Edge-Aware Mirror Network for Camouflaged Object Detection
This work addresses the challenge of detecting camouflaged objects in images, which is important for applications like medical imaging and surveillance, but it is incremental as it builds on existing edge-aware methods.
The paper tackles the problem of camouflaged object detection by proposing an Edge-aware Mirror Network (EAMNet) that cross-refines edge detection and segmentation, achieving state-of-the-art performance on three benchmark datasets.
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.