Depth-Assisted Camouflaged Object Segmentation via Frequency-Domain Fusion and High-Order Interaction

Recently, some studies have introduced depth cues to solve camouflaged object segmentation (COS) tasks and significantly improve segmentation performance. However, current methods still have two limitations: 1) they are confined to first-order or second-order interaction modeling; 2) they neglect the frequency-domain complementary characteristics between RGB and depth modalities. In this work, we propose a Frequency-domain Fusion and High-order Interaction framework, named F$^{2}$HI, to alleviate the above limitations. Specifically, F$^{2}$HI consists of two key components: Frequency Domain Interaction Fusion (FDIF) and the High-order Interaction Unit (HOIU). The FDIF decouples the frequency information of RGB and depth modalities into high-frequency and low-frequency components, utilizing the dominant frequency components of one modality to enhance the weaker frequency components of the other modality, thereby achieving complementary enhancement in the frequency domain. Moreover, it employs cross-attention to facilitate global multimodal interaction. The HOIU employs cascaded self-attention to parse cross-region features generated by multi-object camouflage scenarios, thereby achieving high-order feature interaction. Compared with 27 state-of-the-art COS methods, F$^{2}$HI achieves competitive performance on four mainstream COS benchmarks.