轻量级模块提升显著目标检测精度，兼顾速度与效果。
CPDR: Towards Highly-Efficient Salient Object Detection via Crossed Post-decoder Refinement
- 引入交叉后解码精炼模块，融合高低层特征增强表达
- 在5个数据集上超越现有方法，参数量更低
- 适合资源受限场景下的实时显著目标检测
当前大多数显著目标检测方法采用深层网络和大尺寸骨干网络，导致计算复杂度显著增加。多数网络设计沿用纯UNet或特征金字塔网络（FPN）架构，其特征提取与聚合能力有限。为此，本文提出一种轻量级后解码精炼模块——交叉后解码精炼（CPDR），用于增强标准FPN或U-Net框架的特征表示。具体地，提出注意力下采样融合（ADF），利用高层特征生成的注意力图来优化低层特征；同时设计注意力上采样融合（AUF），通过低层信息引导高层特征的时空注意力。在此基础上，进一步提出双注意力交叉融合（DACF），在减少参数量的同时保持性能。在五个基准数据集上的实验表明，该方法优于此前最优方法。
原文摘要 · Abstract (English)
Most of the current salient object detection approaches use deeper networks with large backbones to produce more accurate predictions, which results in a significant increase in computational complexity. A great number of network designs follow the pure UNet and Feature Pyramid Network (FPN) architecture which has limited feature extraction and aggregation ability which motivated us to design a lightweight post-decoder refinement module, the crossed post-decoder refinement (CPDR) to enhance the feature representation of a standard FPN or U-Net framework. Specifically, we introduce the Attention Down Sample Fusion (ADF), which employs channel attention mechanisms with attention maps generated by high-level representation to refine the low-level features, and Attention Up Sample Fusion (AUF), leveraging the low-level information to guide the high-level features through spatial attention. Additionally, we proposed the Dual Attention Cross Fusion (DACF) upon ADFs and AUFs, which reduces the number of parameters while maintaining the performance. Experiments on five benchmark datasets demonstrate that our method outperforms previous state-of-the-art approaches.
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