  The application of light field data in salient object de-tection is becoming
increasingly popular recently. The diffi-culty lies in how to effectively fuse
the features within the fo-cal stack and how to cooperate them with the feature
of theall-focus image. Previous methods usually fuse focal stackfeatures via
convolution or ConvLSTM, which are both lesseffective and ill-posed. In this
paper, we model the infor-mation fusion within focal stack via graph networks.
Theyintroduce powerful context propagation from neighbouringnodes and also
avoid ill-posed implementations. On the onehand, we construct local graph
connections thus avoidingprohibitive computational costs of traditional graph
net-works. On the other hand, instead of processing the twokinds of data
separately, we build a novel dual graph modelto guide the focal stack fusion
process using all-focus pat-terns. To handle the second difficulty, previous
methods usu-ally implement one-shot fusion for focal stack and
all-focusfeatures, hence lacking a thorough exploration of their sup-plements.
We introduce a reciprocative guidance schemeand enable mutual guidance between
these two kinds of in-formation at multiple steps. As such, both kinds of
featurescan be enhanced iteratively, finally benefiting the saliencyprediction.
Extensive experimental results show that theproposed models are all beneficial
and we achieve signif-icantly better results than state-of-the-art methods.
