OmniMMI: A Comprehensive Multi-modal Interaction Benchmark in Streaming Video Contexts

• URL: http://arxiv.org/abs/2503.22952v1
• Date: Sat, 29 Mar 2025 02:46:58 GMT
• Title: OmniMMI: A Comprehensive Multi-modal Interaction Benchmark in Streaming Video Contexts
• Authors: Yuxuan Wang, Yueqian Wang, Bo Chen, Tong Wu, Dongyan Zhao, Zilong Zheng,
• Abstract summary: We introduce OmniMMI, a comprehensive multi-modal interaction benchmark tailored for OmniLLMs in streaming video contexts.<n>We propose a novel framework, Multi-modal Multiplexing Modeling (M4), designed to enable an inference-efficient streaming model that can see, listen while generating.
• Score: 46.77966058862399
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The rapid advancement of multi-modal language models (MLLMs) like GPT-4o has propelled the development of Omni language models, designed to process and proactively respond to continuous streams of multi-modal data. Despite their potential, evaluating their real-world interactive capabilities in streaming video contexts remains a formidable challenge. In this work, we introduce OmniMMI, a comprehensive multi-modal interaction benchmark tailored for OmniLLMs in streaming video contexts. OmniMMI encompasses over 1,121 videos and 2,290 questions, addressing two critical yet underexplored challenges in existing video benchmarks: streaming video understanding and proactive reasoning, across six distinct subtasks. Moreover, we propose a novel framework, Multi-modal Multiplexing Modeling (M4), designed to enable an inference-efficient streaming model that can see, listen while generating.

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