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The paper addresses the challenge of proactive activation in streaming video understanding, where a system must decide when to respond to incoming frames. They model proactive activation as a structured sequence modeling problem, leveraging the span-structured activation patterns inherent in temporal transitions. STRIDE, their proposed method, uses a masked diffusion module to jointly predict and iteratively refine activation signals within a sliding temporal window, leading to more reliable and temporally coherent responses.
Achieve more reliable and temporally coherent proactive responses in streaming video understanding by modeling activation signals jointly over a sliding temporal window with a masked diffusion module.
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.
