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

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08422.RodnerErik.ExtAbstract.1861.pdf (0.1 MB)

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: Object detection, one-shot learning, knowledge transfer
Collection: 08422 - Klausurtagung Lehrstuhl Joachim Denzler
Issue Date: 2009
Date of publication: 29.01.2009

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