MME-RealWorld: Could Your Multimodal LLM Challenge High-Resolution
  Real-World Scenarios that are Difficult for Humans?
Authors: name: Yi-Fan Zhang, name: Huanyu Zhang, name: Haochen Tian, name: Chaoyou Fu, name: Shuangqing Zhang, name: Junfei Wu, name: Feng Li, name: Kun Wang, name: Qingsong Wen, name: Zhang Zhang, name: Liang Wang, name: Rong Jin, name: Tieniu Tan

Comprehensive evaluation of Multimodal Large Language Models (MLLMs) has
recently garnered widespread attention in the research community. However, we
observe that existing benchmarks present several common barriers that make it
difficult to measure the significant challenges that models face in the real
world, including: 1) small data scale leads to a large performance variance; 2)
reliance on model-based annotations results in restricted data quality; 3)
insufficient task difficulty, especially caused by the limited image
resolution. To tackle these issues, we introduce MME-RealWorld. Specifically,
we collect more than 300K images from public datasets and the Internet,
filtering 13,366 high-quality images for annotation. This involves the
efforts of professional 25 annotators and 7 experts in MLLMs, contributing
to 29,429 question-answer pairs that cover 43 subtasks across 5
real-world scenarios, extremely challenging even for humans. As far as we know,
MME-RealWorld is the largest manually annotated benchmark to date, featuring
the highest resolution and a targeted focus on real-world applications. We
further conduct a thorough evaluation involving 28 prominent MLLMs, such as
GPT-4o, Gemini 1.5 Pro, and Claude 3.5 Sonnet. Our results show that even the
most advanced models struggle with our benchmarks, where none of them reach
60% accuracy. The challenges of perceiving high-resolution images and
understanding complex real-world scenarios remain urgent issues to be
addressed. The data and evaluation code are released at
https://mme-realworld.github.io/ .