Global context-aware progressive aggregation network for salient object detection

Zuyao CHEN, Qianqian XU, Runmin CONG, Qingming HUANG*

*Corresponding author for this work

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Referred Conference Paperpeer-review

330 Citations (Scopus)

Abstract

Deep convolutional neural networks have achieved competitive performance in salient object detection, in which how to learn effective and comprehensive features plays a critical role. Most of the previous works mainly adopted multiplelevel feature integration yet ignored the gap between different features. Besides, there also exists a dilution process of high-level features as they passed on the top-down pathway. To remedy these issues, we propose a novel network named GCPANet to effectively integrate low-level appearance features, high-level semantic features, and global context features through some progressive context-aware Feature Interweaved Aggregation (FIA) modules and generate the saliency map in a supervised way. Moreover, a Head Attention (HA) module is used to reduce information redundancy and enhance the top layers features by leveraging the spatial and channel-wise attention, and the Self Refinement (SR) module is utilized to further refine and heighten the input features. Furthermore, we design the Global Context Flow (GCF) module to generate the global context information at different stages, which aims to learn the relationship among different salient regions and alleviate the dilution effect of high-level features. Experimental results on six benchmark datasets demonstrate that the proposed approach outperforms the state-of-the-art methods both quantitatively and qualitatively.

Original languageEnglish
Title of host publicationProceedings : 34th AAAI Conference on Artificial Intelligence, AAAI 2020
PublisherAAAI press
Pages10599-10606
Number of pages8
ISBN (Electronic)9781577358350
DOIs
Publication statusPublished - 2020
Externally publishedYes
Event34th AAAI Conference on Artificial Intelligence, AAAI 2020 - New York, United States
Duration: 7 Feb 202012 Feb 2020

Conference

Conference34th AAAI Conference on Artificial Intelligence, AAAI 2020
Country/TerritoryUnited States
CityNew York
Period7/02/2012/02/20

Bibliographical note

Publisher Copyright:
© AAAI 2020 - 34th AAAI Conference on Artificial Intelligence. All Rights Reserved.

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