Summary of Videoautoarena: An Automated Arena For Evaluating Large Multimodal Models in Video Analysis Through User Simulation, by Ziyang Luo et al.
VideoAutoArena: An Automated Arena for Evaluating Large Multimodal Models in Video Analysis through User Simulation
by Ziyang Luo, Haoning Wu, Dongxu Li, Jing Ma, Mohan Kankanhalli, Junnan Li
First submitted to arxiv on: 20 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Multimedia (cs.MM)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Large multimodal models (LMMs) with advanced video analysis capabilities have recently garnered significant attention. 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. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about creating a new way to test how well big computer models can understand videos. Right now, people are using old methods that don’t fully capture what humans do when watching videos. To solve this problem, the researchers created an “arena” where these models have to answer open-ended questions based on the video content. The arena uses fake users to generate questions and compare how well different models do. This helps figure out which model is best at understanding videos. |
Keywords
» Artificial intelligence » Attention
