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QuickGrasp: Responsive Video-Language Querying Service via Accelerated Tokenization and Edge-Augmented Inference
arXiv – CS AI|Miao Zhang, Ruixiao Zhang, Jianxin Shi, Hengzhi Wang, Hao Fang, Jiangchuan Liu||7 views
🤖AI Summary
Researchers propose QuickGrasp, a video-language querying system that combines local processing with edge computing to achieve both fast response times and high accuracy. The system achieves up to 12.8x reduction in response delay while maintaining the accuracy of large video-language models through accelerated tokenization and adaptive edge augmentation.
Key Takeaways
- →QuickGrasp addresses the trade-off between speed and accuracy in video-language model deployment through a local-first architecture with edge augmentation.
- →The system achieves up to 12.8x reduction in response delay while maintaining accuracy comparable to large VLMs.
- →Three key innovations include accelerated video tokenization, query-adaptive edge augmentation, and delay-aware token density configuration.
- →The modular architecture shares vision representations across model variants to avoid redundant computation.
- →This represents a significant advancement toward responsive video querying services for real-world applications.
#video-language-models#edge-computing#tokenization#inference-optimization#computer-vision#machine-learning#arxiv-research#system-architecture#performance-optimization
Read Original →via arXiv – CS AI
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