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In existing works that learn representation for object detection, the relationship between a candidate window and the ground truth bounding box of an object is simplified by thresholding their overlap.
Multiresolution gray-scale and rotation invariant texture classification with local binary patterns
T. Ojala, M. Pietikainen, and T. Maenpaa · 2002
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Distinctive image features from scale-invarian keypoints
D. Lowe · 2004
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Histograms of oriented gradients for human detection
N. Dalal and B. Triggs · 2005
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Clustering by passing messages between data points
B. J. Frey and D. Dueck · 2007
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Segmentation as selective search for object recognition
A. Smeulders, T. Gevers, N. Sebe, and C. Snoek · 2011
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Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. Hinton · 2012
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Building high-level features using large scale unsupervised learning
Q. V. Le, M. Ranzato, R. Monga, M. Devin, K. Chen, G. S. Corrado, J. Dean, and A. Y. Ng · 2012
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Overfeat: Integrated recognition, localization and detection using convolutional networks
P. Sermanet, D. Eigen, X. Zhang, M. Mathieu, R. Fergus, and Y. LeCun · 2013
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Deep neural networks for object detection
C. Szegedy, A. Toshev, and D. Erhan · 2013
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Return of the devil in the details: Delving deep into convolutional nets
K. Chatfield, K. Simonyan, A. Vedaldi, and A. Zisserman · 2014
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Decaf: A deep convolutional activation feature for generic visual recognition
J. Donahue, Y. Jia, O. Vinyals, J. Hoffman, N. Zhang, E. Tzeng, and T. Darrell · 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
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Spatial pyramid pooling in deep convolutional networks for visual recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2014
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Network in network
M. Lin, Q. Chen, and S. Yan · 2014
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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 · 2014
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Fisher and vlad with flair
K. E. A. van de Sande, C. G. M. Snoek, and A. W. M. Smeulders · 2014
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Generic object detection with dense neural patterns and regionlets
W. Y. Zou, X. Wang, M. Sun, and Y. Lin · 2014
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Object detection via a multi-region & semantic segmentation-aware cnn model
S. Gidaris and N. Komodakis · 2015
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R. Girshick · 2015
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Deepid-net: multi-stage and deformable deep convolutional neural networks for object detection
W. Ouyang, P. Luo, X. Zeng, S. Qiu, Y. Tian, H. Li, S. Yang, Z. Wang, Y. Xiong, C. Qian, et al · 2014
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Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
Cited alongside, same era.
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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Object detection using deep neural networks, 2015
C. Szegedy, D. Erhan, and A. T. Toshev · 2015
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Attentionnet: Aggregating weak directions for accurate object detection
D. Yoo, S. Park, J.-Y. Lee, A. Paek, and I. S. Kweon · 2015
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