  Recently, with the rise of web videos, managing and understanding large-scale
video datasets has become increasingly important. Video Large Language Models
(VideoLLMs) have emerged in recent years due to their strong video
understanding capabilities. However, training and inference processes for
VideoLLMs demand vast amounts of data, presenting significant challenges to
data management, particularly regarding efficiency, robustness, and
effectiveness. In this work, we present KeyVideoLLM, a text-video frame
similarity-based keyframe selection method designed to manage VideoLLM data
efficiently, robustly, and effectively. Specifically, KeyVideoLLM achieves a
remarkable data compression rate of up to 60.9 times, substantially lowering
disk space requirements, which proves its high efficiency. Additionally, it
maintains a 100% selection success rate across all video formats and scales,
enhances processing speed by up to 200 times compared to existing keyframe
selection methods, and does not require hyperparameter tuning. Beyond its
outstanding efficiency and robustness, KeyVideoLLM further improves model
performance in video question-answering tasks during both training and
inference stages. Notably, it consistently achieved the state-of-the-art (SoTA)
experimental results on diverse datasets.
