Salient object detection (SOD) has been in the spotlight recently, yet has been studied less for high-resolution (HR) images. Unfortunately, HR images and their pixel-level annotations are certainly more labor-intensive and time-consuming compared to low-resolution (LR) images and annotations. Therefore, we propose an image pyramid-based SOD framework, Inverse Saliency Pyramid Reconstruction Network (InSPyReNet), for HR prediction without any of HR datasets. We design InSPyReNet to produce a strict image pyramid structure of saliency map, which enables to ensemble multiple results with pyramid-based image blending. For HR prediction, we design a pyramid blending method which synthesizes two different image pyramids from a pair of LR and HR scale from the same image to overcome effective receptive field (ERF) discrepancy. Our extensive evaluations on public LR and HR SOD benchmarks demonstrate that InSPyReNet surpasses the State-of-the-Art (SotA) methods on various SOD metrics and boundary accuracy.
翻译:对高分辨率图像的研究较少。 不幸的是,与低分辨率图像和说明相比,HR图像及其像素级说明的劳动密集程度和耗时性当然更高。因此,我们提议一个基于图像的SOD金字塔式框架,即反相相光膜膜重建网络(InSPyReNet),用于在没有任何HR数据集的情况下对HR进行预测。我们设计InSPyReNet,以产生一个精致的显要地图图像金字塔结构,使基于金字塔的图像混合成多种结果。关于HR的预测,我们设计一种金字塔式混合方法,将来自LR和HR比例的两种不同的图像金字塔式结构综合起来,以有效克服实际的可接受领域差异。我们对公共LR和HR SOD基准的广泛评价表明,InSPyreNet超过了关于各种SOD指标和边界准确性的国家艺术(SOD)方法。