CapArena: Benchmarking and Analyzing Detailed Image Captioning in the
  LLM Era
Authors: name: Kanzhi Cheng, name: Wenpo Song, name: Jiaxin Fan, name: Zheng Ma, name: Qiushi Sun, name: Fangzhi Xu, name: Chenyang Yan, name: Nuo Chen, name: Jianbing Zhang, name: Jiajun Chen

Image captioning has been a longstanding challenge in vision-language
research. With the rise of LLMs, modern Vision-Language Models (VLMs) generate
detailed and comprehensive image descriptions. However, benchmarking the
quality of such captions remains unresolved. This paper addresses two key
questions: (1) How well do current VLMs actually perform on image captioning,
particularly compared to humans? We built CapArena, a platform with over 6000
pairwise caption battles and high-quality human preference votes. Our
arena-style evaluation marks a milestone, showing that leading models like
GPT-4o achieve or even surpass human performance, while most open-source models
lag behind. (2) Can automated metrics reliably assess detailed caption quality?
Using human annotations from CapArena, we evaluate traditional and recent
captioning metrics, as well as VLM-as-a-Judge. Our analysis reveals that while
some metrics (e.g., METEOR) show decent caption-level agreement with humans,
their systematic biases lead to inconsistencies in model ranking. In contrast,
VLM-as-a-Judge demonstrates robust discernment at both the caption and model
levels. Building on these insights, we release CapArena-Auto, an accurate and
efficient automated benchmark for detailed captioning, achieving 94.3%
correlation with human rankings at just $4 per test. Data and resources will be
open-sourced at https://caparena.github.io.