Abstract
Although CNN-based camouflaged object detection(COD) has made great progress in recent years, their prediction maps usually contain incomplete detail information due to the similarity between the camouflaged object and the background. To alleviate this, a CNN-based framework named SACF-Net is designed for COD via cross-level fusion that facilitates the detection of camouflaged object detail. On the one hand, the low-level features contain abundant edge detail information to distinguish the camouflaged object from the background. On the other hand, the Polarized Self-Attention(PSA) mechanism is introduced to refine high-level features that contain extensive semantic information to enhance inner details and performance. Finally, cross-level complementarity fusion is performed progressively to generate prediction maps in a top-down manner. Extensive experiments on four COD datasets exhibit that the proposed method is better than the state-of-the-art methods.
A. Wang—This work is supported by the National Natural Science Foundation of China under Grant(62162013), the National Undergraduate on Innovation and Entrepreneurship Training Program of Guizhou Province(S202110663028, S202110663029), and the Key Laboratory of Exploitation and Study of Distinctive Plants in Education Department of Sichuan Province(TSZW2109).
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Zhan, C., He, L., Liu, Y., Xu, B., Wang, A. (2022). Self-attention Based Cross-Level Fusion Network for Camouflaged Object Detection. In: Deng, W., et al. Biometric Recognition. CCBR 2022. Lecture Notes in Computer Science, vol 13628. Springer, Cham. https://doi.org/10.1007/978-3-031-20233-9_59
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