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Pooling second-order local feature statistics to form a high-dimensional bilinear feature has been shown to achieve state-of-the-art performance on a variety of fine-grained classification tasks.
Learning the parts of objects by non-negative matrix factorization
D. D. Lee and H. S. Seung · 1999
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Separating style and content with bilinear models
J. B. Tenenbaum and W. T. Freeman · 2000
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A framework for learning predictive structures from multiple tasks and unlabeled data
R. K. Ando and T. Zhang · 2005
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Modeling appearances with low-rank svm
L. Wolf, H. Jhuang, and T. Hazan · 2007
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On the burstiness of visual elements
H. Jégou, M. Douze, and C. Schmid · 2009
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Bilinear classifiers for visual recognition
H. Pirsiavash, D. Ramanan, and C. C. Fowlkes · 2009
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Improving the fisher kernel for large-scale image classification
F. Perronnin, J. Sánchez, and T. Mensink · 2010
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The caltech-ucsd birds-200-2011 dataset
C. Wah, S. Branson, P. Welinder, P. Perona, and S. Belongie · 2011
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Semantic segmentation with second-order pooling
J. Carreira, R. Caseiro, J. Batista, and C. Sminchisescu · 2012
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Random feature maps for dot product kernels
P. Kar and H. Karnick · 2012
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3d object representations for fine-grained categorization
J. Krause, M. Stark, J. Deng, and L. Fei-Fei · 2013
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Fine-grained visual classification of aircraft
S. Maji, E. Rahtu, J. Kannala, M. Blaschko, and A. Vedaldi · 2013
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Fast and scalable polynomial kernels via explicit feature maps
N. Pham and R. Pagh · 2013
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Deep inside convolutional networks: Visualising image classification models and saliency maps
K. Simonyan, A. Vedaldi, and A. Zisserman · 2013
Cited alongside, same era.
Describing textures in the wild
M. Cimpoi, S. Maji, I. Kokkinos, S. Mohamed, and A. Vedaldi · 2014
Cited alongside, same era.
Identifying and attacking the saddle point problem in high-dimensional non-convex optimization
Y. N. Dauphin, R. Pascanu, C. Gulcehre, K. Cho, S. Ganguli, and Y. Bengio · 2014
Cited alongside, same era.
Low-rank bilinear classification: Efficient convex optimization and extensions
T. Kobayashi · 2014
Cited alongside, same era.
Part detector discovery in deep convolutional neural networks
M. Simon, E. Rodner, and J. Denzler · 2014
Cited alongside, same era.
Very deep convolutional networks for large-scale image recognition
Spatial transformer networks
M. Jaderberg, K. Simonyan, A. Zisserman, et al · 2015
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Fine-grained recognition without part annotations
J. Krause, H. Jin, J. Yang, and L. Fei-Fei · 2015
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The unreasonable effectiveness of noisy data for fine-grained recognition
J. Krause, B. Sapp, A. Howard, H. Zhou, A. Toshev, T. Duerig, J. Philbin, and L. Fei-Fei · 2015
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Bilinear cnn models for fine-grained visual recognition
T.-Y. Lin, A. RoyChowdhury, and S. Maji · 2015
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The treasure beneath convolutional layers: Cross-convolutional-layer pooling for image classification
L. Liu, C. Shen, and A. van den Hengel · 2015
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Matconvnet: Convolutional neural networks for matlab
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K. Simonyan and A. Zisserman · 2014
Cited alongside, same era.
Visualizing and understanding convolutional networks
M. D. Zeiler and R. Fergus · 2014
Cited alongside, same era.
Part-based r-cnns for fine-grained category detection
N. Zhang, J. Donahue, R. Girshick, and T. Darrell · 2014
Cited alongside, same era.
Learning deep features for scene recognition using places database
B. Zhou, A. Lapedriza, J. Xiao, A. Torralba, and A. Oliva · 2014
Cited alongside, same era.
The loss surfaces of multilayer networks
A. Choromanska, M. Henaff, M. Mathieu, G. B. Arous, and Y. LeCun · 2015
Cited alongside, same era.
Deep filter banks for texture recognition and segmentation
M. Cimpoi, S. Maji, and A. Vedaldi · 2015
Cited alongside, same era.
Y. Cui, F. Zhou, Y. Lin, and S. Belongie · 2015
Cited alongside, same era.
A. Vedaldi and K. Lenc · 2015
Later among the works it cites.
Compact bilinear pooling
Y. Gao, O. Beijbom, N. Zhang, and T. Darrell · 2016
Closest in time.
Spatially aware dictionary learning and coding for fossil pollen identification
S. Kong, S. Punyasena, and C. Fowlkes · 2016
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Sparse coding for third-order super-symmetric tensor descriptors with application to texture recognition
P. Koniusz and A. Cherian · 2016
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”why should i trust you?”: Explaining the predictions of any classifier
M. T. Ribeiro, S. Singh, and C. Guestrin · 2016
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Mining discriminative triplets of patches for fine-grained classification
Y. Wang, J. Choi, V. I. Morariu, and L. S. Davis · 2016
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Spda-cnn: Unifying semantic part detection and abstraction for fine-grained recognition
H. Zhang, T. Xu, M. Elhoseiny, X. Huang, S. Zhang, A. Elgammal, and D. Metaxas · 2016
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Fine-grained pose prediction, normalization, and recognition
N. Zhang, E. Shelhamer, Y. Gao, and T. Darrell · 2016
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