针对伪装目标检测中多模态信息利用不足问题，提出分层增强融合框架。
Modality-Specific Hierarchical Enhancement for RGB-D Camouflaged Object Detection
- 分别设计纹理与几何的分层增强模块，提取高频细节和可学习梯度。
- 在四个基准上优于16种先进方法，显著提升检测精度。
- 适合需要高精度伪装目标识别的视觉系统开发者使用。
伪装目标检测（COD）因目标与背景高度相似而困难，现有方法虽结合了RGB-D纹理与几何线索，但仍未能充分挖掘模态特异性信息，导致融合质量受限。原因在于RGB与深度特征在骨干网络提取后直接融合，缺乏模态特定增强。为此，本文提出MHENet框架，通过模态特异性分层增强与自适应融合机制，提升特征表达。具体地，引入纹理分层增强模块（THEM）以提取高频信息放大细微纹理差异，几何分层增强模块（GHEM）通过可学习梯度提取强化几何结构，同时保持跨尺度语义一致性。最终，自适应动态融合模块（ADFM）以空间可变权重融合增强后的纹理与几何特征。在四个公开基准上的实验表明，MHENet在定性与定量上均超越16种先进方法。代码已开源：https://github.com/afdsgh/MHENet。
原文摘要 · Abstract (English)
Camouflaged object detection (COD) is challenging due to high target-background similarity, and recent methods address this by complementarily using RGB-D texture and geometry cues. However, RGB-D COD methods still underutilize modality-specific cues, which limits fusion quality. We believe this is because RGB and depth features are fused directly after backbone extraction without modality-specific enhancement. To address this limitation, we propose MHENet, an RGB-D COD framework that performs modality-specific hierarchical enhancement and adaptive fusion of RGB and depth features. Specifically, we introduce a Texture Hierarchical Enhancement Module (THEM) to amplify subtle texture variations by extracting high-frequency information and a Geometry Hierarchical Enhancement Module (GHEM) to enhance geometric structures via learnable gradient extraction, while preserving cross-scale semantic consistency. Finally, an Adaptive Dynamic Fusion Module (ADFM) adaptively fuses the enhanced texture and geometry features with spatially varying weights. Experiments on four benchmarks demonstrate that MHENet surpasses 16 state-of-the-art methods qualitatively and quantitatively. Code is available at https://github.com/afdsgh/MHENet.
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