论文标题
RGB-D显着对象检测的多尺度迭代改进网络
Multi-Scale Iterative Refinement Network for RGB-D Salient Object Detection
论文作者
论文摘要
在显着对象检测中利用了利用RGB-D信息的广泛研究。但是，由于语义差距在不同的特征级别上，显着的视觉提示以各种尺度和RGB图像的分辨率出现。同时，类似的显着模式在跨模式深度图像以及多尺度版本中也提供。在RGB-D显着对象检测任务中，跨模式融合和多尺度改进仍然是一个开放的问题。在本文中，我们首先引入自上而下和自下而上的迭代修复体系结构以利用多尺度功能，然后设计基于注意力的融合模块（ABF）以解决跨模式相关性。我们在七个公共数据集上进行了广泛的实验。实验结果显示了我们设计的方法的有效性
The extensive research leveraging RGB-D information has been exploited in salient object detection. However, salient visual cues appear in various scales and resolutions of RGB images due to semantic gaps at different feature levels. Meanwhile, similar salient patterns are available in cross-modal depth images as well as multi-scale versions. Cross-modal fusion and multi-scale refinement are still an open problem in RGB-D salient object detection task. In this paper, we begin by introducing top-down and bottom-up iterative refinement architecture to leverage multi-scale features, and then devise attention based fusion module (ABF) to address on cross-modal correlation. We conduct extensive experiments on seven public datasets. The experimental results show the effectiveness of our devised method
