  Camouflaged object detection (COD) is a challenging task due to the low
boundary contrast between the object and its surroundings. In addition, the
appearance of camouflaged objects varies significantly, e.g., object size and
shape, aggravating the difficulties of accurate COD. In this paper, we propose
a novel Context-aware Cross-level Fusion Network (C2F-Net) to address the
challenging COD task. Specifically, we propose an Attention-induced Cross-level
Fusion Module (ACFM) to integrate the multi-level features with informative
attention coefficients. The fused features are then fed to the proposed
Dual-branch Global Context Module (DGCM), which yields multi-scale feature
representations for exploiting rich global context information. In C2F-Net, the
two modules are conducted on high-level features using a cascaded manner.
Extensive experiments on three widely used benchmark datasets demonstrate that
our C2F-Net is an effective COD model and outperforms state-of-the-art models
remarkably. Our code is publicly available at:
https://github.com/thograce/C2FNet.
