English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Co-saliency detection aims to discover the common and salient foregroundsfrom a group of relevant images. For this task, we present a novel adaptivegraph convolutional network with attention graph clustering (GCAGC). Threemajor contributions have been made, and are experimentally shown to havesubstantial practical merits. First, we propose a graph convolutional networkdesign to extract information cues to characterize the intra- and interimagecorrespondence. Second, we develop an attention graph clustering algorithm todiscriminate the common objects from all the salient foreground objects in anunsupervised fashion. Third, we present a unified framework withencoder-decoder structure to jointly train and optimize the graph convolutionalnetwork, attention graph cluster, and co-saliency detection decoder in anend-to-end manner. We evaluate our proposed GCAGC method on three cosaliencydetection benchmark datasets (iCoseg, Cosal2015 and COCO-SEG). Our GCAGC methodobtains significant improvements over the state-of-the-arts on most of them.

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