StreamBridge: Turning Your Offline Video Large Language Model into a
  Proactive Streaming Assistant
Authors: name: Haibo Wang, name: Bo Feng, name: Zhengfeng Lai, name: Mingze Xu, name: Shiyu Li, name: Weifeng Ge, name: Afshin Dehghan, name: Meng Cao, name: Ping Huang

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.