  Salient Object Detection (SOD) is a popular and important topic aimed at
precise detection and segmentation of the interesting regions in the images. We
integrate the linguistic information into the vision-based U-Structure networks
designed for salient object detection tasks. The experiments are based on the
newly created DUTS Cross Modal (DUTS-CM) dataset, which contains both visual
and linguistic labels. We propose a new module called efficient Cross-Modal
Self-Attention (eCMSA) to combine visual and linguistic features and improve
the performance of the original U-structure networks. Meanwhile, to reduce the
heavy burden of labeling, we employ a semi-supervised learning method by
training an image caption model based on the DUTS-CM dataset, which can
automatically label other datasets like DUT-OMRON and HKU-IS. The comprehensive
experiments show that the performance of SOD can be improved with the natural
language input and is competitive compared with other SOD methods.
