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Detecting Unseen Visual Relations Using Analogies

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21730%2F19%3A00337253" target="_blank" >RIV/68407700:21730/19:00337253 - isvavai.cz</a>

  • Result on the web

    <a href="https://doi.org/10.1109/ICCV.2019.00207" target="_blank" >https://doi.org/10.1109/ICCV.2019.00207</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1109/ICCV.2019.00207" target="_blank" >10.1109/ICCV.2019.00207</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Detecting Unseen Visual Relations Using Analogies

  • Original language description

    We seek to detect visual relations in images of the form of tripletst= (subject, predicate, object), such as “person riding dog”, where training examples of the individual entities are available but their combinations are unseen at training. This is an important set-up due to the combinatorial nature of visual relations: collecting sufficient training data for all possible triplets would be very hard. The contributions of this work are three-fold. First, we learn a representation of visual relations that combines (i) individual embeddings for subject, object and predicate together with(ii) a visual phrase embedding that represents the relation triplets. Second, we learn how to transfer visual phrase em-beddings from existing training triplets to unseen test triplets using analogies between relations that involve similar ob-jects. Third, we demonstrate the benefits of our approach on three challenging datasets: on HICO-DET, our model achieves significant improvement over a strong baseline for both frequent and unseen triplets, and we observe similar improvement for the retrieval of unseen triplets with out-of-vocabulary predicates on the COCO-a dataset as well as the challenging unusual triplets in the UnRel 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

    <a href="/en/project/EF15_003%2F0000468" target="_blank" >EF15_003/0000468: Intelligent Machine Perception</a><br>

  • Continuities

    P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Others

  • Publication year

    2019

  • Confidentiality

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

Data specific for result type

  • Article name in the collection

    2019 IEEE International Conference on Computer Vision (ICCV 2019)

  • ISBN

    978-1-7281-4804-5

  • ISSN

    1550-5499

  • e-ISSN

    2380-7504

  • Number of pages

    10

  • Pages from-to

    1981-1990

  • Publisher name

    IEEE Computer Society Press

  • Place of publication

    Los Alamitos

  • Event location

    Seoul

  • Event date

    Oct 27, 2019

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article

    000531438102012