Fetching the paper…
Reading the bibliography…
Learning to localize objects with minimal supervision is an important problem in computer vision, since large fully annotated datasets are extremely costly to obtain.
The estimation of the gradient of a density function, with applications in pattern recognition
Fukunaga, K. and Hostetler, L · 1975
Earlier work this paper cites.
An analysis of the greedy algorithm for the submodular set covering problem
Wolsey, L · 1982
Earlier work this paper cites.
Segmentation by minimal description
Darrell, T., Sclaroff, S., and Pentland, A · 1990
Earlier work this paper cites.
PAC learning axis aligned rectangles with respect to product distributions from multiple-instance examples
Long, P.M. and Tan, L · 1996
Earlier work this paper cites.
Numerical Optimization
Nocedal, J. and Wright, S · 1999
Earlier work this paper cites.
Support vector machines for multiple-instance learning
Andrews, S, Tsochantaridis, I, and Hofmann, T · 2003
Earlier work this paper cites.
Yuille, A.L. and Rangarajan, A · 2003
Earlier work this paper cites.
Convex Optimization
Boyd, S. P. and Vandenberghe, L · 2004
Earlier work this paper cites.
Combined object categorization and segmentation with an implicit chape model
Leibe, B., Leonardis, A., and Schiele, B · 2004
Earlier work this paper cites.
Smooth minimization of non-smooth functions
Nesterov, Y · 2005
Earlier work this paper cites.
Weakly supervised learning of part-based spatial models for visual object recognition
Crandall, D. and Huttenlocher, D · 2006
Earlier work this paper cites.
Multiple object class detection with a generative model
Micolajczyk, K., Leibe, G., and Schiele, B · 2006
Earlier work this paper cites.
Cosegmentation of image pairs by histogram matching incorporating a global constraint into MRFs
Rother, C., Minka, T., Blake, A., and Kolmogorov, V · 2006
Earlier work this paper cites.
An exemplar model for learning object classes
Chum, O. and Zisserman, A · 2007
Earlier work this paper cites.
The PASCAL Visual Object Classes Challenge 2007 (VOC2007) Results
Everingham, M., Van Gool, L., Williams, C. K. I., Winn, J., and Zisserman, A · 2007
Earlier work this paper cites.
Weakly supervised scale-invariant learning of models for visual recognition
Fergus, R., Perona, P., and Zisserman, A · 2007
Cited alongside, same era.
Weakly supervised object localization with stable segmentations
Galleguillos, C., Babenko, B., Rabinovich, A., and Belongie, S · 2008
Cited alongside, same era.
Learning structural svms with latent variables
Yu, C.N. and Joachims, T · 2009
Cited alongside, same era.
Classcut for unsupervised class segmentation
Alexe, B., Deselaers, T., and Ferrari, V · 2010
Cited alongside, same era.
Localizing objects while learning their appearance
Deselaers, T., Alex, B., and Ferrari, V · 2010
Cited alongside, same era.
The PASCAL Visual Object Classes (VOC) Challenge
Everingham, M., Van Gool, L., Williams, C. K. I., Winn, J., and Zisserman, A · 2010
Cited alongside, same era.
A convex relaxation for weakly supervised classifiers
Joulin, A. and Bach, F · 2012
Later among the works it cites.
Modeling latent variable uncertainty for loss-based learning
Kumar, P, Packer, B, and Koller, D · 2012
Later among the works it cites.
Discovering discriminative action parts from mid-level video representations
Raptis, M., Kokkinos, I., and Soatto, S · 2012
Later among the works it cites.
Object-centric spatial pooling for image classification
Russakovsky, O., Lin, Y., Yu, K., and Fei Fei, L · 2012
Later among the works it cites.
Unsupervised discovery of mid-level discriminative patches
Singh, S., Gupta, A., and Efros, A · 2012
Later among the works it cites.
In defence of negative mining for annotating weakly labelled data
Siva, P., Russell, C., and Xiang, T · 2012
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Object detection with discriminatively trained part based models
Felzenszwalb, P. F., Girshick, R. B., McAllester, D., and Ramanan, D · 2010
Cited alongside, same era.
Discriminative clustering for image co-segmentation
Joulin, A., Bach, F., and Ponce, J · 2010
Cited alongside, same era.
Distributed cosegmentation via submodular optimization on anisotropic diffusion
Kim, G., Xing, E.P., Fei-Fei, L., and Kanade, T · 2011
Cited alongside, same era.
Scene recognition and weakly supervised object localization with deformable part-based models
Pandey, M. and Lazebnik, S · 2011
Cited alongside, same era.
Weakly supervised object detector learning with model drift detection
Siva, P. and Xiang, T · 2011
Cited alongside, same era.
Optimization with sparsity-inducing penalties
Bach, F., Jenatton, R., Mairal, J., and Obozinski, G · 2012
Cited alongside, same era.
Neil: Extracting visual knowledge from web data
Chen, X., Shrivastava, A., and and, A. Gupta · 2013
Later among the works it cites.
Mid-level visual element discovery as discriminative mode seeking
Doersch, C., Gupta, A., and Efros, A · 2013
Later among the works it cites.
Learning collections of part models for object recognition
Endres, I., Shih, K., and Hoeim, D · 2013
Later among the works it cites.
Blocks that shout: Distinctive parts for scene classification
Juneja, M., Vedaldi, A., Jawahar, V., and Zisserman, A · 2013
Later among the works it cites.
Convex and scalable weakly labeled svms
Li, Y., Tsang, I., Kwok, J., and Zhou, Z · 2013
Later among the works it cites.
Selective search for object recognition
Uijlings, J., van de Sande, K., Gevers, T., and Smeulders, A · 2013
Later among the works it cites.
Active detection via adaptive submodularity
Chen, Y., Shioi, H., Montesinos, C. Fuentes, Koh, L. P., Wich, S., and Krause, A · 2014
Closest in time.
DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition
Donahue, J., Jia, Y., Vinyals, O., Hoffman, J., Zhang, N., Tzeng, E., and Darrell, T · 2014
Closest in time.
Rich feature hierarchies for accurate object detection and semantic segmentation
Girshick, R., Donahue, J., Darrell, T., and Malik, J · 2014
Closest in time.