Global Context-Aware Progressive Aggregation Network for Salient Object Detection

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…

• NN
  National Natural Science Foundation of China

  Awards: U1636214, XDB28000000, 61976202, 61672514, 61620106009, 61836002, 61931008
• CA
  Chinese Academy of Sciences

  Awards: Grant No. XDB28000000, QYZDJ-SSW-SYS013, XDB28000000
• YI
  Youth Innovation Promotion Association of the Chinese Academy of Sciences
• FR
  Fundamental Research Funds for the Central Universities

  Awards: 2019RC039, XDB28000000
• YI
  Youth Innovation Promotion Association