License:
Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/DagSemProc.08422.9
URN: urn:nbn:de:0030-drops-18613
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2009/1861/
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Rodner, Erik ;
Denzler, Joachim
Theory of Learning with Few Examples and Object Localization
Abstract
Visual object localization and categorization is still a big challenge
for current research and gets even more difficult when confronted with
few training examples. Therefore we will present a Bayesian concept to
enhance state-of-the-art machine learning techniques even when dealing with
just a single view of an object category.
Furthermore an object localization approach is presented, which can serve
as a baseline for researchers within the area of object localization.
BibTeX - Entry
@InProceedings{rodner_et_al:DagSemProc.08422.9,
author = {Rodner, Erik and Denzler, Joachim},
title = {{Theory of Learning with Few Examples and Object Localization}},
booktitle = {Computer Vision in Camera Networks for Analyzing Complex Dynamic Natural Scenes},
series = {Dagstuhl Seminar Proceedings (DagSemProc)},
ISSN = {1862-4405},
year = {2009},
volume = {8422},
editor = {Joachim Denzler and Michael Koch},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/opus/volltexte/2009/1861},
URN = {urn:nbn:de:0030-drops-18613},
doi = {10.4230/DagSemProc.08422.9},
annote = {Keywords: Object detection, one-shot learning, knowledge transfer}
}
Keywords: |
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Object detection, one-shot learning, knowledge transfer |
Collection: |
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08422 - Klausurtagung Lehrstuhl Joachim Denzler |
Issue Date: |
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2009 |
Date of publication: |
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29.01.2009 |