论文标题
深度质量意识显着对象检测
Depth Quality Aware Salient Object Detection
论文作者
论文摘要
现有的基于融合的RGB-D显着对象检测方法通常采用双流结构来进行RGB和深度之间的融合权衡（D）。 D质量通常因场景而异，而SOTA双流式方法是深度质量，这很容易导致在RGB和D之间达到互补的融合状态方面遇到很大的困难，从而导致融合不佳，导致面对低品质D。因此，该论文试图将新型深度质量的质量质量质量的子网组合到经典的范围内，以将其整合到经典的forment中，以将其整合到经典的范围内，从而将促进良好的融合物进行了促进，以促进良好的良好的良好质量，并促进了促进良好的良好质量。与SOTA双流方法相比，我们方法的主要亮点是它可以降低RGB-D融合过程中那些低质量，无限制或什至负面贡献D区域的重要性的能力，从而实现了RGB和D之间的互补状态大大提高。
The existing fusion based RGB-D salient object detection methods usually adopt the bi-stream structure to strike the fusion trade-off between RGB and depth (D). The D quality usually varies from scene to scene, while the SOTA bi-stream approaches are depth quality unaware, which easily result in substantial difficulties in achieving complementary fusion status between RGB and D, leading to poor fusion results in facing of low-quality D. Thus, this paper attempts to integrate a novel depth quality aware subnet into the classic bi-stream structure, aiming to assess the depth quality before conducting the selective RGB-D fusion. Compared with the SOTA bi-stream methods, the major highlight of our method is its ability to lessen the importance of those low-quality, no-contribution, or even negative-contribution D regions during the RGB-D fusion, achieving a much improved complementary status between RGB and D.
