  Most existing RGB-D salient object detection (SOD) methods focus on the
foreground region when utilizing the depth images. However, the background also
provides important information in traditional SOD methods for promising
performance. To better explore salient information in both foreground and
background regions, this paper proposes a Bilateral Attention Network (BiANet)
for the RGB-D SOD task. Specifically, we introduce a Bilateral Attention Module
(BAM) with a complementary attention mechanism: foreground-first (FF) attention
and background-first (BF) attention. The FF attention focuses on the foreground
region with a gradual refinement style, while the BF one recovers potentially
useful salient information in the background region. Benefitted from the
proposed BAM module, our BiANet can capture more meaningful foreground and
background cues, and shift more attention to refining the uncertain details
between foreground and background regions. Additionally, we extend our BAM by
leveraging the multi-scale techniques for better SOD performance. Extensive
experiments on six benchmark datasets demonstrate that our BiANet outperforms
other state-of-the-art RGB-D SOD methods in terms of objective metrics and
subjective visual comparison. Our BiANet can run up to 80fps on $224\times224$
RGB-D images, with an NVIDIA GeForce RTX 2080Ti GPU. Comprehensive ablation
studies also validate our contributions.
