Two-Dimensional Quantum Material Identification via Self-Attention and Soft-labeling in Deep Learning
This addresses the challenge of preparing large-scale, high-quality datasets for quantum machine learning, but it is incremental as it builds on existing instance segmentation methods.
The paper tackles the problem of missing annotations in instance segmentation for 2D quantum material identification by proposing a method to detect false negatives and use attention-based loss, resulting in performance improvements over previous works.
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.