Publications related to Learning to Detect Objects with Minimal Supervision

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Keypoint Signatures for Fast Learning and Recognition

Pascal Fua, Vincent Lepetit, Michael Calonder

Statistical learning techniques have been used to dramatically speed-up keypoint matching by training a classifier to recognize a specific set of keypoints. However, the training itself is usually relatively slow and performed offline. Although methods hav ...

2008

Keypoint Signatures for Fast Learning and Recognition

Pascal Fua, Vincent Lepetit, Michael Calonder

Springer-Verlag New York, Ms Ingrid Cunningham, 175 Fifth Ave, New York, Ny 10010 Usa2008

Combination of Fixed and Mobile Cameras for Automatic Pedestrian Detection

Michel Bierlaire, Murat Kunt, Alexandre Massoud Alahi

Pedestrian detection in the surroundings of a vehicle is highly desirable to avoid dangerous traffic situations. Typical vision-based pedestrian detection algorithms on mobile cameras suffer from the lack of a-priori knowledge on the object to be detected. ...

2008

Cross-Validation Optimization for Large Scale Structured Classification Kernel Methods

Matthias Seeger

We propose a highly efficient framework for penalized likelihood kernel methods applied to multi-class models with a large, structured set of classes. As opposed to many previous approaches which try to decompose the fitting problem into many smaller ones, ...

Massachusetts Institute of Technology Press2008

Iterative Learning Control based on Stochastic Approximation

Alireza Karimi, Roland Longchamp, Mark Edward John Butcher

In this paper stochastic approximation theory is used to produce Iterative Learning Control algorithms which are less sensitive to stochastic disturbances, a typical problem for the learning process of standard ILC algorithms. Two algorithms are developed, ...

2008

Principled Detection-by-Classification from Multiple Views

Pascal Fua, François Fleuret, Jérôme Berclaz

Machine-learning based classification techniques have been shown to be effective at detecting objects in complex scenes. However, the final results are often obtained from the alarms produced by the classifiers through a post-processing which typically rel ...

2008

Principled Detection-by-classification from Multiple Views

Pascal Fua, François Fleuret, Jérôme Berclaz

Machine-learning based classification techniques have been shown to be effective at detecting objects in com- plex scenes. However, the final results are often obtained from the alarms produced by the classifiers through a post-processing which typically r ...

2008

Learning sparse generative models of audiovisual signals

Pierre Vandergheynst, Gianluca Monaci

This paper presents a novel framework to learn sparse represen- tations for audiovisual signals. An audiovisual signal is modeled as a sparse sum of audiovisual kernels. The kernels are bimodal functions made of synchronous audio and video components that ...

2008

Learning to Detect Objects with Minimal Supervision

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Keypoint Signatures for Fast Learning and Recognition

Composite Kernel Learning

Composite Kernel Learning

Keypoint Signatures for Fast Learning and Recognition

Combination of Fixed and Mobile Cameras for Automatic Pedestrian Detection

Cross-Validation Optimization for Large Scale Structured Classification Kernel Methods

Iterative Learning Control based on Stochastic Approximation

Principled Detection-by-Classification from Multiple Views

Principled Detection-by-classification from Multiple Views

Learning sparse generative models of audiovisual signals

Keypoint Signatures for Fast Learning and Recognition

Cross-Validation Optimization for Large Scale Structured Classification Kernel Methods

Principled Detection-by-classification from Multiple Views

Principled Detection-by-Classification from Multiple Views

Learning sparse generative models of audiovisual signals

Composite Kernel Learning

Keypoint Signatures for Fast Learning and Recognition

Composite Kernel Learning

Iterative Learning Control based on Stochastic Approximation

Combination of Fixed and Mobile Cameras for Automatic Pedestrian Detection