Fetching the paper…
Reading the bibliography…
We develop methods for detector learning which exploit joint training over both weak and strong labels and which transfer learned perceptual representations from strongly-labeled auxiliary tasks.
Backpropagation applied to handwritten zip code recognition
Y. LeCun, B. Boser, J. Denker, D. Henderson, R. Howard, W. Hubbard, and L. Jackel · 1989
Earlier work this paper cites.
Solving the multiple instance problem with axis-parallel rectangles
T. G. Dietterich, R. H. Lathrop, and T. Lozano-Pérez · 1997
Earlier work this paper cites.
Support vector machines for multiple-instance learning
S. Andrews, I. Tsochantaridis, and T. Hofmann · 2002
Earlier work this paper cites.
The concave-convex procedure
A. L. Yuille and A. Rangarajan · 2003
Earlier work this paper cites.
Multiple instance boosting for object detection
C. Zhang, J. C. Platt, and P. A. Viola · 2005
Earlier work this paper cites.
Frustratingly easy domain adaptation
H. Daumé III · 2007
Earlier work this paper cites.
Cross-domain video concept detection using adaptive svms
J. Yang, R. Yan, and A. G. Hauptmann · 2007
Earlier work this paper cites.
Curriculum learning
Y. Bengio, J. Louradour, R. Collobert, and J. Weston · 2009
Earlier work this paper cites.
Learning structural svms with latent variables
C.-N. J. Yu and T. Joachims · 2009
Earlier work this paper cites.
What is an object?
B. Alexe, T. Deselaers, and V. Ferrari · 2010
Earlier work this paper cites.
Object detection with discriminatively trained part-based models
P. F. Felzenszwalb, R. B. Girshick, D. McAllester, and D. Ramanan · 2010
Earlier work this paper cites.
Self-paced learning for latent variable models
M. P. Kumar, B. Packer, and D. Koller · 2010
Earlier work this paper cites.
Adapting visual category models to new domains
K. Saenko, B. Kulis, M. Fritz, and T. Darrell · 2010
Earlier work this paper cites.
Tabula rasa: Model transfer for object category detection
Y. Aytar and A. Zisserman · 2011
Earlier work this paper cites.
What you saw is not what you get: Domain adaptation using asymmetric kernel transforms
B. Kulis, K. Saenko, and T. Darrell · 2011
Cited alongside, same era.
Scene recognition and weakly supervised object localization with deformable part-based models
M. Pandey and S. Lazebnik · 2011
Cited alongside, same era.
Weakly supervised localization and learning with generic knowledge
T. Deselaers, B. Alexe, and V. Ferrari · 2012
Cited alongside, same era.
Learning with augmented features for heterogeneous domain adaptation
L. Duan, D. Xu, and I. W. Tsang · 2012
Cited alongside, same era.
Geodesic flow kernel for unsupervised domain adaptation
B. Gong, Y. Shi, F. Sha, and K. Grauman · 2012
Cited alongside, same era.
ImageNet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
Looking beyond the image: Unsupervised learning for object saliency and detection
P. Siva, C. Russell, T. Xiang, and L. Agapito · 2013
Later among the works it cites.
Visualizing and Understanding Convolutional Networks
M. Zeiler and R. Fergus · 2013
Later among the works it cites.
Confidence-rated multiple instance boosting for object detection
K. Ali and K. Saenko · 2014
Closest in time.
Object and action classification with latent window parameters
H. Bilen, V. P. Namboodiri, and L. J. Van Gool · 2014
Closest in time.
Multi-fold mil training for weakly supervised object localization
R. G. Cinbis, J. Verbeek, C. Schmid, et al · 2014
Closest in time.
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 · 2014
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Unsupervised discovery of mid-level discriminative patches
S. Singh, A. Gupta, and A. A. Efros · 2012
Cited alongside, same era.
In defence of negative mining for annotating weakly labelled data
P. Siva, C. Russell, and T. Xiang · 2012
Cited alongside, same era.
Large-scale visual sentiment ontology and detectors using adjective nown paiars
D. Borth, R. Ji, T. Chen, T. Breuel, and S. F. Chang · 2013
Cited alongside, same era.
Unsupervised visual domain adaptation using subspace alignment
B. Fernando, A. Habrard, M. Sebban, and T. Tuytelaars · 2013
Cited alongside, same era.
A tractable inference algorithm for diagnosing multiple diseases
D. Heckerman · 2013
Cited alongside, same era.
Efficient learning of domain-invariant image representations
J. Hoffman, E. Rodner, J. Donahue, K. Saenko, and T. Darrell · 2013
Cited alongside, same era.
Closest in time.
Rich feature hierarchies for accurate object detection and semantic segmentation
R. Girshick, J. Donahue, T. Darrell, and J. Malik · 2014
Closest in time.
LSDA: Large scale detection through adaptation
J. Hoffman, S. Guadarrama, E. Tzeng, R. Hu, J. Donahue, R. Girshick, T. Darrell, and K. Saenko · 2014
Closest in time.
Caffe: Convolutional architecture for fast feature embedding
Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell · 2014
Closest in time.
Imagenet large scale visual recognition challenge
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. K. amd Michael Bernstein, A. C. Berg, and L. Fei-Fe · 2014
Closest in time.
On learning to localize objects with minimal supervision
H. Song, R. Girshick, S. Jegelka, J. Mairal, Z. Harchaoui, and T. Darrell · 2014
Closest in time.
Weakly-supervised discovery of visual pattern configurations
H. O. Song, Y. J. Lee, S. Jegelka, and T. Darrell · 2014
Closest in time.
Weakly supervised object localization with latet category learning
C. Wang, W. Ren, K. Huang, and T. Tan · 2014
Closest in time.