Theory of Learning with Few Examples and Object Localization

Rodner, Erik; Denzler, Joachim

doi:10.4230/DagSemProc.08422.9

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Theory of Learning with Few Examples and Object Localization

Authors Erik Rodner, Joachim Denzler

Part of: Volume: Dagstuhl Seminar Proceedings, Volume 8422
Part of: Series: Dagstuhl Seminar Proceedings (DagSemProc)
License: Creative Commons Attribution 4.0 International license
Publication Date: 2009-01-29

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DOI: 10.4230/DagSemProc.08422.9
URN: urn:nbn:de:0030-drops-18613

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Keywords

Object detection
one-shot learning
knowledge transfer

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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.

Cite As Get BibTex

Erik Rodner and Joachim Denzler. Theory of Learning with Few Examples and Object Localization. In Computer Vision in Camera Networks for Analyzing Complex Dynamic Natural Scenes. Dagstuhl Seminar Proceedings, Volume 8422, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2009) https://doi.org/10.4230/DagSemProc.08422.9

Author Details

Erik Rodner

Joachim Denzler

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