AGFNet: Attention Guided Fusion Network for Camouflaged Object Detection

Abstract

AbstractThe camouflaged object detection (COD) task is challenging because of the high similarity between target and background. Most of the existing COD methods are based on the transfer learning of salient object detection (SOD) network, which is not efficient for the COD task. It is also difficult to accurately capture the edge information of the object after the coarse-grained localization of the camouflaged object. In this paper, we propose a novel network: Attention Guided Fusion Network (AGFNet), for the task of COD. We use low-level and high-level features to extract edge and semantic information. To solve the problem of discriminating and localizing the camouflaged object, we adopt a dual-attention module, which can selectively determine the more discriminate information of the camouflaged object. In addition, our method applies a module to fuse edge and semantic information for refinement to generate sharp edges. The experiments demonstrate the effectiveness and superiority of the proposed network over state-of-the-art methods.KeywordsCamouflaged object detectionAttention mechanismFeature fusion