论文标题
重新分辨率显着对象检测的重新访问图像金字塔结构
Revisiting Image Pyramid Structure for High Resolution Salient Object Detection
论文作者
论文摘要
显着对象检测（SOD）最近引起了人们的关注，但对高分辨率（HR）图像的研究较少。不幸的是，与低分辨率（LR）图像和注释相比，HR图像及其像素级注释肯定更加劳动密集型和耗时。因此，我们建议没有任何HR数据集的HR预测，建议基于图像金字塔的SOD框架，逆显着性金字塔重建网络（INSPYRENET）。我们设计了Inspyrenet，以生成显着图像的显着图像结构，从而使多个结果与基于金字塔的图像混合在一起。为了进行HR预测，我们设计了一种金字塔混合方法，该方法从同一图像中从一对LR和HR量表中综合了两个不同的图像金字塔，以克服有效的接受场（ERF）差异。我们对公共LR和HR SOD基准的广泛评估表明，Inspyrenet超过了各种SOD指标和边界准确性的最新方法（SOTA）方法。
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.
