  Depth images and thermal images contain the spatial geometry information and
surface temperature information, which can act as complementary information for
the RGB modality. However, the quality of the depth and thermal images is often
unreliable in some challenging scenarios, which will result in the performance
degradation of the two-modal based salient object detection (SOD). Meanwhile,
some researchers pay attention to the triple-modal SOD task, where they attempt
to explore the complementarity of the RGB image, the depth image, and the
thermal image. However, existing triple-modal SOD methods fail to perceive the
quality of depth maps and thermal images, which leads to performance
degradation when dealing with scenes with low-quality depth and thermal images.
Therefore, we propose a quality-aware selective fusion network (QSF-Net) to
conduct VDT salient object detection, which contains three subnets including
the initial feature extraction subnet, the quality-aware region selection
subnet, and the region-guided selective fusion subnet. Firstly, except for
extracting features, the initial feature extraction subnet can generate a
preliminary prediction map from each modality via a shrinkage pyramid
architecture. Then, we design the weakly-supervised quality-aware region
selection subnet to generate the quality-aware maps. Concretely, we first find
the high-quality and low-quality regions by using the preliminary predictions,
which further constitute the pseudo label that can be used to train this
subnet. Finally, the region-guided selective fusion subnet purifies the initial
features under the guidance of the quality-aware maps, and then fuses the
triple-modal features and refines the edge details of prediction maps through
the intra-modality and inter-modality attention (IIA) module and the edge
refinement (ER) module, respectively. Extensive experiments are performed on
VDT-2048
