  RGB-thermal salient object detection (SOD) aims to segment the common
prominent regions of visible image and corresponding thermal infrared image
that we call it RGBT SOD. Existing methods don't fully explore and exploit the
potentials of complementarity of different modalities and multi-type cues of
image contents, which play a vital role in achieving accurate results. In this
paper, we propose a multi-interactive dual-decoder to mine and model the
multi-type interactions for accurate RGBT SOD. In specific, we first encode two
modalities into multi-level multi-modal feature representations. Then, we
design a novel dual-decoder to conduct the interactions of multi-level
features, two modalities and global contexts. With these interactions, our
method works well in diversely challenging scenarios even in the presence of
invalid modality. Finally, we carry out extensive experiments on public RGBT
and RGBD SOD datasets, and the results show that the proposed method achieves
the outstanding performance against state-of-the-art algorithms. The source
code has been released
at:https://github.com/lz118/Multi-interactive-Dual-decoder.
