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.
|Title of host publication||Proceedings of the 36th AAAI Conference on Artificial Intelligence, AAAI 2022|
|Publisher||Association for the Advancement of Artificial Intelligence|
|Number of pages||9|
|ISBN (Electronic)||1577358767, 9781577358763|
|Publication status||Published - 30 Jun 2022|
|Event||36th AAAI Conference on Artificial Intelligence, AAAI 2022 - Virtual, Online|
Duration: 22 Feb 2022 → 1 Mar 2022
|Name||Proceedings of the AAAI Conference on Artificial Intelligence|
|Conference||36th AAAI Conference on Artificial Intelligence, AAAI 2022|
|Period||22/02/22 → 1/03/22|
Bibliographical noteFunding 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).
Copyright © 2022, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.