STRIDE: When to Speak Meets Sequence Denoising for Streaming Video Understanding
Recent progress in video large language models (Video-LLMs) has enabled strong offline reasoning over long and complex videos. However, real-world deployments increasingly require streaming perception and proactive interaction, where video frames arrive online and the system must decide not only what to respond, but also when to respond. In this work, we revisit proactive activation in streaming video as a structured sequence modeling problem, motivated by the observation that temporal transitions in streaming video naturally form span-structured activation patterns. To capture this span-level structure, we model activation signals jointly over a sliding temporal window and update them iteratively as new frames arrive. We propose STRIDE (Structured Temporal Refinement with Iterative DEnoising), which employs a lightweight masked diffusion module at the activation interface to jointly predict and progressively refine activation signals across the window. Extensive experiments on diverse streaming benchmarks and downstream models demonstrate that STRIDE shows more reliable and temporally coherent proactive responses, significantly improving when-to-speak decision quality in online streaming scenarios.
69cb5e2d22f9e9dce3fe2163
Junho Kim
69cb5e2d22f9e9dce3fe2164
Hosu Lee
claimed_verified
2026-04-08T08:06:50.676Z
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/avatars/d4b1e23de90ff1d02c38186a259b8d1e.svg
Hosu Lee
lakelee
user
lakelee
69cb5e2d22f9e9dce3fe2165
James M. Rehg
69cb5e2d22f9e9dce3fe2166
Minsu Kim
69cb5e2d22f9e9dce3fe2167
Yong Man Ro
STRIDE: When to Speak Meets Sequence Denoising for Streaming Video Understanding
Recent progress in video large language models (Video-LLMs) has enabled strong offline reasoning over long and complex videos. However, real-world deployments increasingly require streaming perception and proactive interaction, where video frames arrive online and the system must decide not only what to respond, but also when to respond. In this work, we revisit proactive activation in streaming video as a structured sequence modeling problem, motivated by the observation that temporal transitions in streaming video naturally form span-structured activation patterns. To capture this span-level structure, we model activation signals jointly over a sliding temporal window and update them iteratively as new frames arrive. We propose STRIDE (Structured Temporal Refinement with Iterative DEnoising), which employs a lightweight masked diffusion module at the activation interface to jointly predict and progressively refine activation signals across the window. Extensive experiments on diverse streaming benchmarks and downstream models demonstrate that STRIDE shows more reliable and temporally coherent proactive responses, significantly improving when-to-speak decision quality in online streaming scenarios.
STRIDE enables proactive video understanding by modeling temporal activation patterns through iterative denoising within sliding windows, improving timing decisions in streaming scenarios.
video large language models streaming video proactive activation temporal transitions span-structured activation patterns sliding temporal window masked diffusion module iterative denoising temporal coherence when-to-speak decision