  Camouflaged object detection is a challenging task that aims to identify
objects having similar texture to the surroundings. This paper presents to
amplify the subtle texture difference between camouflaged objects and the
background for camouflaged object detection by formulating multiple
texture-aware refinement modules to learn the texture-aware features in a deep
convolutional neural network. The texture-aware refinement module computes the
covariance matrices of feature responses to extract the texture information,
designs an affinity loss to learn a set of parameter maps that help to separate
the texture between camouflaged objects and the background, and adopts a
boundary-consistency loss to explore the object detail structures.We evaluate
our network on the benchmark dataset for camouflaged object detection both
qualitatively and quantitatively. Experimental results show that our approach
outperforms various state-of-the-art methods by a large margin.
