论文标题
cascadepsp：通过全球和局部改进迈向类不足和非常高分辨率的细分
CascadePSP: Toward Class-Agnostic and Very High-Resolution Segmentation via Global and Local Refinement
论文作者
论文摘要
最新的语义分割方法几乎仅在固定分辨率范围内的图像上进行训练。对于非常高分辨率的图像，这些分割是不准确的，因为使用低分辨率分割的双色孔UPSMPLING并不能充分沿对象边界捕获高分辨率的细节。在本文中，我们提出了一种新的方法来解决高分辨率分割问题，而无需使用任何高分辨率培训数据。关键的见解是我们的Cascadepsp网络，它尽可能地完善和纠正本地边界。尽管我们的网络接受了低分辨率分割数据的培训，但即使对于大于4K的非常高分辨率的图像，我们的方法也适用于任何分辨率。我们对不同数据集进行了定量和定性研究，以表明CascAdepsp可以使用我们的新颖的细化模块揭示像素准确的分割边界，而无需任何填充。因此，我们的方法可以被视为类不足的方法。最后，我们演示了我们的模型在多级分段中分析场景中的应用。
State-of-the-art semantic segmentation methods were almost exclusively trained on images within a fixed resolution range. These segmentations are inaccurate for very high-resolution images since using bicubic upsampling of low-resolution segmentation does not adequately capture high-resolution details along object boundaries. In this paper, we propose a novel approach to address the high-resolution segmentation problem without using any high-resolution training data. The key insight is our CascadePSP network which refines and corrects local boundaries whenever possible. Although our network is trained with low-resolution segmentation data, our method is applicable to any resolution even for very high-resolution images larger than 4K. We present quantitative and qualitative studies on different datasets to show that CascadePSP can reveal pixel-accurate segmentation boundaries using our novel refinement module without any finetuning. Thus, our method can be regarded as class-agnostic. Finally, we demonstrate the application of our model to scene parsing in multi-class segmentation.
