In quantum machine field, detecting two-dimensional (2D) materials in Silicon chips is one of the most critical problems. Instance segmentation can be considered as a potential approach to solve this problem. However, similar to other deep learning methods, the instance segmentation requires a large scale training dataset and high quality annotation in order to achieve a considerable performance. In practice, preparing the training dataset is a challenge since annotators have to deal with a large image, e.g 2K resolution, and extremely dense objects in this problem. In this work, we present a novel method to tackle the problem of missing annotation in instance segmentation in 2D quantum material identification. We propose a new mechanism for automatically detecting false negative objects and an attention based loss strategy to reduce the negative impact of these objects contributing to the overall loss function. We experiment on the 2D material detection datasets, and the experiments show our method outperforms previous works.

翻译：在量子机器学习领域，检测硅芯片中的二维材料是最关键的问题之一。实例分割可被视为解决该问题的潜在方法。然而，与其他深度学习方法类似，实例分割需要大规模的训练数据集与高质量的标注才能获得可观性能。实践中，由于标注人员需要处理大尺寸图像（例如2K分辨率）及该问题中极度密集的目标对象，制备训练数据集是一项挑战。本研究提出了一种新方法，用于解决二维量子材料识别中实例分割的标注缺失问题。我们设计了一种自动检测假阴性目标的机制，并基于注意力机制提出损失函数策略，以降低这些目标对整体损失函数的负面影响。我们在二维材料检测数据集上进行了实验，结果表明我们的方法优于先前工作。