Deep Video Discovery: Agentic Search with Tool Use for Long-form Video Understanding

• URL: http://arxiv.org/abs/2505.18079v2
• Date: Wed, 28 May 2025 08:30:39 GMT
• Title: Deep Video Discovery: Agentic Search with Tool Use for Long-form Video Understanding
• Authors: Xiaoyi Zhang, Zhaoyang Jia, Zongyu Guo, Jiahao Li, Bin Li, Houqiang Li, Yan Lu,
• Abstract summary: Long-form video understanding presents significant challenges due to extensive temporal-spatial complexity.<n>We propose the Deep Video Discovery agent to leverage an agentic search strategy over segmented video clips.<n>Our DVD agent achieves SOTA performance, significantly surpassing prior works by a large margin on the challenging LVBench dataset.
• Score: 63.82450803014141
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Infinite Video Understanding [50.78256932424239]
  We argue that framing Infinite Video Understanding as a blue-sky research objective provides a vital north star for the multimedia.<n>We outline the core challenges and key research directions towards achieving this transformative capability.
  arXiv  Detail & Related papers  (2025-07-11T23:07:04Z)
• VideoAgent2: Enhancing the LLM-Based Agent System for Long-Form Video Understanding by Uncertainty-Aware CoT [31.413204839972984]
  We propose a specialized chain-of-thought (CoT) process tailored for long video analysis.<n>Our uncertainty-aware CoT effectively mitigates noise from external tools, leading to more reliable outputs.<n>We implement our approach in a system called VideoAgent2, which also includes additional modules such as general context acquisition and specialized tool design.
  arXiv  Detail & Related papers  (2025-04-06T13:03:34Z)
• VideoRefer Suite: Advancing Spatial-Temporal Object Understanding with Video LLM [81.15525024145697]
  Video Large Language Models (Video LLMs) have recently exhibited remarkable capabilities in general video understanding.<n>However, they mainly focus on holistic comprehension and struggle with capturing fine-grained spatial and temporal details.<n>We introduce the VideoRefer Suite to empower Video LLM for finer-level spatial-temporal video understanding.
  arXiv  Detail & Related papers  (2024-12-31T18:56:46Z)
• SALOVA: Segment-Augmented Long Video Assistant for Targeted Retrieval and Routing in Long-Form Video Analysis [52.050036778325094]
  We introduce SALOVA: Segment-Augmented Video Assistant, a novel video-LLM framework designed to enhance the comprehension of lengthy video content.<n>We present a high-quality collection of 87.8K long videos, each densely captioned at the segment level to enable models to capture scene continuity and maintain rich context.<n>Our framework mitigates the limitations of current video-LMMs by allowing for precise identification and retrieval of relevant video segments in response to queries.
  arXiv  Detail & Related papers  (2024-11-25T08:04:47Z)
• OmAgent: A Multi-modal Agent Framework for Complex Video Understanding with Task Divide-and-Conquer [14.503628667535425]
  processing extensive videos presents significant challenges due to the vast data and processing demands. We develop OmAgent, efficiently stores and retrieves relevant video frames for specific queries. It features an Divide-and-Conquer Loop capable of autonomous reasoning. We have endowed it with greater autonomy and a robust tool-calling system, enabling it to accomplish even more intricate tasks.
  arXiv  Detail & Related papers  (2024-06-24T13:05:39Z)
• How Good is my Video LMM? Complex Video Reasoning and Robustness Evaluation Suite for Video-LMMs [98.37571997794072]
  We present the Complex Video Reasoning and Robustness Evaluation Suite (CVRR-ES) CVRR-ES comprehensively assesses the performance of Video-LMMs across 11 diverse real-world video dimensions. Our findings provide valuable insights for building the next generation of human-centric AI systems.
  arXiv  Detail & Related papers  (2024-05-06T17:59:45Z)
• MoVQA: A Benchmark of Versatile Question-Answering for Long-Form Movie Understanding [69.04413943858584]
  We introduce MoVQA, a long-form movie question-answering dataset. We also benchmark to assess the diverse cognitive capabilities of multimodal systems.
  arXiv  Detail & Related papers  (2023-12-08T03:33:38Z)
• Query-aware Long Video Localization and Relation Discrimination for Deep Video Understanding [15.697251303126874]
  Deep Video Understanding (DVU) Challenge aims to push the boundaries of multimodal extraction, fusion, and analytics. This paper introduces a query-aware method for long video localization and relation discrimination, leveraging an imagelanguage pretrained model. Our approach achieved first and fourth positions for two groups of movie-level queries.
  arXiv  Detail & Related papers  (2023-10-19T13:26:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.