论文标题
迭代学习例如细分
Iterative Learning for Instance Segmentation
论文作者
论文摘要
实例分割是一项计算机视觉任务，其中检测并分割了图像中的单独对象。最先进的深神经网络模型需要大量标记的数据，以便在此任务中表现良好。进行这些注释很耗时。我们首次提出了一种迭代学习和注释方法，能够检测由多个相似对象组成的数据集中的实例。该方法需要最少的人类干预，只需要一个含有很少注释的自举套件。两个不同数据集的实验显示了该方法在与视觉检查有关的不同应用中的有效性。
Instance segmentation is a computer vision task where separate objects in an image are detected and segmented. State-of-the-art deep neural network models require large amounts of labeled data in order to perform well in this task. Making these annotations is time-consuming. We propose for the first time, an iterative learning and annotation method that is able to detect, segment and annotate instances in datasets composed of multiple similar objects. The approach requires minimal human intervention and needs only a bootstrapping set containing very few annotations. Experiments on two different datasets show the validity of the approach in different applications related to visual inspection.
