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2018 All Categories [192]

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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Agile Amulet: Real-Time Salient Object Detection with Contextual Attention

Zhang, P., Wang, L., Wang, D., Lu, H., & Shen, C. (2018). Agile Amulet: Real-Time Salient Object Detection with Contextual Attention. Retrieved from http://arxiv.org/abs/1802.06960

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HyperFusion-Net: Densely Reflective Fusion for Salient Object Detection

Zhang, P., Lu, H., & Shen, C. (2018). HyperFusion-Net: Densely Reflective Fusion for Salient Object Detection. Retrieved from https://arxiv.org/abs/1804.05142

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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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Salient Object Detection by Lossless Feature Reflection

Zhang, P., Liu, W., Lu, H., & Shen, C. (2018). Salient Object Detection by Lossless Feature Reflection. Retrieved from https://arxiv.org/pdf/1802.06527

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Adaptive Importance Learning for Improving Lightweight Image Super-resolution Network

Zhang, L., Wang, P., Shen, C., Liu, L., Wei, W., Zhang, Y., & Hengel, A. van den. (2018). Adaptive Importance Learning for Improving Lightweight Image Super-resolution Network. Retrieved from http://arxiv.org/abs/1806.01576

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Not All Negatives Are Equal: Learning to Track With Multiple Background Clusters

Zhu, G., Porikli, F., & Li, H. (2018). Not All Negatives Are Equal: Learning to Track With Multiple Background Clusters. IEEE Transactions on Circuits and Systems for Video Technology, 28(2), 314–326. http://doi.org/10.1109/TCSVT.2016.2615518

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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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End-to-End Diagnosis and Segmentation Learning from Cardiac Magnetic Resonance Imaging

Snaauw, G., Gong, D., Maicas, G., Hengel, A. van den, Niessen, W. J., Verjans, J., & Carneiro, G. (2018). End-to-End Diagnosis and Segmentation Learning from Cardiac Magnetic Resonance Imaging. Retrieved from https://arxiv.org/abs/1810.10117

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Online UAV Path Planning for Joint Detection and Tracking of Multiple Radio-tagged Objects

Nguyen, H. Van, Rezatofighi, S. H., Vo, B.-N., & Ranasinghe, D. C. (2018). Online UAV Path Planning for Joint Detection and Tracking of Multiple Radio-tagged Objects. Retrieved from https://arxiv.org/pdf/1808.04445

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Edge-Preserving Piecewise Linear Image Smoothing Using Piecewise Constant Filters

Liu, W., Xu, W., Chen, X., Huang, X., Shen, C., & Yang, J. (2018). Edge-Preserving Piecewise Linear Image Smoothing Using Piecewise Constant Filters. Retrieved from http://arxiv.org/abs/1801.06928

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Deep attention-based classification network for robust depth prediction

Li, R., Xian, K., Shen, C., Cao, Z., Lu, H., & Hang, L. (2018). Deep attention-based classification network for robust depth prediction. Retrieved from https://arxiv.org/abs/1807.03959

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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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Fast and Low Memory Cost Matrix Factorization: Algorithm, Analysis and Case Study

Yan, Y., Tan, M., Tsang, I., Yang, Y., Shi, Q., & Zhang, C. (2018). Fast and Low Memory Cost Matrix Factorization: Algorithm, Analysis and Case Study. IEEE Transactions on Knowledge and Data Engineering, 1–1. https://doi.org/10.1109/TKDE.2018.2882197

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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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Mask-aware networks for crowd counting

Jiang, S., Lu, X., Lei, Y., & Liu, L. (2018). Mask-aware networks for crowd counting. Retrieved from http://arxiv.org/abs/1901.00039

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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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Deep Auto-Set: A Deep Auto-Encoder-Set Network for Activity Recognition Using Wearables

Varamin, A. A., Abbasnejad, E., Shi, Q., Ranasinghe, D. C., & Rezatofighi, H. (2018). Deep Auto-Set: A Deep Auto-Encoder-Set Network for Activity Recognition Using Wearables (Vol. 18). Retrieved from https://doi.org/10.475/123_4

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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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Producing radiologist-quality reports for interpretable artificial intelligence

Gale, W., Oakden-Rayner, L., Carneiro, G., Bradley, A. P., & Palmer, L. J. (2018). Producing radiologist-quality reports for interpretable artificial intelligence. Retrieved from http://arxiv.org/abs/1806.00340

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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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Automated Quality Assessment of Colour Fundus Images for Diabetic Retinopathy Screening in Telemedicine

Saha, S. K., Fernando, B., Cuadros, J., Xiao, D., & Kanagasingam, Y. (2018). Automated Quality Assessment of Colour Fundus Images for Diabetic Retinopathy Screening in Telemedicine. Journal of Digital Imaging, 31(6), 869–878. http://doi.org/10.1007/s10278-018-0084-9

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ApolloCar3D: A Large 3D Car Instance Understanding Benchmark for Autonomous Driving

*Song, X., Wang, P., Zhou, D., Zhu, R., Guan, C., Dai, Y., Su, H., Li, H., & Yang, R. (2018). ApolloCar3D: A Large 3D Car Instance Understanding Benchmark for Autonomous Driving. Retrieved from http://arxiv.org/abs/1811.12222

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Phase-only Image Based Kernel Estimation for Single-image Blind Deblurring

*Pan, L., Hartley, R., Liu, M., & Dai, Y. (2018). Phase-only Image Based Kernel Estimation for Single-image Blind Deblurring. Retrieved from https://arxiv.org/pdf/1811.10185

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Embedding Bilateral Filter in Least Squares for Efficient Edge-preserving Image Smoothing

Liu, W., Zhang, P., Chen, X., Shen, C., Huang, X., & Yang, J. (2018). Embedding Bilateral Filter in Least Squares for Efficient Edge-preserving Image Smoothing. IEEE Transactions on Circuits and Systems for Video Technology, 1–1. http://doi.org/10.1109/TCSVT.2018.2890202

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Robust and Efficient Relative Pose With a Multi-Camera System for Autonomous Driving in Highly Dynamic Environments

Liu, L., Li, H., Dai, Y., & Pan, Q. (2018). Robust and Efficient Relative Pose With a Multi-Camera System for Autonomous Driving in Highly Dynamic Environments. IEEE Transactions on Intelligent Transportation Systems, 19(8), 2432–2444. http://doi.org/10.1109/TITS.2017.2749409

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Reading car license plates using deep neural networks

Li, H., Wang, P., You, M., & Shen, C. (2018). Reading car license plates using deep neural networks. Image and Vision Computing, 72, 14–23. http://doi.org/10.1016/J.IMAVIS.2018.02.002

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Block Mean Approximation for Efficient Second Order Optimization

*Lu, Y., Harandi, M., Hartley, R., & Pascanu, R. (2018). Block Mean Approximation for Efficient Second Order Optimization. Retrieved from http://arxiv.org/abs/1804.05484

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Deep Stochastic Attraction and Repulsion Embedding for Image Based Localization

*Liu, L., Li, H., & Dai, Y. (2018). Deep Stochastic Attraction and Repulsion Embedding for Image Based Localization. Retrieved from https://arxiv.org/pdf/1808.08779

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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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Light-Weight RefineNet for Real-Time Semantic Segmentation

Nekrasov, V., Shen, C., & Reid, I. (2018). Light-Weight RefineNet for Real-Time Semantic Segmentation. Retrieved from http://arxiv.org/abs/1810.03272

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Pre and Post-hoc Diagnosis and Interpretation of Malignancy from Breast DCE-MRI

Maicas, G., Bradley, A. P., Nascimento, J. C., Reid, I., & Carneiro, G. (2018). Pre and Post-hoc Diagnosis and Interpretation of Malignancy from Breast DCE-MRI. Retrieved from https://arxiv.org/pdf/1809.09404.pdf

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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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Exploring Context with Deep Structured Models for Semantic Segmentation

Lin, G., Shen, C., van den Hengel, A., & Reid, I. (2018). Exploring Context with Deep Structured Models for Semantic Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(6), 1352–1366. http://doi.org/10.1109/TPAMI.2017.2708714

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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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Drones count wildlife more accurately and precisely than humans

Hodgson, J. C., Mott, R., Baylis, S. M., Pham, T. T., Wotherspoon, S., Kilpatrick, A. D., Ramesh, R.S., Reid, I., Terauds, A., & Koh, L. P. (2018). Drones count wildlife more accurately and precisely than humans. Methods in Ecology and Evolution, 9(5), 1160–1167. http://doi.org/10.1111/2041-210X.12974

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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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Semisupervised and Weakly Supervised Road Detection Based on Generative Adversarial Networks.

Han, X., Lu, J., Zhao, C., You, S., & Li, H. (2018). Semisupervised and Weakly Supervised Road Detection Based on Generative Adversarial Networks. IEEE Signal Processing Letters, 25(4), 551–555. http://doi.org/10.1109/LSP.2018.2809685

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Automatic Image Cropping for Visual Aesthetic Enhancement Using Deep Neural Networks and Cascaded Regression

Guo, G., Wang, H., Shen, C., Yan, Y., & Liao, H.-Y. M. (2018). Automatic Image Cropping for Visual Aesthetic Enhancement Using Deep Neural Networks and Cascaded Regression. IEEE Transactions on Multimedia, 20(8), 2073–2085. http://doi.org/10.1109/TMM.2018.2794262

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Learning an Optimizer for Image Deconvolution

Gong, D., Zhang, Z., Shi, Q., Hengel, A. van den, Shen, C., & Zhang, Y. (2018). Learning an Optimizer for Image Deconvolution. Retrieved from https://arxiv.org/abs/1804.03368

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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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Learning to Predict Crisp Boundaries

Deng R., Shen C., Liu S., Wang H., Liu X. (2018) Learning to Predict Crisp Boundaries. 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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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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Simultaneous Localization and Mapping with Dynamic Rigid Objects

Henein, M., Kennedy, G., Ila, V., & Mahony, R. (2018). Simultaneous Localization and Mapping with Dynamic Rigid Objects. Retrieved from http://arxiv.org/abs/1805.03800

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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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Visual Permutation Learning

Santa Cruz, R., Fernando, B., Cherian, A., & Gould, S. (2018). Visual Permutation Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence (Vol. PP). IEEE. https://doi.org/10.1109/TPAMI.2018.2873701 *Early Access

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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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Globally-Optimal Inlier Set Maximisation for Camera Pose and Correspondence Estimation

Campbell, D. J., Petersson, L., Kneip, L., & Li, H. (2018). Globally-Optimal Inlier Set Maximisation for Camera Pose and Correspondence Estimation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1–1. http://doi.org/10.1109/TPAMI.2018.2848650 *In Press

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Action Recognition with Dynamic Image Networks

Bilen, H., Fernando, B., Gavves, E., & Vedaldi, A. (2018). Action Recognition with Dynamic Image Networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(12), 2799–2813. http://doi.org/10.1109/TPAMI.2017.2769085

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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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Partially-Supervised Image Captioning

Anderson, P., Gould, S., & Johnson, M. (2018). Partially-Supervised Image Captioning. Retrieved from http://arxiv.org/abs/1806.06004

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Searching for Representative Modes on Hypergraphs for Robust Geometric Model Fitting

Wang, H., guobao, xiao, Yan, Y., & Suter, D. (2018). Searching for Representative Modes on Hypergraphs for Robust Geometric Model Fitting. IEEE Transactions on Pattern Analysis and Machine Intelligence. https://doi.org/10.1109/TPAMI.2018.2803173 *In Press

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Semantics-Aware Visual Object Tracking

Yao, R., Lin, G., Shen, C., Zhang, Y., & Shi, Q. (2018). Semantics-Aware Visual Object Tracking. IEEE Transactions on Circuits and Systems for Video Technology, 1–1. https://doi.org/10.1109/TCSVT.2018.2848358 *In Press

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Learning Context Flexible Attention Model for Long-Term Visual Place Recognition

Chen, Z., Liu, L., Sa, I., Ge, Z., & Chli, M. (2018). Learning Context Flexible Attention Model for Long-Term Visual Place Recognition. IEEE Robotics and Automation Letters, 3(4), 4015–4022. http://doi.org/10.1109/LRA.2018.2859916

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Unsupervised Domain Adaptation Using Robust Class-Wise Matching

Zhang, L., Wang, P., Wei, W., Lu, H., Shen, C., van den Hengel, A., & Zhang, Y. (2018). Unsupervised Domain Adaptation Using Robust Class-Wise Matching. IEEE Transactions on Circuits and Systems for Video Technology. https://doi.org/10.1109/TCSVT.2018.2842206 *In Press

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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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Towards Effective Deep Embedding for Zero-Shot Learning

Zhang, L., Wang, P., Liu, L., Shen, C., Wei, W., Zhang, Y., & Van Den Hengel, A. (2018). Towards Effective Deep Embedding for Zero-Shot Learning. Retrieved from https://arxiv.org/pdf/1808.10075

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RGB-D Based Action Recognition with Light-weight 3D Convolutional Networks

Zhang, H., Li, Y., Wang, P., Liu, Y., & Shen, C. (2018). RGB-D Based Action Recognition with Light-weight 3D Convolutional Networks. Retrieved from https://arxiv.org/pdf/1811.09908

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Neighbourhood Watch: Referring Expression Comprehension via Language-guided Graph Attention Networks

Wang, P., Wu, Q., Cao, J., Shen, C., Gao, L., & Van Den Hengel, A. (2018). Neighbourhood Watch: Referring Expression Comprehension via Language-guided Graph Attention Networks. Retrieved from https://arxiv.org/pdf/1812.04794.pdf

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Object Captioning and Retrieval with Natural Language

Nguyen, A., Do, T.-T., Reid, I., Caldwell, D. G., & Tsagarakis, N. G. (2018). Object Captioning and Retrieval with Natural Language. Retrieved from https://arxiv.org/abs/1803.06152

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Diagnostics in Semantic Segmentation

Nekrasov, V., Shen, C., & Reid, I. (2018). Diagnostics in Semantic Segmentation. Retrieved from https://arxiv.org/pdf/1809.10328

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Correlation Propagation Networks for Scene Text Detection

Liu, Z., Lin, G., Ling Goh, W., Liu, F., Shen, C., & Yang, X. (2018). Correlation Propagation Networks for Scene Text Detection. Retrieved from https://arxiv.org/pdf/1810.00304

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Optimizable Object Reconstruction from a Single View

Li, K., Garg, R., Cai, M., & Reid, I. (2018). Optimizable Object Reconstruction from a Single View. Retrieved from https://arxiv.org/pdf/1811.11921

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Visual Question Answering as Reading Comprehension

Li, H., Wang, P., Shen, C., & Van Den Hengel, A. (2018). Visual Question Answering as Reading Comprehension. Retrieved from https://arxiv.org/pdf/1811.11903

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Real-Time Monocular Object-Model Aware Sparse SLAM

Hosseinzadeh, M., Li, K., Latif, Y., & Reid, I. (2018). Real-Time Monocular Object-Model Aware Sparse SLAM. Retrieved from https://arxiv.org/pdf/1809.09149

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Simultaneous Compression and Quantization: A Joint Approach for Efficient Unsupervised Hashing

Hoang, T., Do, T.-T., Le-Tan, D.-K., & Cheung, N.-M. (2018). Simultaneous Compression and Quantization: A Joint Approach for Efficient Unsupervised Hashing. Retrieved from http://arxiv.org/abs/1802.06645

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G2D: from GTA to Data

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Practical Visual Localization for Autonomous Driving: Why Not Filter?

Doan, A.-D., Do, T.-T., Latif, Y., Chin, T.-J., & Reid, I. (2018). Practical Visual Localization for Autonomous Driving: Why Not Filter? Retrieved from https://arxiv.org/pdf/1811.08063

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Binary Constrained Deep Hashing Network for Image Retrieval without Human Annotation

Do, T.-T., Tan, D.-K. Le, Pham, T., Hoang, T., Le, H., Cheung, N.-M., & Reid, I. (2018). Binary Constrained Deep Hashing Network for Image Retrieval without Human Annotation. Retrieved from http://arxiv.org/abs/1802.07437

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From Selective Deep Convolutional Features to Compact Binary Representations for Image Retrieval

Do, T.-T., Hoang, T., Tan, D.-K. Le, & Cheung, N.-M. (2018). From Selective Deep Convolutional Features to Compact Binary Representations for Image Retrieval. Retrieved from http://arxiv.org/abs/1802.02899

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Structured Binary Neural Networks for Accurate Image Classification and Semantic Segmentation

Zhuang, B., Shen, C., Tan, M., Liu, L., & Reid, I. (2018). Structured Binary Neural Networks for Accurate Image Classification and Semantic Segmentation. Retrieved from https://arxiv.org/pdf/1811.10413

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Training Compact Neural Networks with Binary Weights and Low Precision Activations

Zhuang, B., Shen, C., & Reid, I. (2018). Training Compact Neural Networks with Binary Weights and Low Precision Activations. Retrieved from https://arxiv.org/pdf/1808.02631

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Discrimination-aware Channel Pruning for Deep Neural Networks

Zhuang, Z., Tan, M., Zhuang, B., Liu, J., Guo, Y., Wu, Q., Huang, J., & Zhu, J. (2018). Discrimination-aware Channel Pruning for Deep Neural Networks. Retrieved from https://arxiv.org/pdf/1810.11809

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Robust Fitting in Computer Vision: Easy or Hard?

Chin, T.-J., Cai, Z., & Neumann, F. (2018). Robust Fitting in Computer Vision: Easy or Hard? Retrieved from http://arxiv.org/abs/1802.06464

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Star Tracking using an Event Camera

Chin, T.-J., Bagchi, S., & Eriksson, A. (2018). Star Tracking using an Event Camera. Retrieved from https://arxiv.org/pdf/1812.02895

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Monocular Depth Estimation with Augmented Ordinal Depth Relationships

Cao, Y., Zhao, T., Xian, K., Shen, C., & Cao, Z. (2018). Monocular Depth Estimation with Augmented Ordinal Depth Relationships. Retrieved from http://arxiv.org/abs/1806.00585

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Adversarial Learning with Local Coordinate Coding

Cao, J., Guo, Y., Wu, Q., Shen, C., Huang, J., & Tan, M. (2018). Adversarial Learning with Local Coordinate Coding. Retrieved from http://arxiv.org/abs/1806.04895

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Deterministic Consensus Maximization with Biconvex Programming

Cai, Z., Chin, T.-J., Le, H., & Suter, D. (2018). Deterministic Consensus Maximization with Biconvex Programming. Retrieved from https://arxiv.org/pdf/1807.09436.pdf

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What’s to know? Uncertainty as a Guide to Asking Goal-oriented Questions.

Abbasnejad, E., Wu, Q., Shi, J., & Van Den Hengel, A. (2018). What’s to know? Uncertainty as a Guide to Asking Goal-oriented Questions. Retrieved from https://arxiv.org/pdf/1812.06401.pdf

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An Active Information Seeking Model for Goal-oriented Vision-and-Language Tasks

Abbasnejad, E., Wu, Q., Abbasnejad, I., Shi, J., & Van Den Hengel, A. (2018). An Active Information Seeking Model for Goal-oriented Vision-and-Language Tasks. Retrieved from https://arxiv.org/pdf/1812.06398.pdf

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Learning Deployable Navigation Policies at Kilometer Scale from a Single Traversal

Bruce, J., Sünderhauf, N., Mirowski, P., Hadsell, R., & Milford, M. (2018). Learning Deployable Navigation Policies at Kilometer Scale from a Single Traversal. Retrieved from http://arxiv.org/abs/1807.05211

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Probability-based Detection Quality (PDQ): A Probabilistic Approach to Detection Evaluation

Hall, D., Dayoub, F., Skinner, J., Corke, P., Carneiro, G., & Sünderhauf, N. (2018). Probability-based Detection Quality (PDQ): A Probabilistic Approach to Detection Evaluation. Retrieved from http://arxiv.org/abs/1811.10800

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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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QuadricSLAM: Dual Quadrics From Object Detections as Landmarks in Object-Oriented SLAM

Nicholson, L., Milford, M., & Sunderhauf, N. (2019). QuadricSLAM: Dual Quadrics From Object Detections as Landmarks in Object-Oriented SLAM. IEEE Robotics and Automation Letters, 4(1), 1–8. http://doi.org/10.1109/LRA.2018.2866205

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Simultaneous Optical Flow and Segmentation (SOFAS) using Dynamic Vision Sensor

Stoffregen, T., & Kleeman, L. (2018). Simultaneous Optical Flow and Segmentation (SOFAS) using Dynamic Vision Sensor. Retrieved from http://arxiv.org/abs/1805.12326

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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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Efficient Subpixel Refinement with Symbolic Linear Predictors

Lui, V., Geeves, J., Yii, W., & Drummond, T. (2018). Efficient Subpixel Refinement with Symbolic Linear Predictors. Retrieved from http://arxiv.org/abs/1804.10750

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Generative Adversarial Forests for Better Conditioned Adversarial Learning

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ENG: End-to-end Neural Geometry for Robust Depth and Pose Estimation using CNNs

Dharmasiri, T., Spek, A., & Drummond, T. (2018). ENG: End-to-end Neural Geometry for Robust Depth and Pose Estimation using CNNs. Retrieved from http://arxiv.org/abs/1807.05705

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Relative Pose Based Redundancy Removal: Collaborative RGB-D Data Transmission in Mobile Visual Sensor Networks

Wang, X., Şekercioğlu, Y., Drummond, T., Frémont, V., Natalizio, E., & Fantoni, I. (2018). Relative Pose Based Redundancy Removal: Collaborative RGB-D Data Transmission in Mobile Visual Sensor Networks. Sensors, 18(8), 2430. http://doi.org/10.3390/s18082430

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Real-Time Joint Semantic Segmentation and Depth Estimation Using Asymmetric Annotations

Nekrasov, V., Dharmasiri, T., Spek, A., Drummond, T., Shen, C., & Reid, I. (2018). Real-Time Joint Semantic Segmentation and Depth Estimation Using Asymmetric Annotations. Retrieved from http://arxiv.org/abs/1809.04766

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

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Deep Metric Learning and Image Classification with Nearest Neighbour Gaussian Kernels

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Approximate Fisher Information Matrix to Characterise the Training of Deep Neural Networks

Liao, Z., Drummond, T., Reid, I., & Carneiro, G. (2018). Approximate Fisher Information Matrix to Characterise the Training of Deep Neural Networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1–1. http://doi.org/10.1109/TPAMI.2018.2876413

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A review of deep learning in the study of materials degradation

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Traversing Latent Space using Decision Ferns

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Decoupled Spatial Neural Attention for Weakly Supervised Semantic Segmentation

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An Extended Filtered Channel Framework for Pedestrian Detection

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Unsupervised Learning of Monocular Depth Estimation and Visual Odometry with Deep Feature Reconstruction

Zhan, H., Garg, R., Weerasekera, C. S., Li, K., Agarwal, H., & Reid, I. (2018). Unsupervised Learning of Monocular Depth Estimation and Visual Odometry with Deep Feature Reconstruction. Retrieved from http://arxiv.org/abs/1803.03893

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An Embarrassingly Simple Approach to Visual Domain Adaptation

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Cluster Sparsity Field: An Internal Hyperspectral Imagery Prior for Reconstruction

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Multi-label learning based deep transfer neural network for facial attribute classification

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An end-to-end TextSpotter with Explicit Alignment and Attention

He, T., Tian, Z., Huang, W., Shen, C., Qiao, Y., & Sun, C. (2018). An end-to-end TextSpotter with Explicit Alignment and Attention. Retrieved from https://arxiv.org/abs/1803.03474

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Multi-Task Structure-aware Context Modeling for Robust Keypoint-based Object Tracking

Li, X., Zhao, L., Ji, W., Wu, Y., Wu, F., Yang, M.-H., Dacheng, T., Reid, I. (2018). Multi-Task Structure-aware Context Modeling for Robust Keypoint-based Object Tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1–1. https://doi.org/10.1109/TPAMI.2018.2818132 *In Press

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Deep Perm-Set Net: Learn to predict sets with unknown permutation and cardinality using deep neural networks

Rezatofighi, S. H., Kaskman, R., Motlagh, F. T., Shi, Q., Cremers, D., Leal-Taixé, L., & Reid, I. (2018). Deep Perm-Set Net: Learn to predict sets with unknown permutation and cardinality using deep neural networks. Retrieved from https://arxiv.org/abs/1805.00613

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Fast Neural Architecture Search of Compact Semantic Segmentation Models via Auxiliary Cells

Nekrasov, V., Chen, H., Shen, C., & Reid, I. (2018). Fast Neural Architecture Search of Compact Semantic Segmentation Models via Auxiliary Cells *. Retrieved from https://arxiv.org/pdf/1810.10804.pdf

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Memorable Maps: A Framework for Re-defining Places in Visual Place Recognition

Zaffar, M., Ehsan, S., Milford, M., & Maier, K. M. (2018). Memorable Maps: A Framework for Re-defining Places in Visual Place Recognition. Retrieved from https://arxiv.org/pdf/1811.03529.pdf

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Component-based Attention for Large-scale Trademark Retrieval

Tursun, O., Denman, S., Sivipalan, S., Sridharan, S., Fookes, C., & Mau, S. (2018). Component-based Attention for Large-scale Trademark Retrieval. Retrieved from http://arxiv.org/abs/1811.02746

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The limits and potentials of deep learning for robotics

Sünderhauf, N., Brock, O., Scheirer, W., Hadsell, R., Fox, D., Leitner, J., Upcroft, B., Abbeel, P., Burgard, W., Milford, M., & Corke, P. (2018). The limits and potentials of deep learning for robotics. The International Journal of Robotics Research, 37(4–5), 405–420. http://doi.org/10.1177/0278364918770733

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Learning Free-Form Deformations for 3D Object Reconstruction

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An Orientation Factor for Object-Oriented SLAM

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Multi-View Picking: Next-best-view Reaching for Improved Grasping in Clutter

Morrison, D., Corke, P., & Leitner, J. (2018). Multi-View Picking: Next-best-view Reaching for Improved Grasping in Clutter. Retrieved from http://arxiv.org/abs/1809.08564

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Evaluating Merging Strategies for Sampling-based Uncertainty Techniques in Object Detection

Miller, D., Dayoub, F., Milford, M., & Sünderhauf, N. (2018). Evaluating Merging Strategies for Sampling-based Uncertainty Techniques in Object Detection. Retrieved from http://arxiv.org/abs/1809.06006

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An adaptive localization system for image storage and localization latency requirements

Mao, J., Hu, X., & Milford, M. (2018). An adaptive localization system for image storage and localization latency requirements. Robotics and Autonomous Systems, 107, 246–261. http://doi.org/10.1016/J.ROBOT.2018.06.007

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A Binary Optimization Approach for Constrained K-Means Clustering

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Large scale visual place recognition with sub-linear storage growth

Le, H., & Milford, M. (2018). Large scale visual place recognition with sub-linear storage growth. Retrieved from http://arxiv.org/abs/1810.09660

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A Holistic Visual Place Recognition Approach using Lightweight CNNs for Severe ViewPoint and Appearance Changes

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Feature Map Filtering: Improving Visual Place Recognition with Convolutional Calibration

Hausler, S., Jacobson, A., & Milford, M. (2018). Feature Map Filtering: Improving Visual Place Recognition with Convolutional Calibration. Retrieved from http://arxiv.org/abs/1810.12465

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3D Move to See: Multi-perspective visual servoing for improving object views with semantic segmentation

Lehnert, C., Tsai, D., Eriksson, A., & McCool, C. (2018). 3D Move to See: Multi-perspective visual servoing for improving object views with semantic segmentation. Retrieved from http://arxiv.org/abs/1809.07896

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Show, Attend and Read: A Simple and Strong Baseline for Irregular Text Recognition

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Scalable Deep k-Subspace Clustering

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Automating analysis of vegetation with computer vision: Cover estimates and classification

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

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A rapidly deployable classification system using visual data for the application of precision weed management

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An Overview of Perception Methods for Horticultural Robots: From Pollination to Harvest

Ahn, H. S., Dayoub, F., Popovic, M., MacDonald, B., Siegwart, R., & Sa, I. (2018). An Overview of Perception Methods for Horticultural Robots: From Pollination to Harvest. Retrieved from http://arxiv.org/abs/1807.03124

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Zero-shot Sim-to-Real Transfer with Modular Priors

Lee, R., Mou, S., Dasagi, V., Bruce, J., Leitner, J., & Sünderhauf, N. (2018). Zero-shot Sim-to-Real Transfer with Modular Priors. Retrieved from http://arxiv.org/abs/1809.07480

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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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Quantifying the Reality Gap in Robotic Manipulation Tasks.

Collins, J., Howard, D., & Leitner, J. (2018). Quantifying the Reality Gap in Robotic Manipulation Tasks. Retrieved from https://arxiv.org/abs/1811.01484

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Coordinated Heterogeneous Distributed Perception based on Latent Space Representation

Korthals, T., Leitner, J., & Rückert, U. (2018). Coordinated Heterogeneous Distributed Perception based on Latent Space Representation. Retrieved from https://arxiv.org/abs/1809.04558

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Measures of incentives and confidence in using a social robot

Robinson, N. L., Connolly, J., Johnson, G. M., Kim, Y., Hides, L., & Kavanagh, D. J. (2018). Measures of incentives and confidence in using a social robot. Science Robotics, 3(21), eaat6963. http://doi.org/10.1126/scirobotics.aat6963

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Glare-free retinal imaging using a portable light field fundus camera

Palmer, D. W., Coppin, T., Rana, K., Dansereau, D. G., Suheimat, M., Maynard, M. Atchison, D. A., Roberts, J., Crawford, R., & Jaiprakash, A. (2018). Glare-free retinal imaging using a portable light field fundus camera. Biomedical Optics Express, 9(7), 3178. http://doi.org/10.1364/BOE.9.003178

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Learning to Detect Aircraft for Long-Range Vision-Based Sense-and-Avoid Systems

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Model-free and learning-free grasping by Local Contact Moment matching

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On Encoding Temporal Evolution for Real-time Action Prediction

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Leveraging variable sensor spatial acuity with a homogeneous, multi-scale place recognition framework

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Action Anticipation By Predicting Future Dynamic Images

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Stereo Computation for a Single Mixture Image

Zhong, Y., Dai, Y., & Li, H. (2018). Stereo Computation for a Single Mixture Image. Retrieved from https://arxiv.org/pdf/1808.08690.pdf

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VIENA 2 : A Driving Anticipation Dataset

Aliakbarian, M. S., Saleh, F. S., Salzmann, M., Fernando, B., Petersson, L., & Andersson, L. (2018). VIENA 2 : A Driving Anticipation Dataset. Retrieved from https://arxiv.org/pdf/1810.09044.pdf

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Continuous-time Intensity Estimation Using Event Cameras

Scheerlinck, C., Barnes, N., & Mahony, R. (2018). Continuous-time Intensity Estimation Using Event Cameras. Retrieved from https://arxiv.org/pdf/1811.00386.pdf

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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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Spatial-Temporal Union of Subspaces for Multi-body NRSFM: Supplementary Material.

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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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Neural Algebra of Classifiers

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Identity-preserving Face Recovery from Portraits

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Output regulation for systems on matrix Lie-groups

de Marco, S., Marconi, L., Mahony, R., & Hamel, T. (2018). Output regulation for systems on matrix Lie-groups. Automatica, 87, 8–16. https://doi.org/10.1016/J.AUTOMATICA.2017.08.006

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Quickest Detection of Intermittent Signals With Application to Vision Based Aircraft Detection

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Enabling a Pepper Robot to provide Automated and Interactive Tours of a Robotics Laboratory

Suddrey, G., Jacobson, A., & Ward, B. (2018). Enabling a Pepper Robot to provide Automated and Interactive Tours of a Robotics Laboratory. Retrieved from http://arxiv.org/abs/1804.03288

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Closing the Loop for Robotic Grasping: A Real-time, Generative Grasp Synthesis Approach

Morrison, D., Corke, P., & Leitner, J. (2018). Closing the Loop for Robotic Grasping: A Real-time, Generative Grasp Synthesis Approach. Retrieved from http://arxiv.org/abs/1804.05172

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Leveraging variable sensor spatial acuity with a homogeneous, multi-scale place recognition framework

Jacobson, A., Chen, Z., & Milford, M. (2018). Leveraging variable sensor spatial acuity with a homogeneous, multi-scale place recognition framework. Biological Cybernetics, 1–17. http://doi.org/10.1007/s00422-017-0745-7

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Rhythmic Representations: Learning Periodic Patterns for Scalable Place Recognition at a Sublinear Storage Cost.

Yu, L., Jacobson, A., & Milford, M. (2018). Rhythmic Representations: Learning Periodic Patterns for Scalable Place Recognition at a Sublinear Storage Cost. IEEE Robotics and Automation Letters, 3(2), 811–818. http://doi.org/10.1109/LRA.2018.2792144

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Training Deep Neural Networks for Visual Servoing

Bateux, Q., Marchand, E., Leitner, J., Chaumette, F., & Corke, P. (2018). Training Deep Neural Networks for Visual Servoing. Retrieved from https://hal.inria.fr/hal-01716679/

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Towards vision-based manipulation of plastic materials.

Cherubini, A., Leitner, J., Ortenzi, V., & Corke, P. (2018). Towards vision-based manipulation of plastic materials. Retrieved from https://hal.archives-ouvertes.fr/hal-01731230

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Assisted Control for Semi-Autonomous Power Infrastructure Inspection using Aerial Vehicles

McFadyen, A., Dayoub, F., Martin, S., Ford, J., & Corke, P. (2018). Assisted Control for Semi-Autonomous Power Infrastructure Inspection using Aerial Vehicles. Retrieved from http://arxiv.org/abs/1804.02154

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OpenSeqSLAM2.0: An Open Source Toolbox for Visual Place Recognition Under Changing Conditions

Talbot, B., Garg, S., & Milford, M. (2018). OpenSeqSLAM2.0: An Open Source Toolbox for Visual Place Recognition Under Changing Conditions. Retrieved from http://arxiv.org/abs/1804.02156

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LoST? Appearance-Invariant Place Recognition for Opposite Viewpoints using Visual Semantics

Garg, S., Suenderhauf, N., & Milford, M. (2018). LoST? Appearance-Invariant Place Recognition for Opposite Viewpoints using Visual Semantics. Retrieved from http://arxiv.org/abs/1804.05526

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Don’t Look Back: Robustifying Place Categorization for Viewpoint- and Condition-Invariant Place Recognition

Garg, S., Suenderhauf, N., & Milford, M. (2018). Don’t Look Back: Robustifying Place Categorization for Viewpoint- and Condition-Invariant Place Recognition. Retrieved from http://arxiv.org/abs/1801.05078

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Multimodal Trip Hazard Affordance Detection on Construction Sites

McMahon, S., Sunderhauf, N., Upcroft, B., & Milford, M. (2018). Multimodal Trip Hazard Affordance Detection on Construction Sites. IEEE Robotics and Automation Letters, 3(1), 1–8. http://doi.org/10.1109/LRA.2017.2719763

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Towards Semantic SLAM: Points, Planes and Objects

Hosseinzadeh, M., Latif, Y., Pham, T., Suenderhauf, N., & Reid, I. (2018). Towards Semantic SLAM: Points, Planes and Objects. Retrieved from http://arxiv.org/abs/1804.09111

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Special issue on deep learning in robotics

Sünderhauf, N., Leitner, J., Upcroft, B., & Roy, N. (2018, April 27). Special issue on deep learning in robotics. The International Journal of Robotics Research. SAGE PublicationsSage UK: London, England. http://doi.org/10.1177/0278364918769189

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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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Multi-Modal Trip Hazard Affordance Detection On Construction Sites

McMahon, S., Sunderhauf, N., Upcroft, B., & Milford, M. (2018). Multimodal Trip Hazard Affordance Detection on Construction Sites. IEEE Robotics and Automation Letters, 3(1), 1–8. http://doi.org/10.1109/LRA.2017.2719763

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Australian Centre for Robotic Vision
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