论文标题
块注释：通过子图像分解的语义分割的更好图像注释
Block Annotation: Better Image Annotation for Semantic Segmentation with Sub-Image Decomposition
论文作者
论文摘要
具有高质量像素级注释的图像数据集对于语义分割很有价值：标记图像中的每个像素可确保注释稀有类和小对象。但是，全图像注释很昂贵，专家每张图像最多花费90分钟。我们提出了块子图像注释，以替代全图像注释。尽管频繁任务切换的注意力成本，但我们发现，与全图像注释相比，使用用于全图像注释的现有注释工具，可以将块注释质量更高，质量更高。令人惊讶的是，我们发现用块注释的50％像素可以使语义分割达到相同的性能至100％的像素。此外，注释的像素的12％可使性能高达98％的性能，并具有致密的注释。在弱监督的设置中，块注释在给定等效注释时间的情况下，块注释的表现优于现有方法3-4％（绝对）。为了恢复表征空间上下文和负担关系等应用的必要全球结构，我们提出了一种有效的方法，可以在没有额外的人为努力的情况下使用高质量的标签注入彩绘块注释的图像。因此，与全图注释相比，这些应用也可以使用更少的注释。
Image datasets with high-quality pixel-level annotations are valuable for semantic segmentation: labelling every pixel in an image ensures that rare classes and small objects are annotated. However, full-image annotations are expensive, with experts spending up to 90 minutes per image. We propose block sub-image annotation as a replacement for full-image annotation. Despite the attention cost of frequent task switching, we find that block annotations can be crowdsourced at higher quality compared to full-image annotation with equal monetary cost using existing annotation tools developed for full-image annotation. Surprisingly, we find that 50% pixels annotated with blocks allows semantic segmentation to achieve equivalent performance to 100% pixels annotated. Furthermore, as little as 12% of pixels annotated allows performance as high as 98% of the performance with dense annotation. In weakly-supervised settings, block annotation outperforms existing methods by 3-4% (absolute) given equivalent annotation time. To recover the necessary global structure for applications such as characterizing spatial context and affordance relationships, we propose an effective method to inpaint block-annotated images with high-quality labels without additional human effort. As such, fewer annotations can also be used for these applications compared to full-image annotation.
