On Large Uni- and Multi-modal Models for Unsupervised Classification of Social Media Images: Nature's Contribution to People as a case study

• URL: http://arxiv.org/abs/2410.00275v2
• Date: Wed, 16 Oct 2024 10:27:14 GMT
• Title: On Large Uni- and Multi-modal Models for Unsupervised Classification of Social Media Images: Nature's Contribution to People as a case study
• Authors: Rohaifa Khaldi, Domingo Alcaraz-Segura, Ignacio Sánchez-Herrera, Javier Martinez-Lopez, Carlos Javier Navarro, Siham Tabik,
• Abstract summary: This work proposes, analyzes, and compares various approaches for mapping social media images into a number of predefined classes. As a case study, we consider the problem of understanding the interactions between humans and nature, also known as Nature's Contribution to People or Cultural Ecosystem Services (CES) Our experiments show that the highest-performing approaches, with accuracy above 95%, still require the creation of a small labeled dataset.
• Score: 1.7736307382785161
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Social media images have proven to be a valuable source of information for understanding human interactions with important subjects such as cultural heritage, biodiversity, and nature, among others. The task of grouping such images into a number of semantically meaningful clusters without labels is challenging due to the high diversity and complex nature of the visual content in addition to their large volume. On the other hand, recent advances in Large Visual Models (LVMs), Large Language Models (LLMs), and Large Visual Language Models (LVLMs) provide an important opportunity to explore new productive and scalable solutions. This work proposes, analyzes, and compares various approaches based on one or more state-of-the-art LVM, LLM, and LVLM, for mapping social media images into a number of predefined classes. As a case study, we consider the problem of understanding the interactions between humans and nature, also known as Nature's Contribution to People or Cultural Ecosystem Services (CES). Our experiments show that the highest-performing approaches, with accuracy above 95%, still require the creation of a small labeled dataset. These include the fine-tuned LVM DINOv2 and the LVLM LLaVA-1.5 combined with a fine-tuned LLM. The top fully unsupervised approaches, achieving accuracy above 84%, are the LVLMs, specifically the proprietary GPT-4 model and the public LLaVA-1.5 model. Additionally, the LVM DINOv2, when applied in a 10-shot learning setup, delivered competitive results with an accuracy of 83.99%, closely matching the performance of the LVLM LLaVA-1.5.

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