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Segmenting out Generic Objects in Monocular Videos

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61988987%3A17610%2F21%3AA2302APR" target="_blank" >RIV/61988987:17610/21:A2302APR - isvavai.cz</a>

  • Result on the web

    <a href="http://ceur-ws.org/Vol-2962/paper49.pdf" target="_blank" >http://ceur-ws.org/Vol-2962/paper49.pdf</a>

  • DOI - Digital Object Identifier

Alternative languages

  • Result language

    angličtina

  • Original language name

    Segmenting out Generic Objects in Monocular Videos

  • Original language description

    We present an approach for generic object detection and segmentation in monocular videos. In this task, we want to segment objects from a background with no prior knowledge about the possible classes of objects which we may encounter. This makes this task much harder than the classical object detection and segmentation, which can be posed as a supervised learning problem. Our approach uses an ensemble of 3 different models which are trained by different objectives and have different failure modes and therefore complement each other. We demonstrate the usefulness of our approach on a custom dataset containing 18 classes of organic objects. Using our method, we were able to recover the classes of objects in a fully unsupervised way.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

    10102 - Applied mathematics

Result continuities

  • Project

  • Continuities

    S - Specificky vyzkum na vysokych skolach

Others

  • Publication year

    2021

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Article name in the collection

    Proceedings of the 21st Conference Information Technologies ? Applications and Theory (ITAT 2021)

  • ISBN

  • ISSN

    1613-0073

  • e-ISSN

  • Number of pages

    7

  • Pages from-to

    123-129

  • Publisher name

    CEUR Workshop Proceedings

  • Place of publication

  • Event location

    Nízke Tatry and Muránska planina, Slovakia

  • Event date

    Jan 1, 2021

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article