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This paper proposes to go beyond the state-of-the-art deep convolutional neural network (CNN) by incorporating the information from object detection, focusing on dealing with fine-grained image classification.
Convolutional networks for images, speech, and time series
Y. LeCun and Y. Bengio · 1995
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Histograms of oriented gradients for human detection
N. Dalal and B. Triggs · 2005
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A discriminatively trained, multiscale, deformable part model
P. Felzenszwalb, D. McAllester, and D. Ramanan · 2008
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Automated flower classification over a large number of classes
M.-E. Nilsback and A. Zisserman · 2008
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An HOG-LBP human detector with partial occlusion handling
X. Wang, T. X. Han, and S. Yan · 2009
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Novel dataset for fine-grained image categorization
A. Khosla, N. Jayadevaprakash, B. Yao, and F.-f. Li · 2011
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Segmentation as selective search for object recognition
K. E. A. Van de Sande, J. R. R. Uijlings, T. Gevers, and A. W. M. Smeulders · 2011
Earlier work this paper cites.
The Caltech-UCSD Birds-200-2011 Dataset
C. Wah, S. Branson, P. Welinder, P. Perona, and S. Belongie · 2011
Cited alongside, same era.
ImageNet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
Cited alongside, same era.
Cats and dogs
O. M. Parkhi, A. Vedaldi, A. Zisserman, and C. V. Jawahar · 2012
Cited alongside, same era.
Object-centric spatial pooling for image classification
O. Russakovsky, Y. Lin, K. Yu, and L. Fei-Fei · 2012
Cited alongside, same era.
Unsupervised Template Learning for Fine-Grained Object Recognition
S. Yang, L. Bo, J. Wang, and L. Shapiro · 2012
Cited alongside, same era.
Pose pooling kernels for sub-category recognition
N. Zhang, R. Farrell, and T. Darrell · 2012
Cited alongside, same era.
3d object representations for fine-grained categorization, 2013
J. Krause, M. Stark, J. Deng, and L. Fei-Fei · 2013
Later among the works it cites.
Regionlets for generic object detection
X. Wang, M. Yang, S. Zhu, and Y. Lin · 2013
Later among the works it cites.
Rich feature hierarchies for accurate object detection and semantic segmentation
R. Girshick, J. Donahue, T. Darrell, and J. Malik · 2014
Closest in time.
Accurate object detection with location relaxation and regionlets relocalization
C. Long, X. Wang, G. Hua, M. Yang, and Y. Lin · 2014
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Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
Closest in time.
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Fine-grained crowdsourcing for fine-grained recognition
J. Deng, J. Krause, and L. Fei-Fei · 2013
Cited alongside, same era.
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich · 2014
Closest in time.