Gemini vs GPT-4V：基于定性案例的视觉-语言模型的初步比较与结合
Gemini vs GPT-4V: A Preliminary Comparison and Combination of Vision-Language Models Through Qualitative Cases
AI总结：
本文通过多维度定性案例比较Gemini与GPT-4V的视觉语言能力，发现GPT-4V回答精确简洁、Gemini提供详尽答案并附图像链接，并尝试结合两者以提升效果。
摘要：
多模态大语言模型（MLLMs）的快速发展正处于人工智能中语言与视觉处理整合的前沿。本文对两个先驱模型进行了深入的比较研究：谷歌的Gemini和OpenAI的GPT-4V(ision)。我们的研究涉及对两个模型在关键维度上的多方面评估，如视觉-语言能力、与人类的交互、时间理解，以及智力和情商方面的评估。我们分析的核心深入探讨了每个模型独特的视觉理解能力。我们进行了一系列结构化实验，以评估它们在各种工业应用场景中的性能，提供了对其实际效用的全面视角。我们不仅进行直接的性能比较，还包括对提示和场景的调整，以确保平衡和公正的分析。我们的发现揭示了两个模型的独特优势和定位。GPT-4V以其在回答中的精确性和简洁性脱颖而出，而Gemini则擅长提供详细、扩展的答案，并附有相关的图像和链接。这些理解不仅阐明了Gemini和GPT-4V的比较优势，也强调了多模态基础模型不断演变的格局，为该领域的未来发展铺平了道路。在比较之后，我们尝试通过结合这两个模型来取得更好的结果。最后，我们衷心感谢GPT-4V和Gemini背后的团队在该领域的开创性贡献。我们同样感谢Yang等人在《Dawn》中呈现的全面定性分析。这项工作，凭借其大量的图像样本、提示和与GPT-4V相关的结果，为我们的分析提供了基础。
英文摘要：
The rapidly evolving sector of Multi-modal Large Language Models (MLLMs) is at the forefront of integrating linguistic and visual processing in artificial intelligence. This paper presents an in-depth comparative study of two pioneering models: Google's Gemini and OpenAI's GPT-4V(ision). Our study involves a multi-faceted evaluation of both models across key dimensions such as Vision-Language Capability, Interaction with Humans, Temporal Understanding, and assessments in both Intelligence and Emotional Quotients. The core of our analysis delves into the distinct visual comprehension abilities of each model. We conducted a series of structured experiments to evaluate their performance in various industrial application scenarios, offering a comprehensive perspective on their practical utility. We not only involve direct performance comparisons but also include adjustments in prompts and scenarios to ensure a balanced and fair analysis. Our findings illuminate the unique strengths and niches of both models. GPT-4V distinguishes itself with its precision and succinctness in responses, while Gemini excels in providing detailed, expansive answers accompanied by relevant imagery and links. These understandings not only shed light on the comparative merits of Gemini and GPT-4V but also underscore the evolving landscape of multimodal foundation models, paving the way for future advancements in this area. After the comparison, we attempted to achieve better results by combining the two models. Finally, We would like to express our profound gratitude to the teams behind GPT-4V and Gemini for their pioneering contributions to the field. Our acknowledgments are also extended to the comprehensive qualitative analysis presented in 'Dawn' by Yang et al. This work, with its extensive collection of image samples, prompts, and GPT-4V-related results, provided a foundational basis for our analysis.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
