StreamMind: Unlocking Full Frame Rate Streaming Video Dialogue through Event-Gated Cognition

• URL: http://arxiv.org/abs/2503.06220v2
• Date: Fri, 28 Mar 2025 06:08:03 GMT
• Title: StreamMind: Unlocking Full Frame Rate Streaming Video Dialogue through Event-Gated Cognition
• Authors: Xin Ding, Hao Wu, Yifan Yang, Shiqi Jiang, Donglin Bai, Zhibo Chen, Ting Cao,
• Abstract summary: We introduce StreamMind, a video LLM framework that achieves ultra-FPS streaming video processing (100 fps on a single A100)<n>We propose a novel perception-cognition intertemporal paradigm named ''event-gated LLM invocation''<n> Experiments on Ego4D and SoccerNet streaming tasks, as well as standard offline benchmarks, demonstrate state-of-the-art performance in both model capability and real-time efficiency.
• Score: 19.54521322177521
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With the rise of real-world human-AI interaction applications, such as AI assistants, the need for Streaming Video Dialogue is critical. To address this need, we introduce StreamMind, a video LLM framework that achieves ultra-FPS streaming video processing (100 fps on a single A100) and enables proactive, always-on responses in real time, without explicit user intervention. To solve the key challenge of the contradiction between linear video streaming speed and quadratic transformer computation cost, we propose a novel perception-cognition interleaving paradigm named ''event-gated LLM invocation'', in contrast to the existing per-time-step LLM invocation. By introducing a Cognition Gate network between the video encoder and the LLM, LLM is only invoked when relevant events occur. To realize the event feature extraction with constant cost, we propose Event-Preserving Feature Extractor (EPFE) based on state-space method, generating a single perception token for spatiotemporal features. These techniques enable the video LLM with full-FPS perception and real-time cognition response. Experiments on Ego4D and SoccerNet streaming tasks, as well as standard offline benchmarks, demonstrate state-of-the-art performance in both model capability and real-time efficiency, paving the way for ultra-high-FPS applications, such as Game AI and interactive media. The code and data is available at https://aka.ms/StreamMind.

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