ProactEgoMem: Benchmarking Proactive Memory in Streaming Egocentric Video
We introduce ProactEgoMem, a benchmark that evaluates proactive memory in streaming egocentric video.
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Key points
- Today's multimodal assistants are typically prompted to interpret the visual evidence presented to them.
- To offload the cognitive burden of remembering across long horizons, wearable assistants should instead proactively acquire and maintain useful memory as the user's surroundings evolve.
- Three tasks ground the evaluation in ambient environmental needs: Information Capture (IC) preserves transient yet dense visual information, State Tracking (ST) follows changing object states, and Location Tracking (LT) records object movements.
- ProactEgoMem provides a rigorous evaluation foundation for proactive multimodal assistance and for video agents that co-exist with their users.
Sources (1)
- [1]ProactEgoMem: Benchmarking Proactive Memory in Streaming Egocentric VideoarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 21, 05:37 PM
We introduce ProactEgoMem, a benchmark that evaluates proactive memory in streaming egocentric video.
Today's multimodal assistants are typically prompted to interpret the visual evidence presented to them.
Extractive summary: sentences quoted from the sources.
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