StreamBridge: Turning Your Offline Video Large Language Model into a Proactive Streaming Assistant

• URL: http://arxiv.org/abs/2505.05467v1
• Date: Thu, 08 May 2025 17:57:40 GMT
• Title: StreamBridge: Turning Your Offline Video Large Language Model into a Proactive Streaming Assistant
• Authors: Haibo Wang, Bo Feng, Zhengfeng Lai, Mingze Xu, Shiyu Li, Weifeng Ge, Afshin Dehghan, Meng Cao, Ping Huang,
• Abstract summary: We present StreamBridge, a framework that seamlessly transforms offline Video-LLMs into streaming-capable models.<n>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.
• Score: 35.00546613484669
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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