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Talent Scout
H
Haozhe Qi
Microsoft Spatial AI Lab
K
Kevin Qu
ETH Zurich
M
Mahdi Rad
Microsoft Spatial AI Lab
R
Rui Wang
Microsoft Spatial AI Lab
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Founder's Pitch
"AdaptToken: Efficient token selection for MLLMs to enhance long video understanding by leveraging entropy for global control."
Commercial Viability Breakdown
0-10 scaleHigh Potential
2/4 signals
Quick Build
4/4 signals
Series A Potential
4/4 signals
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arXiv Paper
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Why It Matters
It improves the efficiency and accuracy of Multi-Modal Large Language Models (MLLMs) in processing long videos, crucial for developing applications like intelligent video analysis and AI assistants that need to manage large volumes of video data.
Product Angle
The approach can be packaged as an API or a plugin for existing MLLM architectures, enabling them to process longer videos with significant accuracy and efficiency gains.
Disruption
This solution could replace more resource-intensive video processing methods that struggle with context-length limitations and high memory overhead, particularly those methods currently limited by frame-by-frame processing inefficiencies.
Product Opportunity
Growing demand in the fields of surveillance, content creation, and virtual assistants. Enterprises managing and analyzing large video datasets would be primary customers seeking efficient and more accurate systems.
Use Case Idea
Develop a SaaS platform that offers efficient video analysis services that can be integrated into security systems for real-time surveillance monitoring or into video editing software to streamline content curation.
Science
AdaptToken introduces an innovative approach to selecting relevant tokens across video frames using entropy from model responses to gauge token importance globally. It allows early stopping in video processing, saving computational resources without heavily impacting performance, by interpreting uncertainty in prediction as a signal to allocate more processing to the least certain video clips.
Method & Eval
The method was tested on four benchmarks (VideoMME, LongVideoBench, LVBench, and MLVU) using different MLLMs with sizes ranging from 7B to 72B parameters. It improved average accuracy by 6.7 points over a baseline model and reduced inference time significantly with the Lite version.
Caveats
The framework's reliance on existing MLLMs' architecture means it may not capture new visual or contextual information if base models can't process it.
Author Intelligence
Haozhe Qi
LEAD Microsoft Spatial AI Lab
Kevin Qu
ETH Zurich
Mahdi Rad
Microsoft Spatial AI Lab
Rui Wang
Microsoft Spatial AI Lab
Alexander Mathis
EPFL
Marc Pollefeys
ETH Zurich