Learning and Recognition in Vision
LEAR is a joint team of INRIA Grenoble
- RhôneAlpes and the LJK
laboratory, a joint research unit of the Centre National de Recherche
Scientifique (CNRS), the Institut National Polytechnique de Grenoble (INPG),
the Université Joseph Fourier (UJF) and Université Pierre-Mendès-France
(UPMF).
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LEAR's main focus is learning based approaches to visual object recognition
and scene interpretation, particularly for object category detection, image
retrieval, video indexing and the analysis of humans and their movements.
Understanding the content of everyday images and videos is one of the fundamental
challenges of computer vision and we believe that significant advances will
be made over the next few years by combining state of the art image analysis
tools with emerging machine learning and statistical modeling techniques.
For more information see our research description page, and annual reports of 2012,2011,2010, 2009, 2008, 2007, 2006.
Highlights
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Cordelia Schmid was awarded one of the ERC advanced grants 2012. Congratulations!
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LEAR participated together with the AXES project to the TRECVID MED challenge, and finished first and second.
The Multimedia Event Detection (MED) evaluation track is part of the TRECVID Evaluation. The goal of MED is to assemble core detection technologies into a system that can search multimedia recordings for user-defined events based on pre-computed metadata.
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The third INRIA Visual Recognition and Machine Learning Summer School took place at the INRIA Grenoble campus, from July 9 to July 13, 2012.
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Our recent publications in the major computer vision conferences:
5 CVPR'12 papers, 2 ECCV'12 papers, and 1 BMVC'12 papers. See our publication page for downloads.
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2011
2010
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We co-organized the workshop on machine learning for next generation computer vision challenges
at NIPS 2010, December 10, Whistler BC, Canada. Papers and slides of the talks are now available online.
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In the PASCAL VOC 2010 our work on human action recognition achieved best results on three out of nine action classes.
In the ECCV'10 International Workshop on Sign, Gesture, and Activity, our paper Human Focused Action Localization in Video was awarded the best paper prize.
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For the Photo Annotation task of ImageClef 2010 our joint submissions with Xerox Research Centre Europe have achieved best results on 56 of the 93 annotation concepts.
For 88 concepts, our runs were among the 6 best runs out of the 63 submitted ones. See this paper for details.
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The first INRIA Visual Recognition and Machine Learning Summer School took place at our institute in Grenoble, from July 26 to July 30. The school had 150 international attendees. Most lecture slides are now available.
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Our recent publications in the major computer vision conferences:
5 CVPR'10 papers (2 orals), 2 ECCV'10 papers, and 1 BMVC'10 paper. See our publication page for details. |
2009
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For both the Photo Annotation and Image Retrieval tasks of ImageCLEF'09 Lear obtained a second place among the 19 participating teams for each task.
The methods that were used are described in this paper.
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Recent publications in major computer vision conferences:
4 ICCV'09 papers (2 orals), 3 BMVC'09 papers (2 orals), and 3 CVPR'09 papers. See publications web page for details. |
2008
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Lear got excellent results on Trecvid 2008.
The method used is described in this paper.
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In the PASCAL VOC 2008 Lear won
the detection contest for 11 out of 20 classes (see example detections here)
and the classification contest for 7 out of 20 classes. |
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Recent publications in the major computer vision conferences:
4 ECCV'08 and 4 CVPR'08 papers. See publications web page for details. |
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Development of an image indexing system that
searches in real time for similar images in very large databases.
It is currently transferred and tested by the Start-Up
MilPix.
Our image search demo on 10,000,000 images: Bigimbaz.
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Organization of an International
Workshop on Object Recognition,
Como, May 2008. |
2007
