Adaptive Graph Convolutional Network with Attention Graph Clustering for Co-saliency Detection

• URL: http://arxiv.org/abs/2003.06167v1
• Date: Fri, 13 Mar 2020 09:35:59 GMT
• Title: Adaptive Graph Convolutional Network with Attention Graph Clustering for Co-saliency Detection
• Authors: Kaihua Zhang, Tengpeng Li, Shiwen Shen, Bo Liu, Jin Chen, Qingshan Liu
• Abstract summary: We present a novel adaptive graph convolutional network with attention graph clustering (GCAGC) We develop an attention graph clustering algorithm to discriminate the common objects from all the salient foreground objects in an unsupervised fashion. We evaluate our proposed GCAGC method on three cosaliency detection benchmark datasets.
• Score: 35.23956785670788
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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