Summary of On Large Uni- and Multi-modal Models For Unsupervised Classification Of Social Media Images: Nature’s Contribution to People As a Case Study, by Rohaifa Khaldi et al.
On Large Uni- and Multi-modal Models for Unsupervised Classification of Social Media Images: Nature’s Contribution to People as a case study
by Rohaifa Khaldi, Domingo Alcaraz-Segura, Ignacio Sánchez-Herrera, Javier Martinez-Lopez, Carlos Javier Navarro, Siham Tabik
First submitted to arxiv on: 30 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 approaches for mapping social media images into predefined classes utilize state-of-the-art Large Visual Models (LVM), Large Language Models (LLM), and Large Visual Language Models (LVLM). The paper focuses on understanding human interactions with nature, a problem known as Cultural Ecosystem Services (CES). Various methods are explored, including fine-tuning LVM DINOv2 and LVLM LLaVA-1.5 combined with a fine-tuned LLM, which achieve accuracy above 95%. Additionally, fully unsupervised approaches using LVLMs like GPT-4 and LLaVA-1.5 achieve accuracy above 84%. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses computer models to sort social media pictures into groups that make sense. It’s trying to understand how people interact with nature. The researchers are testing different ways to do this, including using special language models and image recognition tools. Some of these methods work really well, with accuracy rates above 95%. This is important because it could help us better understand our impact on the environment. |
Keywords
» Artificial intelligence » Fine tuning » Gpt » Unsupervised
