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March 19, 2026cs.CVcs.AIIntermediate
Em-Garde: A Propose-Match Framework for Proactive Streaming Video Understanding
Yikai Zheng, Xin Ding, Yifan Yang, Shiqi Jiang, Hao Wu, Qianxi Zhang, Weijun Wang, Ting Cao, Yunxin Liu
AI-Generated Summary
Em-Garde is a new system for understanding video streams in real-time that can proactively answer user questions about what's happening in videos. Instead of checking every frame to decide when to respond (which is slow and inaccurate), it converts user questions into visual search patterns and efficiently matches them against the incoming video stream, achieving better accuracy with less computational effort.
Difficulty
Intermediate
Categories
cs.CV, cs.AI
AI Tags
video understandingstreaming videoVideoLLMreal-time processingquery-based systems