We present StreamBridge, a simple yet effective framework that seamlessly transforms offline Video-LLMs into streaming-capable models. It addresses two fundamental challenges in adapting existing models into online scenarios: (1) limited capability for multi-turn real-time understanding, and (2) lack of proactive response mechanisms. Specifically, StreamBridge incorporates (1) a memory buffer combined with a round-decayed compression strategy, supporting long-context multi-turn interactions, and (2) a decoupled, lightweight activation model that can be effortlessly integrated into existing Video-LLMs, enabling continuous proactive responses. To further support StreamBridge, we construct Stream-IT, a large-scale dataset tailored for streaming video understanding, featuring interleaved video-text sequences and diverse instruction formats. Extensive experiments show that StreamBridge significantly improves the streaming understanding capabilities of offline Video-LLMs across various tasks, outperforming even proprietary models such as GPT-4o and Gemini 1.5 Pro. Simultaneously, it achieves competitive or superior performance on standard video understanding benchmarks.

本文提出了StreamBridge，这一框架有效地将离线视频大语言模型转变为可进行流媒体处理的模型，解决了现有模型在在线场景下的多轮实时理解能力有限和缺乏主动响应机制的问题。通过引入记忆缓冲和轻量级激活模型，StreamBridge显著提升了离线视频大语言模型在流媒体理解任务中的表现，并在规模化的数据集Stream-IT上的实验中超越了诸如GPT-4o和Gemini 1.5 Pro等专有模型。

StreamBridge：将离线视频大语言模型转变为主动流媒体助手