SFGNet: Semantic and Frequency Guided Network for Camouflaged Object Detection
This work addresses the problem of detecting camouflaged objects in images, which is important for applications like surveillance and biology, but it appears incremental as it builds on existing methods by adding semantic and frequency components.
The paper tackles camouflaged object detection by incorporating semantic prompts and frequency-domain features to improve segmentation and boundary perception, achieving state-of-the-art performance on three benchmark datasets.
Camouflaged object detection (COD) aims to segment objects that blend into their surroundings. However, most existing studies overlook the semantic differences among textual prompts of different targets as well as fine-grained frequency features. In this work, we propose a novel Semantic and Frequency Guided Network (SFGNet), which incorporates semantic prompts and frequency-domain features to capture camouflaged objects and improve boundary perception. We further design Multi-Band Fourier Module(MBFM) to enhance the ability of the network in handling complex backgrounds and blurred boundaries. In addition, we design an Interactive Structure Enhancement Block (ISEB) to ensure structural integrity and boundary details in the predictions. Extensive experiments conducted on three COD benchmark datasets demonstrate that our method significantly outperforms state-of-the-art approaches. The core code of the model is available at the following link: https://github.com/winter794444/SFGNetICASSP2026.