@Article{iasc.2020.010119,
AUTHOR = {Jiajie Mai, Xuemiao Xu, Guorong Xiao, Zijun Deng, Jiaxing Chen},
TITLE = {PGCA-Net: Progressively Aggregating Hierarchical Features with the  Pyramid Guided Channel Attention for Saliency Detection},
JOURNAL = {Intelligent Automation \& Soft Computing},
VOLUME = {26},
YEAR = {2020},
NUMBER = {4},
PAGES = {847--855},
URL = {http://www.techscience.com/iasc/v26n4/40289},
ISSN = {2326-005X},
ABSTRACT = {The Salient object detection aims to segment out the most visually distinctive 
objects in an image, which is a challenging task in computer vision. In this 
paper, we present the PGCA-Net equipped with the pyramid guided channel 
attention fusion block (PGCAFB) for the saliency detection task. Given an input 
image, the hierarchical features are extracted using a deep convolutional neural 
network (DCNN), then starting from the highest-level semantic features, we 
stage-by-stage restore the spatial saliency details by aggregating the lowerlevel detailed features. Since for the weak discriminative ability of the shallow 
detailed features, directly introducing them to the semantic features will only 
lead to sub-optimal results. Thus, we take a novel pyramid channel attention 
mechanism to attend to the useful detailed shallow feature channels before 
aggregation. The experimental results show that our proposed method 
outperforms its competitors on 5 benchmark testing sets.},
DOI = {10.32604/iasc.2020.010119}
}