Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Adaptive Graph Convolutional Network with Attention Graph Clustering for
  Co-saliency Detection   Co-saliency detection aims to discover the common and salient foregrounds
from a group of relevant images. For this task, we present a novel adaptive
graph convolutional network with attention graph clustering (GCAGC). Three
major contributions have been made, and are experimentally shown to have
substantial practical merits. First, we propose a graph convolutional network
design to extract information cues to characterize the intra- and interimage
correspondence. Second, we develop an attention graph clustering algorithm to
discriminate the common objects from all the salient foreground objects in an
unsupervised fashion. Third, we present a unified framework with
encoder-decoder structure to jointly train and optimize the graph convolutional
network, attention graph cluster, and co-saliency detection decoder in an
end-to-end manner. We evaluate our proposed GCAGC method on three cosaliency
detection benchmark datasets (iCoseg, Cosal2015 and COCO-SEG). Our GCAGC method
obtains significant improvements over the state-of-the-arts on most of them.

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The categories of this paper are: q-alg
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The categories of this paper are: adap-org
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The categories of this paper are: alg-geom
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The categories of this paper are: atom-ph
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The categories of this paper are: quant-ph
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The categories of this paper are: comp-gas
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The categories of this paper are: supr-con
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The categories of this paper are: patt-sol
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The categories of this paper are: astro-ph
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The categories of this paper are: q-bio
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The categories of this paper are: nlin
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The categories of this paper are: chem-ph
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The categories of this paper are: math-ph
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The categories of this paper are: eess
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The categories of this paper are: cond-mat
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The categories of this paper are: hep-ph