  We propose Wolf, a WOrLd summarization Framework for accurate video
captioning. Wolf is an automated captioning framework that adopts a
mixture-of-experts approach, leveraging complementary strengths of Vision
Language Models (VLMs). By utilizing both image and video models, our framework
captures different levels of information and summarizes them efficiently. Our
approach can be applied to enhance video understanding, auto-labeling, and
captioning. To evaluate caption quality, we introduce CapScore, an LLM-based
metric to assess the similarity and quality of generated captions compared to
the ground truth captions. We further build four human-annotated datasets in
three domains: autonomous driving, general scenes, and robotics, to facilitate
comprehensive comparisons. We show that Wolf achieves superior captioning
performance compared to state-of-the-art approaches from the research community
(VILA1.5, CogAgent) and commercial solutions (Gemini-Pro-1.5, GPT-4V). For
instance, in comparison with GPT-4V, Wolf improves CapScore both quality-wise
by 55.6% and similarity-wise by 77.4% on challenging driving videos. Finally,
we establish a benchmark for video captioning and introduce a leaderboard,
aiming to accelerate advancements in video understanding, captioning, and data
alignment. Leaderboard: https://wolfv0.github.io/leaderboard.html.
