Christoph H. Lampert

Publications155
h-index38
Citations9,589
Highly Influential Citations1,423
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  • Publications
  • Influence
Learning to detect unseen object classes by between-class attribute transfer
TLDR
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. Expand
  • 1,741
  • 311
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Attribute-Based Classification for Zero-Shot Visual Object Categorization
TLDR
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. Expand
  • 996
  • 236
  • PDF
iCaRL: Incremental Classifier and Representation Learning
TLDR
We introduce a new training strategy, iCaRL, that allows learning in such a class-incremental way: only the training data for a small number of classes has to be present at the same time. Expand
  • 709
  • 206
  • PDF
Zero-Shot Learning—A Comprehensive Evaluation of the Good, the Bad and the Ugly
TLDR
We compare and analyze a significant number of the state-of-the-art methods in depth, both in the classic zero-shot setting but also in the more realistic generalized zero- shot setting. Expand
  • 479
  • 145
  • PDF
Beyond sliding windows: Object localization by efficient subwindow search
TLDR
We propose a simple yet powerful branch-and-bound scheme that allows efficient maximization of a large class of classifier functions over all possible subimages. Expand
  • 797
  • 82
  • PDF
Seed, Expand and Constrain: Three Principles for Weakly-Supervised Image Segmentation
TLDR
We introduce a new loss function for the weakly-supervised training of semantic image segmentation models based on three guiding principles: to seed with weak localization cues, to expand objects based on the information about which classes can occur in an image, and to constrain the segmentations to coincide with object boundaries. Expand
  • 338
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Efficient Subwindow Search: A Branch and Bound Framework for Object Localization
TLDR
We propose Efficient Subwindow Search (ESS), a method for object localization that does not suffer from these drawbacks. Expand
  • 402
  • 52
  • PDF
Learning to Localize Objects with Structured Output Regression
TLDR
We model object localization in a principled way by posing it as a problem of predicting structured data: we model the problem not as binary classification, but as the prediction of the bounding box. Expand
  • 363
  • 35
  • PDF
Structured Learning and Prediction in Computer Vision
TLDR
This monograph introduces the reader to the most popular classes of structured models in computer vision. Expand
  • 316
  • 26
  • PDF
Unsupervised Object Discovery: A Comparison
TLDR
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. Expand
  • 215
  • 19
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