Multi-scale pooling learning for camouflaged instance segmentation

Camouflaged instance segmentation (CIS) focuses on handling instances that attempt to blend into the background. However, existing CIS methods emphasize global interactions but overlook hidden clues at various scales, resulting in inaccurate recognition of camouflaged instances. To address this, we propose a multi-scale pooling network (MSPNet) to mine the hidden cues offered by the camouflaged instances at various scales. The network achieves an enhanced fusion of multi-scale information mainly through multilayer pooling. Specifically, the pyramid pooling transformer (P2T) is utilized as a robust backbone for extracting multi-scale features. Then, we introduce an end-to-end pooling learning transformer (PLT) to obtain instance-aware parameters and high-quality mask features. To further augment the fusion of various mask features, we design a novel multi-scale complementary feature pooling (MCFP) module. Additionally, we also suggest an instance normalization module with fused spatial attention (FSA-IN) to combine instance-aware parameters and mask features, resulting in the final camouflaged instances. Experimental results show the effectiveness of MSPNet, surpassing existing CIS models on the COD10K-Test and NC4K datasets, with respective average precision (AP) scores of 49.6% and 53.4%. This demonstrates the effectiveness of the proposed approach in detecting camouflaged instances. Our code will be published at https://github.com/another-u/MSPNet-main .