  Salient object detection (SOD) focuses on distinguishing the most conspicuous
objects in the scene. However, most related works are based on RGB images,
which lose massive useful information. Accordingly, with the maturity of
thermal technology, RGB-T (RGB-Thermal) multi-modality tasks attain more and
more attention. Thermal infrared images carry important information which can
be used to improve the accuracy of SOD prediction. To accomplish it, the
methods to integrate multi-modal information and suppress noises are critical.
In this paper, we propose a novel network called Interactive Context-Aware
Network (ICANet). It contains three modules that can effectively perform the
cross-modal and cross-scale fusions. We design a Hybrid Feature Fusion (HFF)
module to integrate the features of two modalities, which utilizes two types of
feature extraction. The Multi-Scale Attention Reinforcement (MSAR) and Upper
Fusion (UF) blocks are responsible for the cross-scale fusion that converges
different levels of features and generate the prediction maps. We also raise a
novel Context-Aware Multi-Supervised Network (CAMSNet) to calculate the content
loss between the prediction and the ground truth (GT). Experiments prove that
our network performs favorably against the state-of-the-art RGB-T SOD methods.
