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

arXiv: https://arxiv.org/abs/2205.15948 · HTML: https://mlsanity.com/paper/2205.15948/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 55 references that are in the map.
- First posted on arXiv 31 May 2022.

## Builds on (most cited first, 11 of 11)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [Training Region-based Object Detectors with Online Hard Example Mining](https://mlsanity.com/paper/1604.03540/) (2016)
- [Co-mining: Self-Supervised Learning for Sparsely Annotated Object Detection](https://mlsanity.com/paper/2012.01950/) (2020)
- [Solving Missing-Annotation Object Detection with Background Recalibration Loss](https://mlsanity.com/paper/2002.05274/) (2020)
- [Deep-Learning-Based Image Segmentation Integrated with Optical Microscopy for Automatically Searching for Two-Dimensional Materials](https://mlsanity.com/paper/1910.12750/) (2019)

## Built on by (most cited first, 15 of 15)

- [Insect-Foundation: A Foundation Model and Large-scale 1M Dataset for Visual Insect Understanding](https://mlsanity.com/paper/2311.15206/) (2023)
- [QClusformer: A Quantum Transformer-based Framework for Unsupervised Visual Clustering](https://mlsanity.com/paper/2405.19722/) (2024)
- [Quantum Visual Feature Encoding Revisited](https://mlsanity.com/paper/2405.19725/) (2024)
- [Hierarchical Quantum Control Gates for Functional MRI Understanding](https://mlsanity.com/paper/2408.03596/) (2024)
- [Insect-Foundation: A Foundation Model and Large Multimodal Dataset for Vision-Language Insect Understanding](https://mlsanity.com/paper/2502.09906/) (2025)
- [BRACTIVE: A Brain Activation Approach to Human Visual Brain Learning](https://mlsanity.com/paper/2405.18808/) (2024)
- [COBRA: A Continual Learning Approach to Vision-Brain Understanding](https://mlsanity.com/paper/2411.17475/) (2024)
- [Brainformer: Mimic Human Visual Brain Functions to Machine Vision Models via fMRI](https://mlsanity.com/paper/2312.00236/) (2023)
- [varphi-Adapt: A Physics-Informed Adaptation Learning Approach to 2D Quantum Material Discovery](https://mlsanity.com/paper/2507.05184/) (2025)
- [MANGO: Multimodal Attention-based Normalizing Flow Approach to Fusion Learning](https://mlsanity.com/paper/2508.10133/) (2025)
- [QuPAINT: Physics-Aware Instruction Tuning Approach to Quantum Material Discovery](https://mlsanity.com/paper/2602.17478/) (2026)
- [CLIFF: Continual Learning for Incremental Flake Features in 2D Material Identification](https://mlsanity.com/paper/2508.17261/) (2025)
- [OpenQlaw: An Agentic AI Assistant for Analysis of 2D Quantum Materials](https://mlsanity.com/paper/2603.17043/) (2026)
- [BRAIN: Bias-Mitigation Continual Learning Approach to Vision-Brain Understanding](https://mlsanity.com/paper/2508.18187/) (2025)
- [QuPAINT: Physics-Aware Multimodal Reasoning for Quantum Material Characterization](https://mlsanity.com/paper/2609.12202/) (2026)

## Abstract (arXiv)

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.