CONTOUR-ASSISTED LONG-RANGE PERCEPTUAL NETWORK FOR CAMOUFLAGED INSTANCE SEGMENTATION

High quality instance segmentation has shown emerging sig- nificance in computer vision, especially in complex camouflaged situations. For this reason, we propose a single-stage segmentation model named Contour-assisted Long-range Perceptual Network (CLPNet). By following SOLOv2 model, based on deformable transformer, we propose Aggregate and Optimize multi-layer Transformer for generating refining features. Secondly, through iterative optimization, the full use of long-range context dependencies makes the internal of the instance give strong response, and contour of object tightly surrounds the instance. Experiments on camouflaged object detection dateset show that our method reaches 41.7% AP. Compared with the previous instance segmentation method, it is obviously more effective in small object.