Salient object detection is used as a pre-process in many computer vision tasks (such as salient object segmentation, video salient object detection, etc.). When performing salient object detection, depth information can provide clues to the location of target objects, so effective fusion of RGB and depth feature information is important. In this paper, we propose a new feature information aggregation approach, weighted group integration (WGI), to effectively integrate RGB and depth feature information. We use a dual-branch structure to slice the input RGB image and depth map separately and then merge the results separately by concatenation. As grouped features may lose global information about the target object, we also make use of the idea of residual learning, taking the features captured by the original fusion method as supplementary information to ensure both accuracy and completeness of the fused information. Experiments on five datasets show that our model performs better than typical existing approaches for four evaluation metrics.

显着对象检测在许多计算机视觉任务（例如显着对象分割，视频显着对象检测等）中用作预处理。在执行显着物体检测时，深度信息可以提供目标物体位置的线索，因此RGB与深度特征信息的有效融合非常重要。在本文中，我们提出了一种新的特征信息聚合方法，即加权组集成（WGI），以有效地集成RGB和深度特征信息。我们使用双分支结构分别对输入的RGB图像和深度图进行切片，然后通过串联分别合并结果。由于分组的要素可能会丢失有关目标对象的全局信息，因此我们还利用了残差学习的思想，以原始融合方法捕获的特征作为补充信息，以确保融合信息的准确性和完整性。对五个数据集的实验表明，对于四个评估指标，我们的模型比典型的现有方法表现更好。