P³IBench
A benchmark for proactive video understanding in realistic streaming scenarios. It evaluates video-language models on three task categories: everyday perception, risk prediction and intervention, emphasizing calibration of response decisions and appropriate restraint.
Timeline (last 90 days)
The researchers proposed P³IBench, an active video understanding benchmark oriented toward real streaming scenarios, which splits proactive capability into three categories: daily perception, risk prediction, and intervention.
Interactive video QA is typically triggered by an explicit user question, whereas P³IBench requires the model to decide on its own whether to output when there is no explicit instruction, making the response decision itself an object of evaluation.
The study evaluated 12 representative open-source and proprietary video-language models, and the results show that current models generally struggle to make well-calibrated response decisions.
All Facts (3)
Interactive video QA is typically triggered by an explicit user question, whereas P³IBench requires the model to decide on its own whether to output when there is no explicit instruction, making the response decision itself an object of evaluation.
50%UnverifiedThe study evaluated 12 representative open-source and proprietary video-language models, and the results show that current models generally struggle to make well-calibrated response decisions.
50%UnverifiedThe researchers proposed P³IBench, an active video understanding benchmark oriented toward real streaming scenarios, which splits proactive capability into three categories: daily perception, risk prediction, and intervention.
50%