171 citations · 694 across the 32 of their papers we have counts for
16 papers · 1 filter
Unsupervised Video Prediction from a Single Frame by Estimating 3D Dynamic Scene Structure
Paul Henderson, Christoph H. Lampert, Bernd Bickel
Our goal in this work is to generate realistic videos given just one initial frame as input. Existing unsupervised approaches to this task do not consider the fact that a video typ…
A Flexible Selection Scheme for Minimum-Effort Transfer Learning
Amelie Royer, Christoph H. Lampert
Fine-tuning is a popular way of exploiting knowledge contained in a pre-trained convolutional network for a new visual recognition task. However, the orthogonal setting of transfer…
Unsupervised object-centric video generation and decomposition in 3D
Paul Henderson, Christoph H. Lampert
A natural approach to generative modeling of videos is to represent them as a composition of moving objects. Recent works model a set of 2D sprites over a slowly-varying background…
Localizing Grouped Instances for Efficient Detection in Low-Resource Scenarios
Amelie Royer, Christoph H. Lampert
State-of-the-art detection systems are generally evaluated on their ability to exhaustively retrieve objects densely distributed in the image, across a wide variety of appearances…
Leveraging 2D Data to Learn Textured 3D Mesh Generation
Paul Henderson, Vagia Tsiminaki, Christoph H. Lampert
Numerous methods have been proposed for probabilistic generative modelling of 3D objects. However, none of these is able to produce textured objects, which renders them of limited…
Detecting Visual Relationships Using Box Attention
Alexander Kolesnikov, Alina Kuznetsova, Christoph H. Lampert +1
We propose a new model for detecting visual relationships, such as "person riding motorcycle" or "bottle on table". This task is an important step towards comprehensive structured…
Learning Intelligent Dialogs for Bounding Box Annotation
Ksenia Konyushkova, Jasper Uijlings, Christoph Lampert +1
We introduce Intelligent Annotation Dialogs for bounding box annotation. We train an agent to automatically choose a sequence of actions for a human annotator to produce a bounding…
Zero-Shot Learning -- A Comprehensive Evaluation of the Good, the Bad and the Ugly
Yongqin Xian, Christoph H. Lampert, Bernt Schiele +1
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 steadi…
Probabilistic Image Colorization
Amelie Royer, Alexander Kolesnikov, Christoph H. Lampert
We develop a probabilistic technique for colorizing grayscale natural images. In light of the intrinsic uncertainty of this task, the proposed probabilistic framework has numerous…
PixelCNN Models with Auxiliary Variables for Natural Image Modeling
Alexander Kolesnikov, Christoph H. Lampert
We study probabilistic models of natural images and extend the autoregressive family of PixelCNN architectures by incorporating auxiliary variables. Subsequently, we describe two n…
iCaRL: Incremental Classifier and Representation Learning
Sylvestre-Alvise Rebuffi, Alexander Kolesnikov, Georg Sperl +1
A major open problem on the road to artificial intelligence is the development of incrementally learning systems that learn about more and more concepts over time from a stream of…
Improving Weakly-Supervised Object Localization By Micro-Annotation
Alexander Kolesnikov, Christoph H. Lampert
Weakly-supervised object localization methods tend to fail for object classes that consistently co-occur with the same background elements, e.g. trains on tracks. We propose a meth…
Seed, Expand and Constrain: Three Principles for Weakly-Supervised Image Segmentation
Alexander Kolesnikov, Christoph H. Lampert
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…
Identifying Reliable Annotations for Large Scale Image Segmentation
Alexander Kolesnikov, Christoph H. Lampert
Challenging computer vision tasks, in particular semantic image segmentation, require large training sets of annotated images. While obtaining the actual images is often unproblema…
Learning to Transfer Privileged Information
Viktoriia Sharmanska, Novi Quadrianto, Christoph H. Lampert
We introduce a learning framework called learning using privileged information (LUPI) to the computer vision field. We focus on the prototypical computer vision problem of teaching…
Proceedings of The 38th Annual Workshop of the Austrian Association for Pattern Recognition (ÖAGM), 2014
Vladimir Kolmogorov, Christoph Lampert, Emilie Morvant +1
The 38th Annual Workshop of the Austrian Association for Pattern Recognition (ÖAGM) will be held at IST Austria, on May 22-23, 2014. The workshop provides a platform for researcher…