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Home > Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 36 > No. 3: AAAI-22 Technical Tracks 3

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

February 1, 2023

Authors

Hongwei Zhu

Nanjing University of Aeronautics and Astronautics, Nanjing, China MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, Nanjing, China


Peng Li

Nanjing University of Aeronautics and Astronautics, Nanjing, China MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, Nanjing, China


Haoran Xie

Lingnan University, Hong Kong SAR, China


Xuefeng Yan

Nanjing University of Aeronautics and Astronautics, Nanjing, China MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, Nanjing, China


Dong Liang

Nanjing University of Aeronautics and Astronautics, Nanjing, China MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, Nanjing, China


Dapeng Chen

AI Application Research Center, Huawei Technologies, Shenzhen, China


Mingqiang Wei

Nanjing University of Aeronautics and Astronautics, Nanjing, China MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, Nanjing, China


Jing Qin

Hong Kong Polytechnic University, Hong Kong SAR, China


Proceedings:

No. 3: AAAI-22 Technical Tracks 3

Volume

Issue:

Proceedings of the AAAI Conference on Artificial Intelligence, 36

Track:

AAAI Technical Track on Computer Vision III

Downloads:

Download PDF

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 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 the proposed 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.

DOI:

10.1609/aaai.v36i3.20273


AAAI

Proceedings of the AAAI Conference on Artificial Intelligence, 36



Topics: AAAI

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