Exploring Boundary of GPT-4V on Marine Analysis: A Preliminary Case
  Study
Ziqiang Zheng Yiwei Chen Jipeng Zhang Tuan-Anh Vu Huimin Zeng Yue Him Wong Tim Sai-Kit Yeung
Large language models (LLMs) have demonstrated a powerful ability to answer
various queries as a general-purpose assistant. The continuous multi-modal
large language models (MLLM) empower LLMs with the ability to perceive visual
signals. The launch of GPT-4 (Generative Pre-trained Transformers) has
generated significant interest in the research communities. GPT-4V(ison) has
demonstrated significant power in both academia and industry fields, as a focal
point in a new artificial intelligence generation. Though significant success
was achieved by GPT-4V, exploring MLLMs in domain-specific analysis (e.g.,
marine analysis) that required domain-specific knowledge and expertise has
gained less attention. In this study, we carry out the preliminary and
comprehensive case study of utilizing GPT-4V for marine analysis. This report
conducts a systematic evaluation of existing GPT-4V, assessing the performance
of GPT-4V on marine research and also setting a new standard for future
developments in MLLMs. The experimental results of GPT-4V show that the
responses generated by GPT-4V are still far away from satisfying the
domain-specific requirements of the marine professions. All images and prompts
used in this study will be available at
https://github.com/hkust-vgd/Marine_GPT-4V_Eval