Exploring Boundary of GPT-4V on Marine Analysis: A Preliminary Case Study
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866913185671413760 |
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| author | Zheng, Ziqiang Chen, Yiwei Zhang, Jipeng Vu, Tuan-Anh Zeng, Huimin Tim, Yue Him Wong Yeung, Sai-Kit |
| author_facet | Zheng, Ziqiang Chen, Yiwei Zhang, Jipeng Vu, Tuan-Anh Zeng, Huimin Tim, Yue Him Wong Yeung, Sai-Kit |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_02147 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Exploring Boundary of GPT-4V on Marine Analysis: A Preliminary Case Study Zheng, Ziqiang Chen, Yiwei Zhang, Jipeng Vu, Tuan-Anh Zeng, Huimin Tim, Yue Him Wong Yeung, Sai-Kit Computation and Language Computer Vision and Pattern Recognition 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 |
| title | Exploring Boundary of GPT-4V on Marine Analysis: A Preliminary Case Study |
| topic | Computation and Language Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2401.02147 |