StreamBridge: Turning Your Offline Video Large Language Model into a
  Proactive Streaming Assistant
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.
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Haibo Wang
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2025-05-09T07:21:20.829Z
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WANG HAIBO
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Bo Feng
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Zhengfeng Lai
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2025-05-09T09:00:45.264Z
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Zhengfeng Lai
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jefflai
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Mingze Xu
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2025-09-23T15:15:37.633Z
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Mingze Xu
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Shiyu Li
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2025-05-09T09:01:08.366Z
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Shiyu Li
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ShiyuLi
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Weifeng Ge
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Afshin Dehghan
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2025-05-09T09:01:21.716Z
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Afshin Dehghan
user
afshindn
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Meng Cao
681d95b5c7ae5f65b0e56001
Ping Huang
StreamBridge: Turning Your Offline Video Large Language Model into a
  Proactive Streaming Assistant
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.
StreamBridge is a framework that enhances offline Video-LLMs for streaming capabilities through a memory buffer with compression and a proactive response model, demonstrating superior performance on video understanding tasks.
Video-LLMs streaming-capable models multi-turn real-time understanding round-decayed compression strategy proactive response mechanisms interleaved video-text sequences Stream-IT dataset