  Recently, general salient object detection (SOD) has made great progress with
the rapid development of deep neural networks. However, task-aware SOD has
hardly been studied due to the lack of task-specific datasets. In this paper,
we construct a driving task-oriented dataset where pixel-level masks of salient
objects have been annotated. Comparing with general SOD datasets, we find that
the cross-domain knowledge difference and task-specific scene gap are two main
challenges to focus the salient objects when driving. Inspired by these
findings, we proposed a baseline model for the driving task-aware SOD via a
knowledge transfer convolutional neural network. In this network, we construct
an attentionbased knowledge transfer module to make up the knowledge
difference. In addition, an efficient boundary-aware feature decoding module is
introduced to perform fine feature decoding for objects in the complex
task-specific scenes. The whole network integrates the knowledge transfer and
feature decoding modules in a progressive manner. Experiments show that the
proposed dataset is very challenging, and the proposed method outperforms 12
state-of-the-art methods on the dataset, which facilitates the development of
task-aware SOD.
