粗标注下的语义分割
Semantic segmentation with coarse annotations
AI总结：
本文针对粗标注下的语义分割难题，提出一种基于超像素上采样的正则化方法，应用于FCN-16架构，在多个数据集上显著提升边界召回率。
摘要：
语义分割是将图像中每个像素进行分类的任务。训练分割模型时，使用每像素标注了对应类别的标注图像可获得最佳结果。当获取精细标注困难或成本高昂时，可以获取粗标注，例如粗略标注图像中的像素，而将类别边界周围的某些像素留作未标注。粗标注下的分割是困难的，尤其是当目标是优化类别间边界对齐时。本文提出了一种针对具有基于超像素上采样的编码器-解码器架构模型的正则化方法。该方法鼓励解码图像中被分割的像素成为SLIC超像素，这些超像素基于像素颜色和位置，与分割标注无关。该方法应用于FCN-16全卷积网络架构，并在SUIM、Cityscapes和PanNuke数据集上进行了评估。结果表明，与最先进模型相比，在粗标注上训练时，边界召回率显著提高。
英文摘要：
Semantic segmentation is the task of classifying each pixel in an image. Training a segmentation model achieves best results using annotated images, where each pixel is annotated with the corresponding class. When obtaining fine annotations is difficult or expensive, it may be possible to acquire coarse annotations, e.g. by roughly annotating pixels in an images leaving some pixels around the boundaries between classes unlabeled. Segmentation with coarse annotations is difficult, in particular when the objective is to optimize the alignment of boundaries between classes. This paper proposes a regularization method for models with an encoder-decoder architecture with superpixel based upsampling. It encourages the segmented pixels in the decoded image to be SLIC-superpixels, which are based on pixel color and position, independent of the segmentation annotation. The method is applied to FCN-16 fully convolutional network architecture and evaluated on the SUIM, Cityscapes, and PanNuke data sets. It is shown that the boundary recall improves significantly compared to state-of-the-art models when trained on coarse annotations.
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