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Learning from Narrated Instruction Videos

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21730%2F18%3A00318985" target="_blank" >RIV/68407700:21730/18:00318985 - isvavai.cz</a>

  • Result on the web

    <a href="http://dx.doi.org/10.1109/TPAMI.2017.2749223" target="_blank" >http://dx.doi.org/10.1109/TPAMI.2017.2749223</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1109/TPAMI.2017.2749223" target="_blank" >10.1109/TPAMI.2017.2749223</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Learning from Narrated Instruction Videos

  • Original language description

    Automatic assistants could guide a person or a robot in performing new tasks, such as changing a car tire or repotting a plant. Creating such assistants, however, is non-trivial and requires understanding of visual and verbal content of a video. Towards this goal, we here address the problem of automatically learning the main steps of a task from a set of narrated instruction videos. We develop a new unsupervised learning approach that takes advantage of the complementary nature of the input video and the associated narration. The method sequentially clusters textual and visual representations of a task, where the two clustering problems are linked by joint constraints to obtain a single coherent sequence of steps in both modalities. To evaluate our method, we collect and annotate a new challenging dataset of real-world instruction videos from the Internet. The dataset contains videos for five different tasks with complex interactions between people and objects, captured in a variety of indoor and outdoor settings. We experimentally demonstrate that the proposed method can automatically discover, learn and localize the main steps of a task input videos.

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • 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

    2018

  • Confidentiality

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

Data specific for result type

  • Name of the periodical

    IEEE Transactions on Pattern Analysis and Machine Intelligence

  • ISSN

    0162-8828

  • e-ISSN

    1939-3539

  • Volume of the periodical

    40

  • Issue of the periodical within the volume

    9

  • Country of publishing house

    US - UNITED STATES

  • Number of pages

    15

  • Pages from-to

    2194-2208

  • UT code for WoS article

    000440868400012

  • EID of the result in the Scopus database

    2-s2.0-85029154712