Summary of Aiscivision: a Framework For Specializing Large Multimodal Models in Scientific Image Classification, by Brendan Hogan et al.
AiSciVision: A Framework for Specializing Large Multimodal Models in Scientific Image Classification
by Brendan Hogan, Anmol Kabra, Felipe Siqueira Pacheco, Laura Greenstreet, Joshua Fan, Aaron Ferber, Marta Ummus, Alecsander Brito, Olivia Graham, Lillian Aoki, Drew Harvell, Alex Flecker, Carla Gomes
First submitted to arxiv on: 28 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
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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 paper introduces AiSciVision, a framework that uses Large Multimodal Models (LMMs) to classify images in niche scientific domains. The approach specializes LMMs into interactive research partners and classification models by incorporating Visual Retrieval-Augmented Generation (VisRAG) and domain-specific tools. This agentic workflow enables the LMM to retrieve similar positive and negative labeled images, select and apply tools to manipulate and inspect the target image, and refine its analysis before making a prediction. Each inference produces both a prediction and a natural language transcript detailing the reasoning and tool usage that led to the prediction. The framework is evaluated on three real-world scientific image classification datasets, outperforming fully supervised models in low and full-labeled data settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces AiSciVision, a new way to use Artificial Intelligence (AI) for scientific research. Currently, AI often works like a “black box” that doesn’t explain its decisions. AiSciVision changes this by allowing the AI to interact with humans and provide explanations for its predictions. The approach uses a combination of techniques, including searching for similar images and using specialized tools to analyze new data. This helps scientists understand why the AI made certain predictions. AiSciVision is tested on real-world datasets and performs better than other methods. It’s even being used in real-world research applications. |
Keywords
» Artificial intelligence » Classification » Image classification » Inference » Retrieval augmented generation » Supervised
