ALMNet: Adjacent Layer Driven Multiscale Features for Salient Object Detection

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F21%3A50018317" target="_blank" >RIV/62690094:18450/21:50018317 - isvavai.cz</a>

Result on the web

<a href="https://ieeexplore.ieee.org/document/9524698" target="_blank" >https://ieeexplore.ieee.org/document/9524698</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/TIM.2021.3108503" target="_blank" >10.1109/TIM.2021.3108503</a>

Result language

angličtina

Original language name

ALMNet: Adjacent Layer Driven Multiscale Features for Salient Object Detection

Original language description

Nowadays, the usage of deep learning-based approaches for salient object detection (SOD) is increasing exponentially to detect and localize visually distinct regions in static images. However, the variability in scales of salient objects requires further attention given the abstract nature of the multi-layer feature hierarchy of convolution neural networks (CNNs). Firstly, feature maps of different layers in CNNs embed abstract information about objects that changes with the object’s scale. Secondly, the progressive feature fusion in models such as the feature pyramid network loses its effectiveness in detecting sharp boundaries due to the late fusion of detailed features. This work proposes two modules namely, adjacent layer attention block and partial encoder-decoder block to handle the aforementioned issues. The proposed adjacent layer attention block facilitates communication among the layers of closest abstraction to mine abundant scale features at the current resolution. The resultant integrated feature at a resolution contains detailed as well as semantic information from interaction among the adjacent layers useful to extract scale information of complex objects. A partial encoder-decoder module utilizes the resolution-specific integrated features from the adjacent layer attention block of its corresponding encoder to generate multi-scale features, and fuse them in an top-down manner. This level-specific distribution of aggregated features within a partial encoder-decoder helps coarser-layers of the network to acquire boundary information. Experimental results on five broadly used salient object detection datasets are compared with recent 20 state-of-the-art SOD models. The proposed method performs favorably against its competitors without any pre-processing or post-processing. IEEE

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

20201 - Electrical and electronic engineering

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2021

Confidentiality

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

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

August

Country of publishing house

US - UNITED STATES

Number of pages

14

Pages from-to

"Article Number: 2513214"

UT code for WoS article

000698642300004

EID of the result in the Scopus database

2-s2.0-85114729199