Fetching the paper…
Reading the bibliography…
Despite the importance of image representations such as histograms of oriented gradients and deep Convolutional Neural Networks (CNN), our theoretical understanding of them remains limited.
Principles for automatic scale selection
T. Lindeberg · 1998
Earlier work this paper cites.
Object recognition from local scale-invariant features
D. G. Lowe · 1999
Earlier work this paper cites.
Representing and recognizing the visual appearance of materials using three-dimensional textons
T. Leung and J. Malik · 2001
Earlier work this paper cites.
A performance evaluation of local descriptors
K. Mikolajczyk and C. Schmid · 2003
Earlier work this paper cites.
Video Google: A text retrieval approach to object matching in videos
J. Sivic and A. Zisserman · 2003
Earlier work this paper cites.
Max-margin markov networks
B. Taskar, C. Guestrin, and D. Koller · 2003
Earlier work this paper cites.
Visual categorization with bags of keypoints
G. Csurka, C. R. Dance, L. Dan, J. Willamowski, and C. Bray · 2004
Earlier work this paper cites.
Distinctive image features from scale-invariant keypoints
D. G. Lowe · 2004
Earlier work this paper cites.
Histograms of oriented gradients for human detection
N. Dalal and B. Triggs · 2005
Earlier work this paper cites.
Fisher kernels on visual vocabularies for image categorizaton
F. Perronnin and C. Dance · 2006
Earlier work this paper cites.
The PASCAL visual obiect classes challenge 2007 (VOC2007) results
M. Everingham, A. Zisserman, C. Williams, and L. V. Gool · 2007
Earlier work this paper cites.
Measuring invariances in deep networks
I. Goodfellow, H. Lee, Q. V. Le, A. Saxe, and A. Y. Ng · 2009
Earlier work this paper cites.
Aggregating local descriptors into a compact image representation
H. Jégou, M. Douze, C. Schmid, and P. Pérez · 2010
Cited alongside, same era.
VLFeat – An open and portable library of computer vision algorithms
A. Vedaldi and B. Fulkerson · 2010
Cited alongside, same era.
Locality-constrained linear coding for image classification
J. Wang, J. Yang, K. Yu, F. Lv, T. Huang, and Y. Gong · 2010
Cited alongside, same era.
Supervised translation-invariant sparse coding
J. Yang, K. Yu, and T. Huang · 2010
Cited alongside, same era.
Image classification using super-vector coding of local image descriptors
X. Zhou, K. Yu, T. Zhang, and T. S. Huang · 2010
Cited alongside, same era.
The truth about cats and dogs
O. Parkhi, A. Vedaldi, C. V. Jawahar, and A. Zisserman · 2011
Cited alongside, same era.
Rotation, scaling and deformation invariant scattering for texture discrimination
L. Sifre and S. Mallat · 2013
Later among the works it cites.
Deep inside convolutional networks: Visualising image classification models and saliency maps
K. Simonyan, A. Vedaldi, and A. Zisserman · 2013
Later among the works it cites.
HOGgles: Visualizing object detection features
C. Vondrick, A. Khosla, T. Malisiewicz, and A. Torralba · 2013
Later among the works it cites.
Visualizing and understanding convolutional networks
M. D. Zeiler and R. Fergus · 2013
Later among the works it cites.
Return of the devil in the details: Delving deep into convolutional nets
K. Chatfield, K. Simonyan, A. Vedaldi, and A. Zisserman · 2014
Closest in time.
Detect what you can: Detecting and representing objects using holistic models and body parts
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
Cited alongside, same era.
Learning rotation-aware features: From invariant priors to equivariant descriptors
U. Schimdt and S. Roth · 2012
Cited alongside, same era.
Spasm: A matlab toolbox for sparse statistical modeling
K. Sjöstrand, L. H. Clemmensen, R. Larsen, and B. Ersbøll · 2012
Cited alongside, same era.
Learning invariant representations with local transformations
K. Sohn and H. Lee · 2012
Cited alongside, same era.
Predicting parameters in deep learning
M. Denil, , B. Shakibi, L. Dinh, M. Ranzato, and N. de Freitas · 2013
Cited alongside, same era.
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 · 2013
Cited alongside, same era.
X. Chen, R. Mottaghi, X. Liu, S. Fidler, R. Urtasun, and A. Yuille · 2014
Closest in time.
CNN features off-the-shelf: an astounding baseline for recognition
A. S. Razavian, H. Azizpour, J. Sullivan, and S. Carlsson · 2014
Closest in time.
Imagenet large scale visual recognition challenge, 2014
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 · 2014
Closest in time.
Overfeat: Integrated recognition, localization and detection using convolutional networks
P. Sermanet, D. Eigen, X. Zhang, M. Mathieu, R. Fergus, and Y. LeCun · 2014
Closest in time.
MatConvNet - convolutional neural networks for MATLAB
A. Vedaldi and K. Lenc · 2014
Closest in time.
Learning Deep Features for Scene Recognition using Places Database
B. Zhou, A. Lapedriza, J. Xiao, A. Torralba, and A. Oliva · 2014
Closest in time.