Fetching the paper…
Reading the bibliography…
Weakly-supervised object localization methods tend to fail for object classes that consistently co-occur with the same background elements, e.g.
An iteration method for the solution of the eigenvalue problem of linear differential and integral operators
Cornelius Lanczos · 1950
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.
Weakly supervised top-down image segmentation
Manuela Vasconcelos, Nuno Vasconcelos, and Gustavo Carneiro · 2006
Earlier work this paper cites.
Active learning with gaussian processes for object categorization
Ashish Kapoor, Kristen Grauman, Raquel Urtasun, and Trevor Darrell · 2007
Earlier work this paper cites.
Region classification with Markov field aspect models
Jakob Verbeek and Bill Triggs · 2007
Earlier work this paper cites.
A tutorial on spectral clustering
Ulrike Von Luxburg · 2007
Earlier work this paper cites.
Two-dimensional active learning for image classification
Guo-Jun Qi, Xian-Sheng Hua, Yong Rui, Jinhui Tang, and Hong-Jiang Zhang · 2008
Earlier work this paper cites.
Multi-class active learning for image classification
Ajay J. Joshi, Fatih Porikli, and Nikolaos Papanikolopoulos · 2009
Earlier work this paper cites.
Active learning literature survey
Burr Settles · 2009
Earlier work this paper cites.
Visual recognition with humans in the loop
Steve Branson, Catherine Wah, Florian Schroff, Boris Babenko, Peter Welinder, Pietro Perona, and Serge Belongie · 2010
Earlier work this paper cites.
Localizing objects while learning their appearance
Thomas Deselaers, Bogdan Alexe, and Vittorio Ferrari · 2010
Earlier work this paper cites.
The PASCAL visual object classes (VOC) challenge
Mark Everingham, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman · 2010
Earlier work this paper cites.
Towards weakly supervised semantic segmentation by means of multiple instance and multitask learning
Alexander Vezhnevets and Joachim M. Buhmann · 2010
Earlier work this paper cites.
Semantic contours from inverse detectors
Bharath Hariharan, Pablo Arbelaez, Lubomir Bourdev, Subhransu Maji, and Jitendra Malik · 2011
Cited alongside, same era.
Weakly supervised semantic segmentation with a multi-image model
Alexander Vezhnevets, Vittorio Ferrari, and Joachim M. Buhmann · 2011
Cited alongside, same era.
Multiclass recognition and part localization with humans in the loop
Catherine Wah, Steve Branson, Pietro Perona, and Serge Belongie · 2011
Cited alongside, same era.
Building high-level features using large scale unsupervised learning
Quoc V. Le, Marc A. Ranzato, Rajat Monga, Matthieu Devin, Kai Chen, Greg S. Corrado, Jeffrey Dean, and Andrew Ng · 2012
Cited alongside, same era.
Weakly supervised structured output learning for semantic segmentation
Alexander Vezhnevets, Vittorio Ferrari, and Joachim M. Buhmann · 2012
Cited alongside, same era.
Fine-grained crowdsourcing for fine-grained recognition
Weakly- and semi-supervised learning of a deep convolutional network for semantic image segmentation
George Papandreou, Liang-Chieh Chen, Kevin P. Murphy, and Alan L. Yuille · 2015
Later among the works it cites.
From image-level to pixel-level labeling with convolutional networks
Pedro O. Pinheiro and Ronan Collobert · 2015
Later among the works it cites.
ImageNet Large Scale Visual Recognition Challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, and Li Fei-Fei · 2015
Later among the works it cites.
Neural activation constellations: Unsupervised part model discovery with convolutional networks
Marcel Simon and Erik Rodner · 2015
Later among the works it cites.
Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2015
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Jia Deng, Jonathan Krause, and Li Fei-Fei · 2013
Cited alongside, same era.
Weakly supervised object detection with posterior regularization
Hakan Bilen, Marco Pedersoli, and Tinne Tuytelaars · 2014
Cited alongside, same era.
Multi-fold MIL training for weakly supervised object localization
Ramazan Gokberk Cinbis, Jakob Verbeek, and Cordelia Schmid · 2014
Cited alongside, same era.
Selecting influential examples: Active learning with expected model output changes
Alexander Freytag, Erik Rodner, and Joachim Denzler · 2014
Cited alongside, same era.
Caffe: Convolutional architecture for fast feature embedding
Yangqing Jia, Evan Shelhamer, Jeff Donahue, Sergey Karayev, Jonathan Long, Ross Girshick, Sergio Guadarrama, and Trevor Darrell · 2014
Cited alongside, same era.
Similarity comparisons for interactive fine-grained categorization
Catherine Wah, Grant Horn, Steve Branson, Subhransu Maji, Pietro Perona, and Serge Belongie · 2014
Cited alongside, same era.
Tell me what you see and I will show you where it is
Jia Xu, Alexander G. Schwing, and Raquel Urtasun · 2014
Cited alongside, same era.
Large-scale weakly supervised object localization via latent category learning
Chong Wang, Kaiqi Huang, Weiqiang Ren, Junge Zhang, and Steve Maybank · 2015
Later among the works it cites.
Learning to segment under various forms of weak supervision
Jia Xu, Alexander G. Schwing, and Raquel Urtasun · 2015
Later among the works it cites.
Weakly supervised semantic segmentation for social images
Wei Zhang, Sheng Zeng, Dequan Wang, and Xiangyang Xue · 2015
Later among the works it cites.
Object detectors emerge in deep scene CNNs
Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, and Antonio Torralba · 2015
Later among the works it cites.
Self-taught object localization with deep networks
Loris Bazzani, Alessandro Bergamo, Dragomir Anguelov, and Lorenzo Torresani · 2016
Closest in time.
Seed, expand and constrain: Three principles for weakly-supervised image segmentation
Alexander Kolesnikov and Christoph H. Lampert · 2016
Closest in time.
We don’t need no bounding-boxes: Training object class detectors using only human verification
Dim P. Papadopoulos, Jasper R.R. Uijlings, Frank Keller, and Vittorio Ferrari · 2016
Closest in time.
Learning deep features for discriminative localization
Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, and Antonio Torralba · 2016
Closest in time.