VideoAutoArena: An Automated Arena for Evaluating Large Multimodal
  Models in Video Analysis through User Simulation
Authors: name: Ziyang Luo, name: Haoning Wu, name: Dongxu Li, name: Jing Ma, name: Mohan Kankanhalli, name: Junnan Li

Large multimodal models (LMMs) with advanced video analysis capabilities have
recently garnered significant attention. However, most evaluations rely on
traditional methods like multiple-choice questions in benchmarks such as
VideoMME and LongVideoBench, which are prone to lack the depth needed to
capture the complex demands of real-world users. To address this limitation-and
due to the prohibitive cost and slow pace of human annotation for video
tasks-we introduce VideoAutoArena, an arena-style benchmark inspired by LMSYS
Chatbot Arena's framework, designed to automatically assess LMMs' video
analysis abilities. VideoAutoArena utilizes user simulation to generate
open-ended, adaptive questions that rigorously assess model performance in
video understanding. The benchmark features an automated, scalable evaluation
framework, incorporating a modified ELO Rating System for fair and continuous
comparisons across multiple LMMs. To validate our automated judging system, we
construct a 'gold standard' using a carefully curated subset of human
annotations, demonstrating that our arena strongly aligns with human judgment
while maintaining scalability. Additionally, we introduce a fault-driven
evolution strategy, progressively increasing question complexity to push models
toward handling more challenging video analysis scenarios. Experimental results
demonstrate that VideoAutoArena effectively differentiates among
state-of-the-art LMMs, providing insights into model strengths and areas for
improvement. To further streamline our evaluation, we introduce VideoAutoBench
as an auxiliary benchmark, where human annotators label winners in a subset of
VideoAutoArena battles. We use GPT-4o as a judge to compare responses against
these human-validated answers. Together, VideoAutoArena and VideoAutoBench
offer a cost-effective, and scalable framework for evaluating LMMs in
user-centric video analysis.