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

Kaihua Zhang; Tengpeng Li; Shiwen Shen; Bo Liu; Jin Chen; Qingshan Liu

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arXiv · 2003.06167

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

Kaihua Zhang·Tengpeng Li·Shiwen Shen·Bo Liu·Jin Chen·Qingshan Liu

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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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Kaihua Zhang, Tengpeng Li, Shiwen Shen, Bo Liu, Jin Chen, Qingshan Liu. 2020-03-13. Adaptive Graph Convolutional Network with Attention Graph Clustering for Co-saliency Detection. https://arxiv.org/abs/2003.06167

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