Fetching the paper…
Reading the bibliography…
In the scenario of one/multi-shot learning, conventional end-to-end learning strategies without sufficient supervision are usually not powerful enough to learn correct patterns from noisy signals.
Gradient-based learning applied to document recognition
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner · 1998
Earlier work this paper cites.
Max-margin and/or graph learning for parsing the human body
L. Zhu, Y. Chen, Y. Lu, C. Lin, and A. Yuille · 2008
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
Earlier work this paper cites.
The caltech-ucsd birds-200-2011 dataset
C. Wah, S. Branson, P. Welinder, P. Perona, and S. Belongie · 2011
Earlier work this paper cites.
Object detection using strongly-supervised deformable part models
H. Azizpour and I. Laptev · 2012
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. Hinton · 2012
Earlier work this paper cites.
Modeling occlusion by discriminative and-or structures
B. Li, W. Hu, T. Wu, and S.-C. Zhu · 2013
Earlier work this paper cites.
Learning and-or templates for object recognition and detection
Z. Si and S.-C. Zhu · 2013
Earlier work this paper cites.
Deep inside convolutional networks: Visualising image classification models and saliency maps
K. Simonyan, A. Vedaldi, and A. Zisserman · 2013
Cited alongside, same era.
Detect what you can: Detecting and representing objects using holistic models and body parts
X. Chen, R. Mottaghi, X. Liu, S. Fidler, R. Urtasun, and A. Yuille · 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.
Visualizing and understanding convolutional networks
M. D. Zeiler and R. Fergus · 2014
Cited alongside, same era.
Understanding deep features with computer-generated imagery
M. Aubry and B. C. Russell · 2015
Cited alongside, same era.
Fast r-cnn
R. Girshick · 2015
Neural activation constellations: Unsupervised part model discovery with convolutional networks
M. Simon and E. Rodner · 2015
Later among the works it cites.
Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2015
Later among the works it cites.
Object detectors emerge in deep scene cnns
B. Zhou, A. Khosla, A. Lapedriza, A. Oliva, and A. Torralba · 2015
Later among the works it cites.
Inverting visual representations with convolutional networks
A. Dosovitskiy and T. Brox · 2016
Later among the works it cites.
Learning deep features for discriminative localization
B. Zhou, A. Khosla, A. Lapedriza, A. Oliva, and A. Torralba · 2016
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Understanding deep image representations by inverting them
A. Mahendran and A. Vedaldi · 2015
Cited alongside, same era.
Q. Zhang, R. Cao, Y. Wu, and S.-C. Zhu · 2017
Closest in time.
Growing interpretable part graphs on convnets via multi-shot learning
Q. Zhang, R. Cao, Y. N. Wu, and S.-C. Zhu · 2017
Closest in time.