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We define the object detection from imagery problem as estimating a very large but extremely sparse bounding box dependent probability distribution.
Gradient-based learning applied to document recognition
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner · 1998
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The pascal visual object classes (voc) challenge
M. Everingham, L. Gool, C. K. Williams, J. Winn, and A. Zisserman · 2010
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On the importance of initialization and momentum in deep learning
I. Sutskever, J. Martens, G. E. Dahl, and G. E. Hinton · 2013
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Selective search for object recognition
J. R. R. Uijlings, K. E. A. van de Sande, T. Gevers, and A. W. M. Smeulders · 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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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. S. Bernstein, A. C. Berg, and F. Li · 2014
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Fast r-cnn
R. Girshick · 2015
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Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
K. He, X. Zhang, S. Ren, and J. Sun · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
S. Ioffe and C. Szegedy · 2015
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SSD: single shot multibox detector
W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. E. Reed, C. Fu, and A. C. Berg · 2015
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Learning deconvolution network for semantic segmentation
H. Noh, S. Hong, and B. Han · 2015
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Faster r-cnn: Towards real-time object detection with region proposal networks
S. Ren, K. He, R. Girshick, and J. Sun · 2015
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Laplacian pyramid reconstruction and refinement for semantic segmentation
G. Ghiasi and C. C. Fowlkes · 2016
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Region-based convolutional networks for accurate object detection and segmentation
R. Girshick, J. Donahue, T. Darrell, and J. Malik · 2016
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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R-fcn: Object detection via region-based fully convolutional networks
Y. Li, K. He, J. Sun, et al · 2016
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Feature pyramid networks for object detection
T. Lin, P. Dollár, R. B. Girshick, K. He, B. Hariharan, and S. J. Belongie · 2016
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You only look once: Unified, real-time object detection
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Training very deep networks
R. K. Srivastava, K. Greff, and J. Schmidhuber · 2015
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Going deeper with convolutions
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich · 2015
Cited alongside, same era.
J. Redmon, S. Divvala, R. Girshick, and A. Farhadi · 2016
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Training region-based object detectors with online hard example mining
A. Shrivastava, A. Gupta, and R. Girshick · 2016
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