Examining the Commitments and Difficulties Inherent in Multimodal Foundation Models for Street View Imagery

TL;DR

This paper evaluates ChatGPT-4V and Gemini Pro on multimodal tasks across Street View, Built Environment, and Interior, revealing strengths in measurement and style analysis but limitations in detailed recognition and counting, highlighting their potential and challenges.

Contribution

It provides a comprehensive assessment of multimodal foundation models' capabilities and limitations across diverse real-world visual and textual tasks.

Findings

Proficient in length measurement and style analysis

Limitations in detailed recognition and counting tasks

Zero-shot learning shows potential but varies by domain

Abstract

The emergence of Large Language Models (LLMs) and multimodal foundation models (FMs) has generated heightened interest in their applications that integrate vision and language. This paper investigates the capabilities of ChatGPT-4V and Gemini Pro for Street View Imagery, Built Environment, and Interior by evaluating their performance across various tasks. The assessments include street furniture identification, pedestrian and car counts, and road width measurement in Street View Imagery; building function classification, building age analysis, building height analysis, and building structure classification in the Built Environment; and interior room classification, interior design style analysis, interior furniture counts, and interior length measurement in Interior. The results reveal proficiency in length measurement, style analysis, question answering, and basic image understanding, but highlight limitations in detailed recognition and counting tasks. While zero-shot learning shows potential, performance varies depending on the problem domains and image complexities. This study provides new insights into the strengths and weaknesses of multimodal foundation models for practical challenges in Street View Imagery, Built Environment, and Interior. Overall, the findings demonstrate foundational multimodal intelligence, emphasizing the potential of FMs to drive forward interdisciplinary applications at the intersection of computer vision and language.