arXiv:2603.19054v2 Announce Type: replace-cross Abstract: Recent advances in Streaming Video Understanding has enabled a new interaction paradigm where models respond proactively to user queries. Current proactive VideoLLMs rely on per-frame triggering decision making, which suffers from an efficiency-accuracy dilemma. We propose Em-Garde, a novel framework that decouples semantic understanding from streaming perception. At query time, the Instruction-Guided Proposal Parser transforms user queries into structured, perceptually grounded visual proposals; during streaming, a Lightweight Proposal
The continuous improvement in AI models for video understanding demands more efficient and accurate processing of streaming data, pushing for innovative architectural solutions.
This development addresses a critical performance bottleneck in proactive AI systems, enabling more responsive and practical applications in real-world scenarios.
The proposed framework significantly improves the efficiency and accuracy of streaming video understanding by decoupling semantic processing from real-time perception, allowing AI systems to anticipate user needs more effectively.
- · AI developers
- · Video analytics companies
- · Surveillance and security sector
- · Inefficient video processing architectures
- · Systems reliant on per-frame decision making
Improved performance and responsiveness of AI models in streaming video applications.
Accelerated adoption of proactive AI systems across various industries due to enhanced reliability and lower computational overhead.
New interaction paradigms emerging from highly responsive, context-aware AI agents in daily life and industrial operations.
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Read at arXiv cs.AI