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About this paper
Zhenghui Guo, Yuanbin Man, Junyuan Sheng, Bowen Lin, Ahmed Ahmed, Bo Jiang, Boyuan Zhang, Miao Yin and 3 more
Event-VStream groups incoming video frames into events using semantic change, motion, and prediction error, then updates persistent memory and generates text at detected boundaries. On OVOBench-Realtime, its reported aggregate is 28.15 versus 17.73 for VideoLLM-Online-8B under matched input conditions. Long-stream tests report an 88.3% final-segment GPT-5 win rate across four selected sequences, not unbounded streams.
Key insights
- Video content exhibits 'block-structured recurrence' in visual embeddings, where semantics remain stable within segments and change abruptly at boundaries, validating an event-centric processing approach.
- Designing VLM processing to align with human cognitive segmentation of continuous experience into discrete events leads to more efficient and coherent understanding.
- Integrating multiple complementary cues (semantic drift, motion, prediction error) is crucial for robust and accurate real-time detection of meaningful event boundaries.
Summary by alphaXiv.
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