The Dawn of LMMs: Preliminary Explorations with GPT-4V(ision)

TL;DR

This paper explores GPT-4V(ision), a large multimodal model, analyzing its capabilities, input modes, and potential applications, highlighting its unprecedented multimodal processing and generalist abilities for future research and real-world tasks.

Contribution

The paper provides a comprehensive qualitative analysis of GPT-4V(ision), revealing its multimodal generality, input flexibility, and potential for new human-computer interaction methods.

Findings

GPT-4V can process arbitrarily interleaved multimodal inputs.

GPT-4V demonstrates strong capabilities across diverse tasks and domains.

Visual marker understanding enables new interaction methods.

Abstract

Large multimodal models (LMMs) extend large language models (LLMs) with multi-sensory skills, such as visual understanding, to achieve stronger generic intelligence. In this paper, we analyze the latest model, GPT-4V(ision), to deepen the understanding of LMMs. The analysis focuses on the intriguing tasks that GPT-4V can perform, containing test samples to probe the quality and genericity of GPT-4V's capabilities, its supported inputs and working modes, and the effective ways to prompt the model. In our approach to exploring GPT-4V, we curate and organize a collection of carefully designed qualitative samples spanning a variety of domains and tasks. Observations from these samples demonstrate that GPT-4V's unprecedented ability in processing arbitrarily interleaved multimodal inputs and the genericity of its capabilities together make GPT-4V a powerful multimodal generalist system. Furthermore, GPT-4V's unique capability of understanding visual markers drawn on input images can give rise to new human-computer interaction methods such as visual referring prompting. We conclude the report with in-depth discussions on the emerging application scenarios and the future research directions for GPT-4V-based systems. We hope that this preliminary exploration will inspire future research on the next-generation multimodal task formulation, new ways to exploit and enhance LMMs to solve real-world problems, and gaining better understanding of multimodal foundation models. Finally, we acknowledge that the model under our study is solely the product of OpenAI's innovative work, and they should be fully credited for its development. Please see the GPT-4V contributions paper for the authorship and credit attribution: https://cdn.openai.com/contributions/gpt-4v.pdf