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Visual Saliency Detection via Convolutional Gated Recurrent Units
Published in Springer
2019
Volume: 11954 LNCS
   
Pages: 162 - 174
Abstract
Context is an important aspect for accurate saliency detection. However, the question of how to formally model image context within saliency detection frameworks is still an open problem. Recent saliency detection models designed using complex Deep Neural Networks to extract robust features, however often fail to select the right contextual features. These methods generally utilize physical attributes of objects for generating final saliency maps, but ignores scene contextual information. In this paper, we overcome such limitation using (i) a proposed novel end-to-end framework with a Contextual Unit (CTU) module that models the scene contextual information to give efficient saliency maps with the help of Convolutional GRU (Conv-GRU). This is the first work reported so far that utilizes Conv-GRU to generate image saliency maps. In addition, (ii) we propose a novel way of using the Conv-GRU that helps to refine saliency maps based on input image context. The proposed model has been evaluated on challenging benchmark saliency datasets, where it outperforms prominent state-of-the-art methods. © 2019, Springer Nature Switzerland AG.
About the journal
JournalData powered by TypesetLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherData powered by TypesetSpringer
ISSN03029743
Open AccessNo
Concepts (13)
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    Convolution
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    Deep learning
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    Deep neural networks
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    Visualization
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    CONTEXTUAL FEATURE
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    Contextual information
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    GATED RECURRENT UNIT
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    IMAGE SALIENCY MAP
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    SALIENCY DETECTION
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    State-of-the-art methods
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    VISUAL SALIENCY
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    VISUAL SALIENCY DETECTIONS
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    Image segmentation