  Depth information has been proved beneficial in RGB-D salient object
detection (SOD). However, depth maps obtained often suffer from low quality and
inaccuracy. Most existing RGB-D SOD models have no cross-modal interactions or
only have unidirectional interactions from depth to RGB in their encoder
stages, which may lead to inaccurate encoder features when facing low quality
depth. To address this limitation, we propose to conduct progressive
bi-directional interactions as early in the encoder stage, yielding a novel
bi-directional transfer-and-selection network named BTS-Net, which adopts a set
of bi-directional transfer-and-selection (BTS) modules to purify features
during encoding. Based on the resulting robust encoder features, we also design
an effective light-weight group decoder to achieve accurate final saliency
prediction. Comprehensive experiments on six widely used datasets demonstrate
that BTS-Net surpasses 16 latest state-of-the-art approaches in terms of four
key metrics.
