Summary of Preliminary Investigations Of a Multi-faceted Robust and Synergistic Approach in Semiconductor Electron Micrograph Analysis: Integrating Vision Transformers with Large Language and Multimodal Models, by Sakhinana Sagar Srinivas et al.
Preliminary Investigations of a Multi-Faceted Robust and Synergistic Approach in Semiconductor Electron Micrograph Analysis: Integrating Vision Transformers with Large Language and Multimodal Models
by Sakhinana Sagar Srinivas, Geethan Sannidhi, Sreeja Gangasani, Chidaksh Ravuru, Venkataramana Runkana
First submitted to arxiv on: 24 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed architecture combines generative capabilities of Large Language Models (LLMs) like GPT-4(language only) with predictive ability of few-shot learning in Large Multimodal Models (LMMs) like GPT-4(V)ision, fusing image-based and linguistic insights for accurate nanomaterial category prediction. This approach aims to provide a robust solution for automated nanomaterial identification in semiconductor manufacturing, balancing performance, efficiency, and interpretability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to identify nanomaterials using electron micrographs. Right now, it’s hard to classify these images because they have complex structures. The authors combine two types of artificial intelligence models: one that can understand language (GPT-4) and another that can look at pictures (GPT-4(V)ision). By combining these abilities, the model can recognize different nanomaterials with high accuracy. |
Keywords
» Artificial intelligence » Few shot » Gpt
