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Gated Contextual Features for Salient Object Detection

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F21%3A50017956" target="_blank" >RIV/62690094:18450/21:50017956 - isvavai.cz</a>

  • Result on the web

    <a href="https://ieeexplore.ieee.org/document/9371722" target="_blank" >https://ieeexplore.ieee.org/document/9371722</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1109/TIM.2021.3064423" target="_blank" >10.1109/TIM.2021.3064423</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Gated Contextual Features for Salient Object Detection

  • Original language description

    The effective extraction of local and contextual visual cues carrying information of different scales is crucial for accurate detection of the salient object(s) with varying shape, size, and location. The Atrous Spatial Pyramid Pooling (ASPP) and its dense versions are widely used for extracting contextual features for dense prediction tasks. The skip connections in densely or moderately connected ASPP directly propagate the context information from a parallel dilated convolution to the next higher-rate dilated convolution to combat the “gridding issue” in àtrous convolutions. The aggregated context from several scales may dilute features belonging to small objects or confuse between the salient object and the background. To emphasize invariance features for different scale visual patterns in an image, a gate-based context extraction module is proposed in this work. Gate functions are embedded in the inter-branch short connection of the proposed module. The learnable gates are deployed to decide on the relevance of the contextual information extracted at a lower scale for the next higher scale. Experimental results on salient object detection task demonstrate that gates are helpful to retain relevant contextual information across multiple-scales of the context-extraction module. The performance of the proposed gated contextual feature-based salient object detector is evaluated on five broadly used saliency detection benchmarks by comparing it with the other 13 state-of-the-art approaches. Experimental outcomes show that the proposed method achieves a favorable performance for various compared evaluation measures. IEEE

  • 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

    21101 - Food and beverages

Result continuities

  • Project

  • Continuities

    I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Others

  • Publication year

    2021

  • 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 Instrumentation and Measurement

  • ISSN

    0018-9456

  • e-ISSN

  • Volume of the periodical

    70

  • Issue of the periodical within the volume

    March

  • Country of publishing house

    US - UNITED STATES

  • Number of pages

    13

  • Pages from-to

    "Article Number: 5007613"

  • UT code for WoS article

    000731626300022

  • EID of the result in the Scopus database

    2-s2.0-85102622466