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State-of-the-art object detection systems rely on an accurate set of region proposals.
Contextual guidance of eye movements and attention in real-world scenes: the role of global features in object search
A. Torralba, A. Oliva, M. S. Castelhano, and J. M. Henderson · 2006
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The PASCAL Visual Object Classes Challenge 2007 (VOC2007) Results
M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman · 2007
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An empirical study of context in object detection
S. K. Divvala, D. Hoiem, J. H. Hays, A. Efros, M. Hebert, et al · 2009
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Efficient subwindow search: A branch and bound framework for object localization
C. H. Lampert, M. B. Blaschko, and T. Hofmann · 2009
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The PASCAL Visual Object Classes Challenge 2012 (VOC2012) Results
M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman · 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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State-of-the-art in visual attention modeling
A. Borji and L. Itti · 2013
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Deep neural networks for object detection
C. Szegedy, A. Toshev, and D. Erhan · 2013
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Selective search for object recognition
J. R. Uijlings, K. E. van de Sande, T. Gevers, and A. W. Smeulders · 2013
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Scalable object detection using deep neural networks
D. Erhan, C. Szegedy, A. Toshev, and D. Anguelov · 2014
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Rich feature hierarchies for accurate object detection and semantic segmentation
R. Girshick, J. Donahue, T. Darrell, and J. Malik · 2014
Cited alongside, same era.
Spatial pyramid pooling in deep convolutional networks for visual recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2014
Cited alongside, same era.
Caffe: Convolutional architecture for fast feature embedding
Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell · 2014
Cited alongside, same era.
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
Cited alongside, same era.
Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
Cited alongside, same era.
Fast r-cnn
R. Girshick · 2015
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An active search strategy for efficient object class detection
A. Gonzalez-Garcia, A. Vezhnevets, and V. Ferrari · 2015
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2015
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Towards computational baby learning: A weakly-supervised approach for object detection
X. Liang, S. Liu, Y. Wei, L. Liu, L. Lin, and S. Yan · 2015
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Ssd: Single shot multibox detector
W. Liu, D. Anguelov, D. Erhan, C. Szegedy, and S. Reed · 2015
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C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich · 2014
Cited alongside, same era.
Edge boxes: Locating object proposals from edges
C. L. Zitnick and P. Dollár · 2014
Cited alongside, same era.
Inside-outside net: Detecting objects in context with skip pooling and recurrent neural networks
S. Bell, C. L. Zitnick, K. Bala, and R. Girshick · 2015
Cited alongside, same era.
Active object localization with deep reinforcement learning
J. C. Caicedo and S. Lazebnik · 2015
Cited alongside, same era.
Object detection via a multi-region and semantic segmentation-aware cnn model
S. Gidaris and N. Komodakis · 2015
Cited alongside, same era.
Y. Lu and T. Javidi · 2015
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You only look once: Unified, real-time object detection
J. Redmon, S. Divvala, R. Girshick, and A. Farhadi · 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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Attentionnet: Aggregating weak directions for accurate object detection
D. Yoo, S. Park, J.-Y. Lee, A. S. Paek, and I. So Kweon · 2015
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