Aggregating attentional dilated features for salient object detection

Lei Zhu, Jiaxing Chen, Xiaowei Hu, Chi Wing Fu, Xuemiao Xu, Jing Qin, Pheng Ann Heng

Research output: Journal article publicationJournal articleAcademic researchpeer-review

19 Citations (Scopus)

Abstract

This paper presents a novel deep learning model to aggregate the attentional dilated features for salient object detection by exploring the complementary information between the global and local context in a convolutional neural network. There are two technical contributions to our network design. First, we develop an attentional dense atrous (dilated) spatial pyramid pooling (AD-ASPP) module to selectively use the local saliency cues captured by dilated convolutions with a small rate and the global saliency cues captured by dilated convolutions with a large rate. Second, taking the feature pyramid network as the backbone, we develop an aggregation network to integrate the refined features by formulating two consecutive chains of residual learning based modules: one chain from deep to shallow layers while another chain from shallow to deep layers. We evaluate our network on seven widely-used saliency detection benchmarks by comparing it against 21 state-of-the-art methods. Experimental results show that our network outperforms others on all the seven benchmark datasets.

Original languageEnglish
Article number8836095
Pages (from-to)3358-3371
Number of pages14
JournalIEEE Transactions on Circuits and Systems for Video Technology
Volume30
Issue number10
DOIs
Publication statusPublished - Oct 2020

Keywords

  • attentional dilated features
  • multiple layer aggregation
  • Saliency detection

ASJC Scopus subject areas

  • Media Technology
  • Electrical and Electronic Engineering

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