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A new semi-supervised method improving optical flow on distant domains

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F20%3A00338252" target="_blank" >RIV/68407700:21230/20:00338252 - isvavai.cz</a>

  • Result on the web

    <a href="https://data.vicos.si/cvww20/22.pdf" target="_blank" >https://data.vicos.si/cvww20/22.pdf</a>

  • DOI - Digital Object Identifier

Alternative languages

  • Result language

    angličtina

  • Original language name

    A new semi-supervised method improving optical flow on distant domains

  • Original language description

    We propose a semi-supervised approach to learning by formulating the optimization as constrained gradient descent on a loss function that includes unsupervised terms. The method is demonstrated on semi-supervised optical flow training that promotes photo-consistency and smoothness of the flow. We show that the unsupervised objective significantly improves the estimation on a distant domain while maintaining the performance on the original domain. As a result, we achieve state-of-the-art results on the Creative Flow+ dataset among CNN based methods that did not train on any samples from the dataset.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Result continuities

  • Project

  • Continuities

    I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Others

  • Publication year

    2020

  • 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 25th Computer Vision Winter Workshop Conference February 3-5, 2020, Rogaška Slatina, Slovenia

  • ISBN

    978-961-90901-9-0

  • ISSN

  • e-ISSN

  • Number of pages

    9

  • Pages from-to

    37-45

  • Publisher name

    Slovenian Pattern Recognition Society

  • Place of publication

    Ljubljana

  • Event location

    Rogaška Slatina

  • Event date

    Feb 3, 2020

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article