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