Deep Video Discovery: Agentic Search with Tool Use for Long-form Video
  Understanding
Authors: name: Xiaoyi Zhang, name: Zhaoyang Jia, name: Zongyu Guo, name: Jiahao Li, name: Bin Li, name: Houqiang Li, name: Yan Lu

Long-form video understanding presents significant challenges due to
extensive temporal-spatial complexity and the difficulty of question answering
under such extended contexts. While Large Language Models (LLMs) have
demonstrated considerable advancements in video analysis capabilities and long
context handling, they continue to exhibit limitations when processing
information-dense hour-long videos. To overcome such limitations, we propose
the Deep Video Discovery agent to leverage an agentic search strategy over
segmented video clips. Different from previous video agents manually designing
a rigid workflow, our approach emphasizes the autonomous nature of agents. By
providing a set of search-centric tools on multi-granular video database, our
DVD agent leverages the advanced reasoning capability of LLM to plan on its
current observation state, strategically selects tools, formulates appropriate
parameters for actions, and iteratively refines its internal reasoning in light
of the gathered information. We perform comprehensive evaluation on multiple
long video understanding benchmarks that demonstrates the advantage of the
entire system design. Our DVD agent achieves SOTA performance, significantly
surpassing prior works by a large margin on the challenging LVBench dataset.
Comprehensive ablation studies and in-depth tool analyses are also provided,
yielding insights to further advance intelligent agents tailored for long-form
video understanding tasks. The code will be released later.