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VGGNets have turned out to be effective for object recognition in still images.
Gradient-based learning applied to document recognition
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner · 1998
Earlier work this paper cites.
ImageNet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L. Li, K. Li, and F. Li · 2009
Earlier work this paper cites.
Recognizing indoor scenes
A. Quattoni and A. Torralba · 2009
Earlier work this paper cites.
SUN database: Large-scale scene recognition from abbey to zoo
J. Xiao, J. Hays, K. A. Ehinger, A. Oliva, and A. Torralba · 2010
Earlier work this paper cites.
ImageNet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
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. S. Bernstein, A. C. Berg, and F. Li · 2014
Cited alongside, same era.
Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
Cited alongside, same era.
Going deeper with convolutions
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich · 2014
Cited alongside, same era.
Caffe: Convolutional architecture for fast feature embedding
Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. B. Girshick, S. Guadarrama, and T. Darrell
Cited in the paper.
Learning deep features for scene recognition using places database
B. Zhou, À. Lapedriza, J. Xiao, A. Torralba, and A. Oliva · 2014
Later among the works it cites.
Training deeper convolutional networks with deep supervision
L. Wang, C. Lee, Z. Tu, and S. Lazebnik · 2015
Closest in time.
Towards good practices for very deep two-stream ConvNets
L. Wang, Y. Xiong, Z. Wang, and Y. Qiao · 2015
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