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
