Em-Garde: A Propose-Match Framework for Proactive Streaming Video Understanding
Fuente:
arXiv
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| Autori principali: | , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| Soggetti: | |
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| _version_ | 1866908901421613056 |
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| author | Zheng, Yikai Ding, Xin Yang, Yifan Jiang, Shiqi Wu, Hao Zhang, Qianxi Wang, Weijun Cao, Ting Liu, Yunxin |
| author_facet | Zheng, Yikai Ding, Xin Yang, Yifan Jiang, Shiqi Wu, Hao Zhang, Qianxi Wang, Weijun Cao, Ting Liu, Yunxin |
| contents | Recent advances in Streaming Video Understanding has enabled a new interaction paradigm where models respond proactively to user queries. Current proactive VideoLLMs rely on per-frame triggering decision making, which suffers from an efficiency-accuracy dilemma. We propose Em-Garde, a novel framework that decouples semantic understanding from streaming perception. At query time, the Instruction-Guided Proposal Parser transforms user queries into structured, perceptually grounded visual proposals; during streaming, a Lightweight Proposal Matching Module performs efficient embedding-based matching to trigger responses. Experiments on StreamingBench and OVO-Bench demonstrate consistent improvements over prior models in proactive response accuracy and efficiency, validating an effective solution for proactive video understanding under strict computational constraints. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_19054 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Em-Garde: A Propose-Match Framework for Proactive Streaming Video Understanding Zheng, Yikai Ding, Xin Yang, Yifan Jiang, Shiqi Wu, Hao Zhang, Qianxi Wang, Weijun Cao, Ting Liu, Yunxin Computer Vision and Pattern Recognition Artificial Intelligence Recent advances in Streaming Video Understanding has enabled a new interaction paradigm where models respond proactively to user queries. Current proactive VideoLLMs rely on per-frame triggering decision making, which suffers from an efficiency-accuracy dilemma. We propose Em-Garde, a novel framework that decouples semantic understanding from streaming perception. At query time, the Instruction-Guided Proposal Parser transforms user queries into structured, perceptually grounded visual proposals; during streaming, a Lightweight Proposal Matching Module performs efficient embedding-based matching to trigger responses. Experiments on StreamingBench and OVO-Bench demonstrate consistent improvements over prior models in proactive response accuracy and efficiency, validating an effective solution for proactive video understanding under strict computational constraints. |
| title | Em-Garde: A Propose-Match Framework for Proactive Streaming Video Understanding |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2603.19054 |