首页|Frequency-Guided Spatial Adaptation for Camouflaged Object Detection

Frequency-Guided Spatial Adaptation for Camouflaged Object Detection

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  • NETL
  • NSTL
  • IEEE
Camouflaged object detection (COD) aims to segment camouflaged objects which exhibit very similar patterns with the surrounding environment. Recent research works have shown that enhancing the feature representation via the frequency information can greatly alleviate the ambiguity problem between the foreground objects and the background. With the emergence of vision foundation models, like InternImage, Segment Anything Model etc, adapting the pretrained model on COD tasks with a lightweight adapter module shows a novel and promising research direction. Existing adapter modules mainly care about the feature adaptation in the spatial domain. In this paper, we propose a novel frequency-guided spatial adaptation method for COD task. Specifically, we transform the input features of the adapter into frequency domain. By grouping and interacting with frequency components located within non overlapping circles in the spectrogram, different frequency components are dynamically enhanced or weakened, making the intensity of image details and contour features adaptively adjusted. At the same time, the features that are conducive to distinguishing object and background are highlighted, indirectly implying the position and shape of camouflaged object. We conduct extensive experiments on four widely adopted benchmark datasets and the proposed method outperforms 26 state-of-the-art methods with large margins. Code will be released.

Adaptation modelsFoundation modelsShapeFusesFrequency-domain analysisObject detectionTransformsBenchmark testingFeature extractionSpectrogram

Shizhou Zhang、Dexuan Kong、Yinghui Xing、Yue Lu、Lingyan Ran、Guoqiang Liang、Hexu Wang、Yanning Zhang

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National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, School of Computer Science, Northwestern Polytechnical University, Xi'an, China

National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, School of Computer Science, Northwestern Polytechnical University, Xi'an, China|Research & Development Institute of Northwestern Polytechnical University in Shenzhen, Shenzhen, China

School of Information and Technology, Northwest University, Xi'an, China|Xi'an Key Laboratory of Human-Machine Integration and Control Technology for Intelligent Rehabilitation, Xijing University, Xi'an, China

2025

10.1109/TMM.2024.3521681

IEEE transactions on multimedia

IEEE transactions on multimedia

ISSN：
年,卷(期)：2025.27(1)
  • 76