I Can Find You! Boundary-Guided Separated Attention Network for Camouflaged Object Detection

Hongwei ZHU, Peng LI, Haoran XIE, Xuefeng YAN, Dong LIANG, Dapeng CHEN, Mingqiang WEI*, Jing QIN

*Corresponding author for this work

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

47 Citations (Scopus)

Abstract

Can you find me? By simulating how humans to discover the so-called ‘perfectly'-camouflaged object, we present a novel boundary-guided separated attention network (call BSA-Net). Beyond the existing camouflaged object detection (COD) wisdom, BSA-Net utilizes two-stream separated attention modules to highlight the separator (or say the camouflaged object's boundary) between an image's background and foreground: the reverse attention stream helps erase the camouflaged object's interior to focus on the background, while the normal attention stream recovers the interior and thus pay more attention to the foreground; and both streams are followed by a boundary guider module and combined to strengthen the understanding of the boundary. The core design of such separated attention is motivated by the COD procedure of humans: find the subtle difference between the foreground and background to delineate the boundary of a camouflaged object, then the boundary can help further enhance the COD accuracy. We validate on three benchmark datasets that our BSA-Net is very beneficial to detect camouflaged objects with the blurred boundaries and similar colors/patterns with their backgrounds. Extensive results exhibit very clear COD improvements on our BSA-Net over sixteen SOTAs.

Original languageEnglish
Title of host publicationProceedings of the 36th AAAI Conference on Artificial Intelligence, AAAI 2022
PublisherAssociation for the Advancement of Artificial Intelligence
Pages3608-3616
Number of pages9
ISBN (Electronic)1577358767, 9781577358763
Publication statusPublished - 30 Jun 2022
Event36th AAAI Conference on Artificial Intelligence, AAAI 2022 - Virtual, Online
Duration: 22 Feb 20221 Mar 2022

Publication series

NameProceedings of the AAAI Conference on Artificial Intelligence
Number11
Volume36
ISSN (Print)2159-5399
ISSN (Electronic)2374-3468

Conference

Conference36th AAAI Conference on Artificial Intelligence, AAAI 2022
CityVirtual, Online
Period22/02/221/03/22

Bibliographical note

Funding Information:
This work was supported in part by the National Natural Science Foundation of China (No. 62172218, No. 62032011), in part by the Joint Fund of National Natural Science Foundation of China and Civil Aviation Administration of China (No. U2033202), in part by the Free Exploration of Basic Research Project, Local Science and Technology Development Fund Guided by the Central Government of China (No. 2021Szvup060), and in part by the Project of Strategic Importance in The Hong Kong Polytechnic University (No. ZE2Q).

Publisher Copyright:
Copyright © 2022, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.

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