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Xinlei Yin
University of Science and Technology of China
X
Xiulian Peng
Microsoft Research Asia
X
Xiao Li
Microsoft Research Asia
Z
Zhiwei Xiong
University of Science and Technology of China
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Founder's Pitch
"HAVEN: Enhance video comprehension with hierarchical indexing and multimodal cohesion for long-form video analysis."
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Why It Matters
Long-form video content is increasingly prevalent in entertainment, education, and surveillance sectors, yet existing automated systems struggle to maintain coherence and context over extended durations. HAVEN offers a structured method for understanding these videos comprehensively, promising to improve interaction and intelligence in video processing applications.
Product Angle
The product could be a SaaS platform for video analysis with applications in media, security, and digital archiving, employing HAVEN's framework to offer real-time insights and comprehensive summaries for long-duration videos.
Disruption
HAVEN could replace existing video analysis tools that rely on simpler techniques such as surface-level chunking and retrieval-augmented generation, potentially transforming video editing, summarization, and quality assurance processes in professional settings.
Product Opportunity
There is a growing market for video content analysis, especially with increasing demand for intelligent processing in streaming services, security surveillance, and education platforms. Enterprises in these domains will pay for improved efficiency and capabilities in video management.
Use Case Idea
A video analysis tool for the surveillance industry that automatically summarizes and indexes hours of footage, highlighting events and identifying relevant entities without losing context.
Science
HAVEN introduces a method for understanding long videos by structuring them hierarchically and integrating audiovisual entity cohesion. It organizes content into a hierarchy—global summary, scenes, segments, and entities—and employs an agentic search mechanism to dynamically retrieve relevant information, facilitating coherent narrative reconstruction and fine-grained tracking of entities over time.
Method & Eval
The paper details how HAVEN achieved state-of-the-art results in benchmarks like LVBench, with an 84.1% overall accuracy and 80.1% in reasoning tasks. Coherence and entity tracking were tested through structured reasoning over hierarchical video representations, demonstrating advances over existing approaches in both accuracy and efficiency.
Caveats
The deployment of HAVEN could face challenges in computational requirements due to its sophisticated hierarchical indexing and retrieval processes. Additionally, while high potential in theory, scalability, and integration into existing systems might require significant development effort.
Author Intelligence
Xinlei Yin
University of Science and Technology of China
Xiulian Peng
Microsoft Research Asia
Xiao Li
Microsoft Research Asia
Zhiwei Xiong
University of Science and Technology of China
Yan Lu
Microsoft Research Asia