The Dawn of LMMs: A Preliminary Exploration of GPT-4V(ision): Multimodal General System Capabilities and Interaction Innovation
This paper systematically analyzes GPT-4V's capabilities in multimodal understanding, visual marker interaction, and arbitrary interleaved input processing, demonstrating its strong performance as a multimodal general system and proposing new human-computer interaction methods such as visual reference prompting.
Through carefully designed qualitative samples, this paper reveals GPT-4V's breakthrough capabilities in visual understanding, multimodal input processing, and visual marker interaction, marking a new stage for large multimodal models (LMMs) and providing an important foundation for general artificial intelligence.
The paper adopts a qualitative analysis method, evaluating GPT-4V's capabilities through cross-domain task samples. Core mechanisms include: arbitrary interleaved multimodal input processing, visual marker understanding (e.g., arrows, circles) enabling visual reference prompting. The evaluation does not provide quantitative metrics but demonstrates the model's generalization in complex scenarios. Limitations include reliance on OpenAI's closed-source model and limited sample size.
This research is a milestone for the AI industry, driving the evolution of multimodal models from single-task to general systems, potentially reshaping application paradigms in human-computer interaction, content generation, and decision support.
It is recommended that enterprises explore products based on GPT-4V's visual reference prompting interaction, such as intelligent design tools and educational assistance systems, and assess the integration costs with existing workflows.
Future work needs to validate GPT-4V's robustness in real-world scenarios, such as high-risk fields like medical image analysis and autonomous driving, as well as performance comparisons with open-source alternatives.