  Salient object detection segments attractive objects in scenes. RGB and
thermal modalities provide complementary information and scribble annotations
alleviate large amounts of human labor. Based on the above facts, we propose a
scribble-supervised RGB-T salient object detection model. By a four-step
solution (expansion, prediction, aggregation, and supervision), label-sparse
challenge of scribble-supervised method is solved. To expand scribble
annotations, we collect the superpixels that foreground scribbles pass through
in RGB and thermal images, respectively. The expanded multi-modal labels
provide the coarse object boundary. To further polish the expanded labels, we
propose a prediction module to alleviate the sharpness of boundary. To play the
complementary roles of two modalities, we combine the two into aggregated
pseudo labels. Supervised by scribble annotations and pseudo labels, our model
achieves the state-of-the-art performance on the relabeled RGBT-S dataset.
Furthermore, the model is applied to RGB-D and video scribble-supervised
applications, achieving consistently excellent performance.
