-Field Cameras Using Plenoptic Disc Features. In 2018 International Conference on 3D Vision (3DV) (pp. 286–294). Verona, Italy: IEEE. http://doi.org/10.1109/3DV.2018.00041

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A Geometric Observer for Scene Reconstruction Using Plenoptic Cameras

O’Brien, S. G. P., Trumpf, J., Ila, V., & Mahony, R. (2018). A Geometric Observer for Scene Reconstruction Using Plenoptic Cameras. In 2018 IEEE Conference on Decision and Control (CDC) (pp. 557–564). Florida, United States: IEEE. http://doi.org/10.1109/CDC.2018.8618954

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Homography estimation of a moving planar scene from direct point correspondence

De Marco, S., Hua, M.-D., Mahony, R., & Hamel, T. (2018). Homography estimation of a moving planar scene from direct point correspondence. In 2018 IEEE Conference on Decision and Control (CDC) (pp. 565–570). Florida, United States: IEEE. http://doi.org/10.1109/CDC.2018.8619386

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Video Representation Learning Using Discriminative Pooling

Wang, J., Cherian, A., Porikli, F., & Gould, S. (2018). Video Representation Learning Using Discriminative Pooling. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 1149–1158). Salt Lake City, United States: IEEE. http://doi.org/10.1109/CVPR.2018.00126

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Non-linear Temporal Subspace Representations for Activity Recognition

Cherian, A., Sra, S., Gould, S., & Hartley, R. (2018). Non-linear Temporal Subspace Representations for Activity Recognition. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 2197–2206). Salt Lake City, United States: IEEE. http://doi.org/10.1109/CVPR.2018.00234

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One-class Gaussian process regressor for quality assessment of transperineal ultrasound images

Camps, S. M., Houben, T., Fontanarosa, D., Edwards, C., Antico, M., Dunnhofer, M., Martens, E.G.H.J, Baeza, J.A., Vanneste, B.G.L., van Limbergen, E.J., de W., Peter, H.N., Verhaegen, F., & Carneiro, G. (2018). One-class Gaussian process regressor for quality assessment of transperineal ultrasound images. In International Conference on Medical Imaging with Deep Learning (MIDL). Amsterdam. Retrieved from https://eprints.qut.edu.au/120113/

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Vision-and-Language Navigation: Interpreting Visually-Grounded Navigation Instructions in Real Environments

Anderson, P., Wu, Q., Teney, D., Bruce, J., Johnson, M., Sunderhauf, N., Reid, I., Gould, S., & van den Hengel, A. (2018). Vision-and-Language Navigation: Interpreting Visually-Grounded Navigation Instructions in Real Environments. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 3674–3683). IEEE. http://doi.org/10.1109/CVPR.2018.00387

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Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering

Anderson, P., He, X., Buehler, C., Teney, D., Johnson, M., Gould, S., & Zhang, L. (2018). Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 6077–6086). Salt Lake City, United States: IEEE. http://doi.org/10.1109/CVPR.2018.00636

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Practical Motion Segmentation for Urban Street View Scenes

Rubino, C., Del Bue, A., & Chin, T.-J. (2018). Practical Motion Segmentation for Urban Street View Scenes. In 2018 IEEE International Conference on Robotics and Automation (ICRA) (pp. 1879–1886). Brisbane, Australia: IEEE. http://doi.org/10.1109/ICRA.2018.8460993

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VITAL: VIsual Tracking via Adversarial Learning

Song, Y., Ma, C., Wu, X., Gong, L., Bao, L., Zuo, W., Shen, C., Lau, Rynson W.H., & Yang, M.-H. (2018). VITAL: VIsual Tracking via Adversarial Learning. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 8990–8999). Salt Lake City, United States: IEEE. http://doi.org/10.1109/CVPR.2018.00937

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A Fast Resection-Intersection Method for the Known Rotation Problem

Zhang, Q., Chin, T.-J., & Le, H. M. (2018). A Fast Resection-Intersection Method for the Known Rotation Problem. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 3012–3021). Salt Lake City, United States: IEEE. http://doi.org/10.1109/CVPR.2018.00318

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Rotation Averaging and Strong Duality

Eriksson, A., Olsson, C., Kahl, F., & Chin, T.-J. (2018). Rotation Averaging and Strong Duality. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 127–135). Salt Lake City, United States: IEEE. http://doi.org/10.1109/CVPR.2018.00021

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ArthroSLAM: Multi-Sensor Robust Visual Localization for Minimally Invasive Orthopedic Surgery

Marmol, A., Corke, P., & Peynot, T. (2018). ArthroSLAM: Multi-Sensor Robust Visual Localization for Minimally Invasive Orthopedic Surgery. In 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 3882–3889). Madrid, Spain: IEEE. https://doi.org/10.1109/IROS.2018.8593501

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Collaborative Planning for Mixed-Autonomy Lane Merging

Bansal, S., Cosgun, A., Nakhaei, A., & Fujimura, K. (2018). Collaborative Planning for Mixed-Autonomy Lane Merging. In 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 4449–4455). Madrid, Spain: IEEE. http://doi.org/10.1109/IROS.2018.8594197

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CReaM: Condensed Real-time Models for Depth Prediction using Convolutional Neural Networks

Spek, A., Dharmasiri, T., & Drummond, T. (2018). CReaM: Condensed Real-time Models for Depth Prediction using Convolutional Neural Networks. In 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 540–547). Madrid, Spain: IEEE. http://doi.org/10.1109/IROS.2018.8594243

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Deep Metric Learning and Image Classification with Nearest Neighbour Gaussian Kernels

Meyer, B. J., Harwood, B., & Drummond, T. (2018). Deep Metric Learning and Image Classification with Nearest Neighbour Gaussian Kernels. In IEEE International Conference on Image Processing (ICIP) (pp. 151–155). Athens, Greece: IEEE. http://doi.org/10.1109/ICIP.2018.8451297

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A dynamic planner for object assembly tasks based on learning the spatial relationships of its parts from a single demonstration

Abbas, A., Maire, F., Shirazi, S., Dayoub, F., & Eich, M. (2018). A dynamic planner for object assembly tasks based on learning the spatial relationships of its parts from a single demonstration. In Science & Engineering Faculty. Wellington, New Zealand: Springer. Retrieved from https://eprints.qut.edu.au/121640/

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SceneCut: Joint Geometric and Object Segmentation for Indoor Scenes

Pham, T. T., Do, T.-T., Sunderhauf, N., & Reid, I. (2018). SceneCut: Joint Geometric and Object Segmentation for Indoor Scenes. In 2018 IEEE International Conference on Robotics and Automation (ICRA) (pp. 1–9). Brisbane: IEEE. http://doi.org/10.1109/ICRA.2018.8461108

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Bootstrapping the Performance of Webly Supervised Semantic Segmentation

Shen, T., Lin, G., Shen, C., & Reid, I. (2018). Bootstrapping the Performance of Webly Supervised Semantic Segmentation. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Utah, United States. Retrieved from http://openaccess.thecvf.com/content_cvpr_2018/CameraReady/1401.pdf

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Scalable Dense Non-rigid Structure-from-Motion: A Grassmannian Perspective

Kumar, S., Cherian, A., Dai, Y., & Li, H. (2018). Scalable Dense Non-rigid Structure-from-Motion: A Grassmannian Perspective. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 254–263). Salt Lake City, United States: IEEE. http://doi.org/10.1109/CVPR.2018.00034

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Just-In-Time Reconstruction: Inpainting Sparse Maps using Single View Depth Predictors as Priors

Weerasekera, C., Dharmasiri, T., Garg, R., Drummond, T., & Reid, I. (2017). Just-In-Time Reconstruction: Inpainting Sparse Maps using Single View Depth Predictors as Priors.

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SceneCut: Joint Geometric and Object Segmentation for Indoor Scenes

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

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Elastic LiDAR Fusion: Dense Map-Centric Continuous-Time SLAM

Park, C., Moghadam, P., Kim, S., Elfes, A., Fookes, C., & Sridharan, S. (2017). Elastic LiDAR Fusion: Dense Map-Centric Continuous-Time SLAM. Retrieved from http://arxiv.org/abs/1711.01691

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Cartman: The low-cost Cartesian Manipulator that won the Amazon Robotics Challenge

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

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Dropout Sampling for Robust Object Detection in Open-Set Conditions

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

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Semantic Segmentation from Limited Training Data

Milan, A., Pham, T., Vijay, K., Morrison, D., Tow, A. W., Liu, L., Erskine, J., Grinover, R., Gurman, A., Hunn, T., Kelly-Boxall, N., 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., & Leitner, J. (2017). Semantic Segmentation from Limited Training Data. Retrieved from http://arxiv.org/abs/1709.07665

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Efficacy of Mechanical Weeding Tools: a study into alternative weed management strategies enabled by robotics

McCool, C. S., Beattie, J., Firn, J., Lehnert, C., Kulk, J., Bawden, O., Russell, R., & Perez, T. (2018). Efficacy of Mechanical Weeding Tools: a study into alternative weed management strategies enabled by robotics. IEEE Robotics and Automation Letters, 1–1. http://doi.org/10.1109/LRA.2018.2794619

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Addressing Challenging Place Recognition Tasks using Generative Adversarial Networks

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

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