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In quantum machine field, detecting two-dimensional (2D) materials in Siliconchips is one of the most critical problems. Instance segmentation can beconsidered as a potential approach to solve this problem. However, similar toother deep learning methods, the instance segmentation requires a large scaletraining dataset and high quality annotation in order to achieve a considerableperformance. In practice, preparing the training dataset is a challenge sinceannotators have to deal with a large image, e.g 2K resolution, and extremelydense objects in this problem. In this work, we present a novel method totackle the problem of missing annotation in instance segmentation in 2D quantummaterial identification. We propose a new mechanism for automatically detectingfalse negative objects and an attention based loss strategy to reduce thenegative impact of these objects contributing to the overall loss function. Weexperiment on the 2D material detection datasets, and the experiments show ourmethod outperforms previous works.

Two-Dimensional Quantum Material Identification via Self-Attention and Soft-labeling in Deep Learning