Attention-based Neighbor Selective Aggregation Network for Camouflaged Object Detection

Abstract

Camouflaged Object Detection (COD) aims to discover objects that are finely disguised in the environment. Its challenge is that the targets generally have similar textures and colors to their surroundings. In this paper, we propose a novel network, named attention-based neighbor selective aggregation network (ANSA-Net), which can effectively and efficiently detect camouflaged objects. Specifically, our ANSA-Net contains two novel modules, namely, neighbor selective aggregation (NSA) and high-level feature guided attention (HLGA). The NSA is designed to locate concealed targets by fusing multi-scale features adaptively. Furthermore, the HLGA is designed to improve the semantic information of features by employing attention maps derived from high-level features. Experiments show that ANSA-Net exhibits relatively accurate detection performance on four COD datasets, outperforming existing state-of-the-art methods.