Fetching the paper…
Reading the bibliography…
Low-shot learning methods for image classification support learning from sparse data.
Cross-generalization: Learning novel classes from a single example by feature replacement
Evgeniy Bart and Shimon Ullman · 2005
Earlier work this paper cites.
One-shot learning of object categories
Fei-Fei Li, Rob Fergus, and Pietro Perona · 2006
Earlier work this paper cites.
Cosegmentation of image pairs by histogram matching-incorporating a global constraint into mrfs
Carsten Rother, Tom Minka, Andrew Blake, and Vladimir Kolmogorov · 2006
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.
An efficient algorithm for co-segmentation
Dorit S Hochbaum and Vikas Singh · 2009
Earlier work this paper cites.
The PASCAL Visual Object Classes Challenge 2012 (VOC2012) Results
M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman · 2012
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
Earlier work this paper cites.
One-shot learning with a hierarchical nonparametric bayesian model
Ruslan Salakhutdinov, Joshua B Tenenbaum, and Antonio Torralba · 2012
Earlier work this paper cites.
Co-segmentation by composition
Alon Faktor and Michal Irani · 2013
Earlier work this paper cites.
Rich feature hierarchies for accurate object detection and semantic segmentation
Ross Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik · 2014
Earlier work this paper cites.
Simultaneous detection and segmentation
Bharath Hariharan, Pablo Arbeláez, Ross Girshick, and Jitendra Malik · 2014
Earlier work this paper cites.
Fully convolutional multi-class multiple instance learning
Deepak Pathak, Evan Shelhamer, Jonathan Long, and Trevor Darrell · 2014
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
Earlier work this paper cites.
Compressing neural networks with the hashing trick
Wenlin Chen, James T Wilson, Stephen Tyree, Kilian Q Weinberger, and Yixin Chen · 2015
Cited alongside, same era.
Boxsup: Exploiting bounding boxes to supervise convolutional networks for semantic segmentation
Jifeng Dai, Kaiming He, and Jian Sun · 2015
Cited alongside, same era.
Hypercolumns for object segmentation and fine-grained localization
Bharath Hariharan, Pablo Arbeláez, Ross Girshick, and Jitendra Malik · 2015
Cited alongside, same era.
Decoupled deep neural network for semi-supervised semantic segmentation
Seunghoon Hong, Hyeonwoo Noh, and Bohyung Han · 2015
Cited alongside, same era.
Siamese neural networks for one-shot image recognition
Gregory Koch · 2015
Cited alongside, same era.
Human-level concept learning through probabilistic program induction
Brenden M Lake, Ruslan Salakhutdinov, and Joshua B Tenenbaum · 2015
Learning feed-forward one-shot learners
Luca Bertinetto, João F Henriques, Jack Valmadre, Philip Torr, and Andrea Vedaldi · 2016
Later among the works it cites.
Low-shot visual object recognition
Bharath Hariharan and Ross B. Girshick · 2016
Later among the works it cites.
Learning transferrable knowledge for semantic segmentation with deep convolutional neural network
Seunghoon Hong, Junhyuk Oh, Honglak Lee, and Bohyung Han · 2016
Later among the works it cites.
What makes imagenet good for transfer learning?
Minyoung Huh, Pulkit Agrawal, and Alexei A Efros · 2016
Later among the works it cites.
Image question answering using convolutional neural network with dynamic parameter prediction
Hyeonwoo Noh, Paul Hongsuck Seo, and Bohyung Han · 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.
Fully convolutional networks for semantic segmentation
Jonathan Long, Evan Shelhamer, and Trevor Darrell · 2015
Cited alongside, same era.
Feedforward semantic segmentation with zoom-out features
Mohammadreza Mostajabi, Payman Yadollahpour, and Gregory Shakhnarovich · 2015
Cited alongside, same era.
Learning deconvolution network for semantic segmentation
Hyeonwoo Noh, Seunghoon Hong, and Bohyung Han · 2015
Cited alongside, same era.
Weakly-and semi-supervised learning of a dcnn for semantic image segmentation
George Papandreou, Liang-Chieh Chen, Kevin Murphy, and Alan L Yuille · 2015
Cited alongside, same era.
Constrained convolutional neural networks for weakly supervised segmentation
Deepak Pathak, Philipp Krahenbuhl, and Trevor Darrell · 2015
Cited alongside, same era.
Weakly supervised semantic segmentation with convolutional networks
Pedro O Pinheiro and Ronan Collobert · 2015
Cited alongside, same era.
Object co-segmentation via graph optimized-flexible manifold ranking
Rong Quan, Junwei Han, Dingwen Zhang, and Feiping Nie · 2016
Later among the works it cites.
Meta-learning with memory-augmented neural networks
Adam Santoro, Sergey Bartunov, Matthew Botvinick, Daan Wierstra, and Timothy Lillicrap · 2016
Later among the works it cites.
Matching networks for one shot learning
Oriol Vinyals, Charles Blundell, Tim Lillicrap, Daan Wierstra, et al · 2016
Later among the works it cites.
Learning to learn: Model regression networks for easy small sample learning
Yu-Xiong Wang and Martial Hebert · 2016
Later among the works it cites.
One-shot video object segmentation
Sergi Caelles, Kevis-Kokitsi Maninis, Jordi Pont-Tuset, Laura Leal-Taixé, Daniel Cremers, and Luc Van Gool · 2017
Closest in time.
Learning to remember rare events
Łukasz Kaiser, Ofir Nachum, Aurko Roy, and Samy Bengio · 2017
Closest in time.
Enriching visual knowledge bases via object discovery and segmentation
Xinlei Chen, Abhinav Shrivastava, and Abhinav Gupta · 2034
Closest in time.