VLM-Eval: A General Evaluation on Video Large Language Models

• URL: http://arxiv.org/abs/2311.11865v1
• Date: Mon, 20 Nov 2023 16:02:10 GMT
• Title: VLM-Eval: A General Evaluation on Video Large Language Models
• Authors: Shuailin Li, Yuang Zhang, Yucheng Zhao, Qiuyue Wang, Fan Jia, Yingfei Liu, Tiancai Wang
• Abstract summary: We introduce a unified evaluation that encompasses multiple video tasks, including captioning, question and answering, retrieval, and action recognition. We propose a simple baseline: Video-LLaVA, which uses a single linear projection and outperforms existing video LLMs. We evaluate video LLMs beyond academic datasets, which show encouraging recognition and reasoning capabilities in driving scenarios with only hundreds of video-instruction pairs for fine-tuning.
• Score: 16.92780012093112
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Despite the rapid development of video Large Language Models (LLMs), a comprehensive evaluation is still absent. In this paper, we introduce a unified evaluation that encompasses multiple video tasks, including captioning, question and answering, retrieval, and action recognition. In addition to conventional metrics, we showcase how GPT-based evaluation can match human-like performance in assessing response quality across multiple aspects. We propose a simple baseline: Video-LLaVA, which uses a single linear projection and outperforms existing video LLMs. Finally, we evaluate video LLMs beyond academic datasets, which show encouraging recognition and reasoning capabilities in driving scenarios with only hundreds of video-instruction pairs for fine-tuning. We hope our work can serve as a unified evaluation for video LLMs, and help expand more practical scenarios. The evaluation code will be available soon.

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