Camouflaged Object Detection (COD) is challenging due to the strong similarity between camouflaged objects and their surroundings, which complicates identification. Existing methods mainly rely on spatial local features, failing to capture global information, while Transformers increase computationalthis http URLaddress this, the Frequency-Assisted Mamba-Like Linear Attention Network (FMNet) is proposed, which leverages frequency-domain learning to efficiently capture global features and mitigate ambiguity between objects and the background. FMNet introduces the Multi-Scale Frequency-Assisted Mamba-Like Linear Attention (MFM) module, integrating frequency and spatial features through a multi-scale structure to handle scale variations while reducing computational complexity. Additionally, the Pyramidal Frequency Attention Extraction (PFAE) module and the Frequency Reverse Decoder (FRD) enhance semantics and reconstruct features. Experimental results demonstrate that FMNet outperforms existing methods on multiple COD datasets, showcasing its advantages in both performance and efficiency. Code available atthis https URL.
View on arXiv@article{deng2025_2503.11030,
title={ FMNet: Frequency-Assisted Mamba-Like Linear Attention Network for Camouflaged Object Detection },
author={ Ming Deng and Sijin Sun and Zihao Li and Xiaochuan Hu and Xing Wu },
journal={arXiv preprint arXiv:2503.11030},
year={ 2025 }
}