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We introduce Probabilistic Object Detection, the task of detecting objects in images and accurately quantifying the spatial and semantic uncertainties of the detections.
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Robust real-time face detection
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Histograms of oriented gradients for human detection
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Probabilistic robotics
S. Thrun, W. Burgard, and D. Fox · 2005
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The Pascal Visual Object Classes (VOC) Challenge
M. Everingham, L. V. Gool, C. K. I. Williams, J. Winn, and A. Zisserman · 2010
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Object detection with discriminatively trained part-based models
P. F. Felzenszwalb, R. B. Girshick, D. McAllester, and D. Ramanan · 2010
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Unbiased look at dataset bias
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Multi-column deep neural network for traffic sign classification
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Diagnosing error in object detectors
D. Hoiem, Y. Chodpathumwan, and Q. Dai · 2012
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ImageNet Classification with Deep Convolutional Neural Networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
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What is a good evaluation measure for semantic segmentation?
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Safe and interpretable machine learning: A methodological review
C. Otte · 2013
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Microsoft coco: Common objects in context
T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick · 2014
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C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus · 2014
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The pascal visual object classes challenge: A retrospective
M. Everingham, S. A. Eslami, L. Van Gool, C. K. Williams, J. Winn, and A. Zisserman · 2015
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Bayesian convolutional neural networks with bernoulli approximate variational inference
Y. Gal and Z. Ghahramani · 2015
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Fast r-cnn
R. Girshick · 2015
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Region-based convolutional networks for accurate object detection and segmentation
R. Girshick, J. Donahue, T. Darrell, and J. Malik · 2015
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A. Kendall, V. Badrinarayanan, and R. Cipolla · 2015
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Deep learning for detecting robotic grasps
I. Lenz, H. Lee, and A. Saxena · 2015
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Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
A. Nguyen, J. Yosinski, and J. Clune · 2015
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Faster r-cnn: Towards real-time object detection with region proposal networks
What uncertainties do we need in bayesian deep learning for computer vision?
A. Kendall and Y. Gal · 2017
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Openimages: A public dataset for large-scale multi-label and multi-class image classification
I. Krasin, T. Duerig, N. Alldrin, V. Ferrari, S. Abu-El-Haija, A. Kuznetsova, H. Rom, J. Uijlings, S. Popov, S. Kamali, M. Malloci, J. Pont-Tuset, A. Veit, S. Belongie, V. Gomes, A. Gupta, C. Sun, G. Chechik, D. Cai, Z. Feng, D. Narayanan, and K. Murphy · 2017
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MINOS: Multimodal indoor simulator for navigation in complex environments
M. Savva, A. X. Chang, A. Dosovitskiy, T. Funkhouser, and V. Koltun · 2017
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Bayesian image quality transfer with cnns: exploring uncertainty in dmri super-resolution
R. Tanno, D. E. Worrall, A. Ghosh, E. Kaden, S. N. Sotiropoulos, A. Criminisi, and D. C. Alexander · 2017
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On the safety of machine learning: Cyber-physical systems, decision sciences, and data products
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S. Ren, K. He, R. Girshick, and J. Sun · 2015
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ImageNet Large Scale Visual Recognition Challenge
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei · 2015
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Concrete problems in ai safety
D. Amodei, C. Olah, J. Steinhardt, P. Christiano, J. Schulman, and D. Mané · 2016
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R-fcn: Object detection via region-based fully convolutional networks
J. Dai, Y. Li, K. He, and J. Sun · 2016
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Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Y. Gal and Z. Ghahramani · 2016
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Semantic segmentation of small objects and modeling of uncertainty in urban remote sensing images using deep convolutional neural networks
M. Kampffmeyer, A.-B. Salberg, and R. Jenssen · 2016
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Ssd: Single shot multibox detector
W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu, and A. C. Berg · 2016
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K. R. Varshney and H. Alemzadeh · 2017
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A Faster Pytorch Implementation of Faster R-CNN
J. Yang, J. Lu, D. Batra, and D. Parikh · 2017
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A. Kuznetsova, H. Rom, N. Alldrin, J. Uijlings, I. Krasin, J. Pont-Tuset, S. Kamali, S. Popov, M. Malloci, T. Duerig, and V. Ferrari · 2018
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Focal loss for dense object detection
T.-Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár · 2018
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maskrcnn-benchmark: Fast, modular reference implementation of Instance Segmentation and Object Detection algorithms in PyTorch
F. Massa and R. Girshick · 2018
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Dropout Sampling for Robust Object Detection in Open-Set Conditions
D. Miller, L. Nicholson, F. Dayoub, M. Milford, and N. Sünderhauf · 2018
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Exploring uncertainty measures in deep networks for multiple sclerosis lesion detection and segmentation
T. Nair, D. Precup, D. L. Arnold, and T. Arbel · 2018
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Risks of deep reinforcement learning applied to fall prevention assist by autonomous mobile robots in the hospital
T. Namba and Y. Yamada · 2018
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Localization recall precision (lrp): A new performance metric for object detection
K. Oksuz, B. Can Cam, E. Akbas, and S. Kalkan · 2018
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YOLOv3: An Incremental Improvement
J. Redmon and A. Farhadi · 2018
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Bayesian learning for safe high-speed navigation in unknown environments
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The limits and potentials of deep learning for robotics
N. Sünderhauf, O. Brock, W. Scheirer, R. Hadsell, D. Fox, J. Leitner, B. Upcroft, P. Abbeel, W. Burgard, M. Milford, et al · 2018
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Evaluating Merging Strategies for Sampling-based Uncertainty Techniques in Object Detection
D. Miller, F. Dayoub, M. Milford, and N. Sünderhauf · 2019
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