  Existing edge-aware camouflaged object detection (COD) methods normally
output the edge prediction in the early stage. However, edges are important and
fundamental factors in the following segmentation task. Due to the high visual
similarity between camouflaged targets and the surroundings, edge prior
predicted in early stage usually introduces erroneous foreground-background and
contaminates features for segmentation. To tackle this problem, we propose a
novel Edge-aware Mirror Network (EAMNet), which models edge detection and
camouflaged object segmentation as a cross refinement process. More
specifically, EAMNet has a two-branch architecture, where a
segmentation-induced edge aggregation module and an edge-induced integrity
aggregation module are designed to cross-guide the segmentation branch and edge
detection branch. A guided-residual channel attention module which leverages
the residual connection and gated convolution finally better extracts
structural details from low-level features. Quantitative and qualitative
experiment results show that EAMNet outperforms existing cutting-edge baselines
on three widely used COD datasets. Codes are available at
https://github.com/sdy1999/EAMNet.
