598 citations · 2.9k across the 89 of their papers we have counts for
12 papers · 1 filter
Loss Functions for Top-k Error: Analysis and Insights
Maksim Lapin, Matthias Hein, Bernt Schiele
In order to push the performance on realistic computer vision tasks, the number of classes in modern benchmark datasets has significantly increased in recent years. This increase i…
Weakly Supervised Object Boundaries
Anna Khoreva, Rodrigo Benenson, Mohamed Omran +2
State-of-the-art learning based boundary detection methods require extensive training data. Since labelling object boundaries is one of the most expensive types of annotations, the…
Top-k Multiclass SVM
Maksim Lapin, Matthias Hein, Bernt Schiele
Class ambiguity is typical in image classification problems with a large number of classes. When classes are difficult to discriminate, it makes sense to allow k guesses and evalua…
Efficient Output Kernel Learning for Multiple Tasks
Pratik Jawanpuria, Maksim Lapin, Matthias Hein +1
The paradigm of multi-task learning is that one can achieve better generalization by learning tasks jointly and thus exploiting the similarity between the tasks rather than learnin…
An Efficient Multilinear Optimization Framework for Hypergraph Matching
Quynh Nguyen, Francesco Tudisco, Antoine Gautier +1
Hypergraph matching has recently become a popular approach for solving correspondence problems in computer vision as it allows to integrate higher-order geometric information. Hype…
Robust PCA: Optimization of the Robust Reconstruction Error over the Stiefel Manifold
Anastasia Podosinnikova, Simon Setzer, Matthias Hein
It is well known that Principal Component Analysis (PCA) is strongly affected by outliers and a lot of effort has been put into robustification of PCA. In this paper we present a n…