用频域信息提升隐蔽目标检测的全局感知能力
FMNet: Frequency-Assisted Mamba-Like Linear Attention Network for Camouflaged Object Detection
- 融合频域与空间特征，构建轻量级线性注意力模块
- 在多个数据集上超越现有方法，兼顾精度与效率
- 适合需要高效高精度隐蔽目标检测的场景
隐蔽目标检测（COD）因目标与背景高度相似而极具挑战，现有方法多依赖局部空间特征，难以捕捉全局信息，而基于Transformer的方法又带来高昂计算成本。为此，本文提出频率辅助类Mamba线性注意力网络（FMNet），通过频域学习高效获取全局特征，缓解目标与背景的混淆问题。FMNet引入多尺度频域辅助类Mamba线性注意力（MFM）模块，利用多尺度结构融合频域与空间特征，有效应对尺度变化的同时降低计算开销。此外，金字塔频域注意力提取（PFAE）模块和频域逆向解码器（FRD）进一步增强语义表达并重构特征。实验表明，FMNet在多个COD数据集上优于现有方法，展现出卓越的性能与效率。代码已开源：https://github.com/Chranos/FMNet。
原文摘要 · Abstract (English)
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 computational costs. To address 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 at https://github.com/Chranos/FMNet.
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