Benchmarking Multimodal Models for Fine-Grained Image Analysis: A Comparative Study Across Diverse Visual Features

TL;DR

This paper presents a comprehensive benchmark for evaluating multimodal models on fine-grained image analysis across seven visual aspects, using a large dataset to compare model performance and identify strengths and weaknesses.

Contribution

It introduces a new benchmark dataset and evaluation framework for assessing multimodal models' ability to analyze detailed visual features in images.

Findings

Different models excel at different visual aspects.

The benchmark reveals specific strengths and weaknesses of each model.

Results guide future development of more comprehensive multimodal image analysis models.

Abstract

This article introduces a benchmark designed to evaluate the capabilities of multimodal models in analyzing and interpreting images. The benchmark focuses on seven key visual aspects: main object, additional objects, background, detail, dominant colors, style, and viewpoint. A dataset of 14,580 images, generated from diverse text prompts, was used to assess the performance of seven leading multimodal models. These models were evaluated on their ability to accurately identify and describe each visual aspect, providing insights into their strengths and weaknesses for comprehensive image understanding. The findings of this benchmark have significant implications for the development and selection of multimodal models for various image analysis tasks.