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W
Wenhui Tan
Renmin University of China
R
Ruihua Song
Renmin University of China
J
Jiaze Li
MiLM Plus, Xiaomi Inc.
J
Jianzhong Ju
Xiaomi Inc.
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Founder's Pitch
"Revolutionizing long-form video understanding with efficient frame selection through Think-Clip-Sample technology."
Commercial Viability Breakdown
Breakdown pending for this paper.
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Why It Matters
The advancement of video understanding technology is crucial as the amount of video content exponentially grows. Efficient processing of long-form videos enables applications like surveillance, video summarization, and real-time video analytics for various industries.
Product Angle
Develop a SaaS product that provides API access for enhanced video frame selection and understanding, allowing companies to integrate this technology into video analysis tools, improving efficiency and reducing computation costs.
Disruption
This technology could disrupt traditional video processing and surveillance methods by offering more efficient data analysis, reducing hardware costs associated with processing large video datasets.
Product Opportunity
The video analytics market is projected to reach $9 billion by 2027, driven by the demand for advanced video surveillance, content tagging, and retrieval solutions. Companies dealing with hours of video content would pay for more efficient processing methods.
Use Case Idea
A platform for video surveillance companies that improves the detection and reporting of critical events by processing video feeds more efficiently with enhanced frame sampling and understanding.
Science
The paper introduces Think-Clip-Sample (TCS), a method to enhance video understanding by efficiently selecting frames. TCS uses multi-query reasoning to generate diverse queries, and clip-level slow-fast sampling to allocate resources effectively, ensuring both detailed and global context capture in long-form videos.
Method & Eval
The paper evaluates the method on MLVU, LongVideoBench, and VideoMME benchmarks using two base MLLMs. It demonstrates up to 6.9% accuracy improvement and over 50% inference time reduction compared to existing methods, highlighting efficiency gains in long video understanding.
Caveats
The approach relies on the quality of multi-modal language models and may require further adaptation for different domains or video types. The computational cost, though reduced, still requires significant processing power.
Author Intelligence
Wenhui Tan
Renmin University of China
Ruihua Song
Renmin University of China
Jiaze Li
MiLM Plus, Xiaomi Inc.
Jianzhong Ju
Xiaomi Inc.
Zhenbo Luo
Xiaomi Inc.