Polarization-driven Camouflaged ObjectSegmentation via Gated Fusion

Recently, polarization-based models for camouflaged object segmentation have attracted research attention. However, to construct this camouflaged object segmentation model, the main challenge is to effectively fuse polarization and light intensity features. Therefore, we propose a multi-modal camouflaged object segmentation method via gated fusion. First, the spatial positioning module is designed to perform channel calibration and global spatial attention alignment between polarization mode and light intensity mode from high-level feature representation to locate object positioning accurately. Then, the gated fusion module (GFM) is designed to selectively fuse the object information contained in the polarization and light intensity features. Among them, semantic information of location features is introduced in the GFM to guide each mode to aggregate dominant features. Finally, the features of each layer are aggregated to obtain an accurate segmentation result map. At the same time, considering the lack of public evaluation and training data on light intensity-polarization (I-P) camouflaged detection, we build the light I-P camouflaged detection dataset. Experimental results demonstrate that our proposed method outperforms other typical multi-modal segmentation methods in this dataset.