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- Christoph H. Lampert, Hannes Nickisch, Stefan Harmeling
- 2009 IEEE Conference on Computer Vision and…
- 2009
We study the problem of object classification when training and test classes are disjoint, i.e. no training examples of the target classes are available. This setup has hardly been studied in… (More)
- Christoph H. Lampert, Hannes Nickisch, Stefan Harmeling
- IEEE Transactions on Pattern Analysis and Machine…
- 2014
We study the problem of object recognition for categories for which we have no training examples, a task also called zero--data or zero-shot learning. This situation has hardly been studied in… (More)
- Christoph H. Lampert, Matthew B. Blaschko, Thomas Hofmann
- 2008 IEEE Conference on Computer Vision and…
- 2008
Most successful object recognition systems rely on binary classification, deciding only if an object is present or not, but not providing information on the actual object location. To perform… (More)
- Christoph H. Lampert, Matthew B. Blaschko, Thomas Hofmann
- IEEE Transactions on Pattern Analysis and Machine…
- 2009
Most successful object recognition systems rely on binary classification, deciding only if an object is present or not, but not providing information on the actual object location. To estimate the… (More)
- Matthew B. Blaschko, Christoph H. Lampert
- ECCV
- 2008
Sliding window classifiers are among the most successful and widely applied techniques for object localization. However, training is typically done in a way that is not specific to the localization… (More)
- Alexander Kolesnikov, Christoph H. Lampert
- ECCV
- 2016
c∈T ∑ u∈Sc log fu,c(X), where Sc – set of locations, which were identified as a class c ∈ C by the weak localization procedure. 2 Expansion principle • Expansion loss incorporates a prior knowledge… (More)
- Sebastian Nowozin, Christoph H. Lampert
- 2011
Powerful statistical models that can be learned efficiently from large amounts of data are currently revolutionizing computer vision. These models possess a rich internal structure reflecting… (More)
- Tinne Tuytelaars, Christoph H. Lampert, Matthew B. Blaschko, Wray L. Buntine
- International Journal of Computer Vision
- 2009
The goal of this paper is to evaluate and compare models and methods for learning to recognize basic entities in images in an unsupervised setting. In other words, we want to discover the objects… (More)
- Yongqin Xian, Christoph H. Lampert, Bernt Schiele, Zeynep Akata
- IEEE transactions on pattern analysis and machine…
- 2018
Due to the importance of zero-shot learning, i.e. classifying images where there is a lack of labeled training data, the number of proposed approaches has recently increased steadily. We argue that… (More)