论文标题
有效的RGB和RGB-D显着对象检测的统一结构
A Unified Structure for Efficient RGB and RGB-D Salient Object Detection
论文作者
论文摘要
近年来，对物体检测（SOD）进行了充分的研究，尤其是使用深神经网络。但是，带有RGB和RGB-D图像的SOD通常被视为具有需要专门设计的不同网络结构的两个不同任务。在本文中，我们提出了一个具有跨意义上下文提取（CRACE）模块的统一和有效的结构，以有效地解决SOD的两个任务。所提出的Crace模块接收并适当融合了两个（用于RGB SOD）或三个（对于RGB-D SOD）输入。带有Crace模块的简单统一特征金字塔网络（FPN）类似于结构，并在显着性和边界的多层次监督下完善了结果。提出的结构简单而有效。 RGB和深度的丰富上下文信息可以有效地由提议的结构适当提取和融合。实验结果表明，我们的方法在各个数据集和大多数指标上都优于RGB和RGB-D SOD任务中的其他最先进方法。
Salient object detection (SOD) has been well studied in recent years, especially using deep neural networks. However, SOD with RGB and RGB-D images is usually treated as two different tasks with different network structures that need to be designed specifically. In this paper, we proposed a unified and efficient structure with a cross-attention context extraction (CRACE) module to address both tasks of SOD efficiently. The proposed CRACE module receives and appropriately fuses two (for RGB SOD) or three (for RGB-D SOD) inputs. The simple unified feature pyramid network (FPN)-like structure with CRACE modules conveys and refines the results under the multi-level supervisions of saliency and boundaries. The proposed structure is simple yet effective; the rich context information of RGB and depth can be appropriately extracted and fused by the proposed structure efficiently. Experimental results show that our method outperforms other state-of-the-art methods in both RGB and RGB-D SOD tasks on various datasets and in terms of most metrics.
