论文标题
实例细分的调查：最新技术
A Survey on Instance Segmentation: State of the art
论文作者
论文摘要
对象检测或本地化是从粗糙数字图像推断发展的增量步骤。它不仅提供了图像对象的类，而且还提供了已分类的图像对象的位置。该位置以边界框或质心的形式给出。语义分割通过预测输入图像中每个像素的标签来提供精细的推断。每个像素都根据包装在其中的对象类标记。进化此演变，实例细分为属于同一类的对象的单独实例提供了不同的标签。因此，实例分割可以定义为同时解决对象检测问题以及语义分割的技术。在此调查文件中，讨论了有关最新技术的背景，问题，技术，进化，流行的数据集，与最新范围和未来范围相关的工作。本文为那些想在实例细分领域进行研究的人提供了宝贵的信息。
Object detection or localization is an incremental step in progression from coarse to fine digital image inference. It not only provides the classes of the image objects, but also provides the location of the image objects which have been classified. The location is given in the form of bounding boxes or centroids. Semantic segmentation gives fine inference by predicting labels for every pixel in the input image. Each pixel is labelled according to the object class within which it is enclosed. Furthering this evolution, instance segmentation gives different labels for separate instances of objects belonging to the same class. Hence, instance segmentation may be defined as the technique of simultaneously solving the problem of object detection as well as that of semantic segmentation. In this survey paper on instance segmentation -- its background, issues, techniques, evolution, popular datasets, related work up to the state of the art and future scope have been discussed. The paper provides valuable information for those who want to do research in the field of instance segmentation.
