Multi-Modal Instruction-Tuning Small-Scale Language-and-Vision Assistant for Semiconductor Electron Micrograph Analysis

• URL: http://arxiv.org/abs/2409.07463v1
• Date: Tue, 27 Aug 2024 15:50:04 GMT
• Title: Multi-Modal Instruction-Tuning Small-Scale Language-and-Vision Assistant for Semiconductor Electron Micrograph Analysis
• Authors: Sakhinana Sagar Srinivas, Geethan Sannidhi, Venkataramana Runkana,
• Abstract summary: We present a novel framework for analyzing and interpreting electron microscopy images in semiconductor manufacturing. The framework employs a unique teacher-student approach, leveraging pre-trained multimodal large language models.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: We present a novel framework for analyzing and interpreting electron microscopy images in semiconductor manufacturing using vision-language instruction tuning. The framework employs a unique teacher-student approach, leveraging pre-trained multimodal large language models such as GPT-4 to generate instruction-following data for zero-shot visual question answering (VQA) and classification tasks, customizing smaller multimodal models (SMMs) for microscopy image analysis, resulting in an instruction-tuned language-and-vision assistant. Our framework merges knowledge engineering with machine learning to integrate domain-specific expertise from larger to smaller multimodal models within this specialized field, greatly reducing the need for extensive human labeling. Our study presents a secure, cost-effective, and customizable approach for analyzing microscopy images, addressing the challenges of adopting proprietary models in semiconductor manufacturing.

Related papers

• Parameter-Efficient Quantized Mixture-of-Experts Meets Vision-Language Instruction Tuning for Semiconductor Electron Micrograph Analysis [0.0]
  We introduce sLAVA, a small-scale vision-language assistant tailored for semiconductor manufacturing. It addresses challenges of data scarcity and acquiring high-quality, expert-annotated data.
  arXiv  Detail & Related papers  (2024-08-27T15:59:26Z)
• Preliminary Investigations of a Multi-Faceted Robust and Synergistic Approach in Semiconductor Electron Micrograph Analysis: Integrating Vision Transformers with Large Language and Multimodal Models [0.0]
  This study introduces an innovative architecture that leverages the generative capabilities of zero-shot prompting. It fuses knowledge across image based and linguistic insights for accurate nanomaterial category prediction.
  arXiv  Detail & Related papers  (2024-08-24T16:28:00Z)
• Foundational Model for Electron Micrograph Analysis: Instruction-Tuning Small-Scale Language-and-Vision Assistant for Enterprise Adoption [0.0]
  We introduce a small-scale framework for analyzing semiconductor electron microscopy images (MAEMI) We generate a customized instruction-following dataset using large multimodal models on microscopic image analysis. We perform knowledge transfer from larger to smaller models through knowledge distillation, resulting in improved accuracy of smaller models on visual question answering tasks.
  arXiv  Detail & Related papers  (2024-08-23T17:42:11Z)
• MMSci: A Dataset for Graduate-Level Multi-Discipline Multimodal Scientific Understanding [59.41495657570397]
  This dataset includes figures such as schematic diagrams, simulated images, macroscopic/microscopic photos, and experimental visualizations. We developed benchmarks for scientific figure captioning and multiple-choice questions, evaluating six proprietary and over ten open-source models. The dataset and benchmarks will be released to support further research.
  arXiv  Detail & Related papers  (2024-07-06T00:40:53Z)
• Cambrian-1: A Fully Open, Vision-Centric Exploration of Multimodal LLMs [56.391404083287235]
  We introduce Cambrian-1, a family of multimodal LLMs (MLLMs) designed with a vision-centric approach. Our study uses LLMs and visual instruction tuning as an interface to evaluate various visual representations. We provide model weights, code, supporting tools, datasets, and detailed instruction-tuning and evaluation recipes.
  arXiv  Detail & Related papers  (2024-06-24T17:59:42Z)
• Benchmarking Multi-Image Understanding in Vision and Language Models: Perception, Knowledge, Reasoning, and Multi-Hop Reasoning [15.296263261737026]
  We introduce a Multi-Image MIRB Benchmark to evaluate visual language models' ability to compare, analyze, and reason across multiple images. Our benchmark encompasses four categories: perception, visual world knowledge, reasoning, and multi-hop reasoning. We demonstrate that while open-source VLMs were shown to approach the GPT-4V in single-image tasks, a significant gap remains in multi-image reasoning tasks.
  arXiv  Detail & Related papers  (2024-06-18T16:02:18Z)
• MM1: Methods, Analysis & Insights from Multimodal LLM Pre-training [103.72844619581811]
  We build performant Multimodal Large Language Models (MLLMs) In particular, we study the importance of various architecture components and data choices. We demonstrate that for large-scale multimodal pre-training using a careful mix of image-caption, interleaved image-text, and text-only data.
  arXiv  Detail & Related papers  (2024-03-14T17:51:32Z)
• Position-Enhanced Visual Instruction Tuning for Multimodal Large Language Models [50.07056960586183]
  We propose Position-enhanced Visual Instruction Tuning (PVIT) to extend the functionality of Multimodal Large Language Models (MLLMs) This integration promotes a more detailed comprehension of images for the MLLM. We present both quantitative experiments and qualitative analysis that demonstrate the superiority of the proposed model.
  arXiv  Detail & Related papers  (2023-08-25T15:33:47Z)
• UniDiff: Advancing Vision-Language Models with Generative and Discriminative Learning [86.91893533388628]
  This paper presents UniDiff, a unified multi-modal model that integrates image-text contrastive learning (ITC), text-conditioned image synthesis learning (IS), and reciprocal semantic consistency modeling (RSC) UniDiff demonstrates versatility in both multi-modal understanding and generative tasks.
  arXiv  Detail & Related papers  (2023-06-01T15:39:38Z)
• Scaling Vision-Language Models with Sparse Mixture of Experts [128.0882767889029]
  We show that mixture-of-experts (MoE) techniques can achieve state-of-the-art performance on a range of benchmarks over dense models of equivalent computational cost. Our research offers valuable insights into stabilizing the training of MoE models, understanding the impact of MoE on model interpretability, and balancing the trade-offs between compute performance when scaling vision-language models.
  arXiv  Detail & Related papers  (2023-03-13T16:00:31Z)
• Self-supervised machine learning model for analysis of nanowire morphologies from transmission electron microscopy images [0.0]
  We present a self-supervised transfer learning approach that uses a small number of labeled microscopy images for training. Specifically, we train an image encoder with unlabeled images and use that encoder for transfer learning of different downstream image tasks.
  arXiv  Detail & Related papers  (2022-03-25T19:32:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.