On Large Uni- and Multi-modal Models for Unsupervised Classification of Social Media Images: Nature's Contribution to People as case study
<|reference_start|>On Large Uni- and Multi-modal Models for Unsupervised Classification of Social Media Images: Nature's Contribution to People as case study: Social media images have shown 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 given the high diversity and complex nature of the visual content of these images in addition to their large volume. On the other hand, the last advances in Large Visual Models (LVM), Large Language Models (LLM) and Large Visual Language Models (LVLM) provide an important opportunity to explore new productive and scalable solutions. This works 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 pre-defined classes. As case study, we consider the problem of understanding the interactions between human and nature, also known as Nature's Contribution to People or Cultural Ecosystem Services (CES). Our experiments reveal that the top-performing approaches, delivering highly competitive results, are the fine-tuned LVM DINOv2 on a small labeled dataset and LVLM models like the proprietary GPT-4 (gpt-4o-mini) using a simple prompt.<|reference_end|>
@article{khaldi2024on,
    title={On Large Uni- and Multi-modal Models for Unsupervised Classification of
  Social Media Images: Nature's Contribution to People as a case study},
    author={Rohaifa Khaldi, Domingo Alcaraz-Segura, Ignacio S'anchez-Herrera,
  Javier Martinez-Lopez, Carlos Javier Navarro, Siham Tabik},
    journal={arXiv preprint arXiv:2410.00275},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.00275},
    primaryClass={cs.CV cs.AI}
}