arxiv preprint - Video-MME: The First-Ever Comprehensive Evaluation Benchmark of Multi-modal LLMs in Video Analysis

In this episode, we discuss Video-MME: The First-Ever Comprehensive Evaluation Benchmark of Multi-modal LLMs in Video Analysis by Chaoyou Fu, Yuhan Dai, Yondong Luo, Lei Li, Shuhuai Ren, Renrui Zhang, Zihan Wang, Chenyu Zhou, Yunhang Shen, Mengdan Zhang, Peixian Chen, Yanwei Li, Shaohui Lin, Sirui Zhao, Ke Li, Tong Xu, Xiawu Zheng, Enhong Chen, Rongrong Ji, Xing Sun. The paper introduces Video-MME, a comprehensive benchmark for evaluating Multi-modal Large Language Models (MLLMs) in video analysis, which assesses capabilities across diverse video types, durations, and data modalities with high-quality annotations. Their experiments show commercial models like Gemini 1.5 Pro outperform open-source counterparts and highlight the significant impact of subtitles and audio on video understanding, along with a noted drop in model performance with longer videos. The findings emphasize the need for improvements in handling extended sequences and multi-modal data, driving future advancements in MLLM capabilities.