Frequency Director: Learnable Mixture of Frequency Experts for Unified Concealed Scene Segmentation

Concealed object segmentation aims to segment objects thatblend into their surroundings, presenting significant challenges due tothe high similarity between objects and backgrounds. Existing methodsrely on task-specific designs and independent training, leading to limitedgeneralization and structural redundancy. Although a unified parameter-shared model for diverse concealed scenarios is highly desirable andpromising, it is hindered by two challenges: large representation gapsacross scenarios and the intrinsic difficulty of concealed targets. From afrequency-domain perspective, we observe that different concealed sce-narios exhibit more discriminative and interpretable spectral character-istics compared to those in the RGB domain. These findings motivate usto approach unified concealed scene segmentation from the perspectiveof frequency modulation. To this end, we propose Frequency Director(FreqDirect), a dynamic frequency adaptation framework that directsspectral representations across diverse concealed scenes. It features amixture of frequency experts to perform adaptive routing over frequencycomponents, capturing heterogeneous task-specific patterns, along witha spatial commonality anchor to complement the spatial and structurecues. These components collectively enable effective and efficient adap-tation to diverse concealed segmentation tasks within a single model.Extensive experiments on eight concealed scenarios demonstrate thatFreqDirect surpasses existing unified models and achieves performancecomparable to, or better than, specialist models.