Gemini vs GPT-4V: A Preliminary Comparison and Combination of Vision-Language Models Through Qualitative Cases (2312.15011v1)

Abstract: The rapidly evolving sector of Multi-modal LLMs (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.

Comparative Analysis of Vision-LLMs: Gemini and GPT-4V

This paper presents a comprehensive examination of two state-of-the-art vision-LLMs: Google's Gemini and OpenAI’s GPT-4V(ision). The paper evaluates these models across various dimensions, including their vision-language capabilities, human interaction, temporal understanding, and distinct visual comprehension skills. A series of structured experiments were conducted to assess their performance in multiple industrial application scenarios, offering insights into the models’ practical utility and integration potential.

Model Comparison and Performance

The evaluation reveals both strengths and limitations inherent in each model. GPT-4V excels in producing precise and succinct responses, presenting a high degree of accuracy in visual comprehension tasks. Conversely, Gemini is noted for generating detailed and expansive answers embellished with relevant images and links, enhancing its explanatory capabilities.

Image Recognition and Understanding

In basic image recognition scenarios involving object, landmark, food, and logo recognition, both models exhibit comparable performance. However, GPT-4V showcases a more robust ability when tasked with abstract image recognition. Scene understanding and counterfactual evaluation are tasks where both models adequately discern discrepancies, although Gemini's limitations in memory handling appear to affect its performance slightly.

Text Recognition and Reasoning

Both models demonstrate strong text extraction capabilities in multilingual and scene text recognition. However, when dealing with complex mathematical formulas and experimental table recognition, their proficiency declines. GPT-4V maintains an edge over Gemini in tasks requiring intricate reasoning, likely due to its advanced inferential algorithms.

Temporal Understanding and Multilingual Capabilities

GPT-4V outperforms Gemini in tasks involving the sequencing of events, benefiting from its coherent processing of temporal data. While both models display considerable abilities in multilingual tasks, recognizing and responding to varied language inputs effectively, GPT-4V still holds a slight advantage in scenario-based tasks.

Industrial Applications and Practical Implications

The analysis extends to speculating the role of these models in industry-specific applications, including defect detection, grocery checkout systems, and auto insurance evaluations. Gemini’s adeptness in providing image-rich feedback positions it as a potent tool in sectors demanding detailed visual inspections. However, GPT-4V’s precision solidifies its utility in contexts necessitating accurate data assimilation and succinct output, such as due diligence in financial and legal documentation analysis.

Conclusion and Future Directions

Both Gemini and GPT-4V stand as testaments to the advancements in vision-language integration within AI, each displaying unique attributes that cater to different user needs. The paper concludes by reflecting on the potential forthcoming entries, such as Gemini Ultra and GPT-4.5, which may further redefine the landscape of visual multimodal applications. Such developments promise to elevate model performance and broaden the scope of application in both academic and commercial sectors.

The paper advocates for a combined usage strategy, leveraging the preciseness of GPT-4V and the detailed expressiveness of Gemini, thus creating a more robust and versatile AI tool. As vision-LLMs continue to evolve, their integration into everyday applications will likely increase, demanding ongoing research to optimize their functionalities further for industry-specific challenges.