Mutually Guided Fusion Learning for Collaborative Camouflaged Object Segmentation

Collaborative camouflaged object segmentation (CoCOS) is a challenging task, focusing on identifying objects that blend closely with their backgrounds by jointly processing intraclass images. Existing methods fail to fully leverage the shared features (e.g., shape, texture, and contour) from these intraclass images, which leads to poor segmentation performance in relatively complex scenarios. To address this issue, we propose a novel mutually guided fusion refinement network (MFRNet), which improves the model performance by more effectively collaborating and optimizing the shared information. Specifically, it includes feature encoding, single-image branch feature enhancement, multiimage branch feature enhancement, and mutual guidance. After the feature encoding step, we design the graph convolution self-attention (GCS) and spatial context exploration (SCE) modules to enhance multilevel features of the single-image and multiimage branches, respectively. Moreover, we propose a mutual guidance fusion (MGF) module to utilize cross-scene image information for mutual guidance and progressive refinement, enhancing intraclass collaboration for improving target feature distinction. Extensive experimental results demonstrate that our MFRNet significantly outperforms existing CoCOS methods, achieving a mean E-measure score of 0.846 on the CoCOD8K dataset. Our code will be published at https://github.com/another-u/MFRNet.