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.

本文提出了一种新的方法来解决二维量子材料在硅芯片中的检测问题，该方法利用实例分割重点解决了缺少注释数据的问题，并通过自动检测假阴性物体和基于注意力的损失策略来减少这些物体对整体损失函数的负面影响，实验表明该方法优于之前的工作。

利用自注意力和软标记的深度学习进行二维量子材料识别