Deep convolutional neural networks have achieved competitive performance in
salient object detection, in which how to learn effective and comprehensive
features plays a critical role. Most of the previous works mainly adopted
multiple level feature integration yet ignored the gap between different
features. Besides, there also exists a dilution process of high-level features
as they passed on the top-down pathway. To remedy these issues, we propose a
novel network named GCPANet to effectively integrate low-level appearance
features, high-level semantic features, and global context features through
some progressive context-aware Feature Interweaved Aggregation (FIA) modules
and generate the saliency map in a supervised way. Moreover, a Head Attention
(HA) module is used to reduce information redundancy and enhance the top layers
features by leveraging the spatial and channel-wise attention, and the Self
Refinement (SR) module is utilized to further refine and heighten the input
features. Furthermore, we design the Global Context Flow (GCF) module to
generate the global context information at different stages, which aims to
learn the relationship among different salient regions and alleviate the
dilution effect of high-level features. Experimental results on six benchmark
datasets demonstrate that the proposed approach outperforms the
state-of-the-art methods both quantitatively and qualitatively.

该研究提出了一个名为 GCPANet 的新网络，采用一些渐进式的、上下文感知的特征交织聚合 (FIA) 模块有效地整合低层外观特征、高层语义特征和全局上下文特征，并通过监督学习生成显着性图像。此外，还设计了一个头部注意力 (HA) 模块、一个自我细化 (SR) 模块和一个全局上下文流 (GCF) 模块，以进一步提高网络性能。实验结果表明，该方法在六个基准数据集上的表现比现有方法都要好。