Intelligent assistance involves not only understanding but also action. Existing ego-centric video datasets contain rich annotations of the videos, but not of actions that an intelligent assistant could perform in the moment. To address this gap, we release PARSE-Ego4D, a new set of personal action recommendation annotations for the Ego4D dataset. We take a multi-stage approach to generating and evaluating these annotations. First, we used a prompt-engineered large language model (LLM) to generate context-aware action suggestions and identified over 18,000 action suggestions. While these synthetic action suggestions are valuable, the inherent limitations of LLMs necessitate human evaluation. To ensure high-quality and user-centered recommendations, we conducted a large-scale human annotation study that provides grounding in human preferences for all of PARSE-Ego4D. We analyze the inter-rater agreement and evaluate subjective preferences of participants. Based on our synthetic dataset and complete human annotations, we propose several new tasks for action suggestions based on ego-centric videos. We encourage novel solutions that improve latency and energy requirements. The annotations in PARSE-Ego4D will support researchers and developers who are working on building action recommendation systems for augmented and virtual reality systems.

翻译：智能辅助不仅涉及理解，更关乎行动。现有的第一人称视频数据集虽包含丰富的视频标注，但缺乏对智能助手可即时执行行动的标注。为弥补这一空白，我们发布了PARSE-Ego4D——为Ego4D数据集构建的全新个人行动推荐标注集。我们采用多阶段方法生成并评估这些标注：首先通过提示工程优化的大语言模型生成情境感知的行动建议，识别出超过18,000项行动建议。尽管这些合成行动建议具有价值，但大语言模型的固有局限仍需人工评估。为确保高质量且以用户为中心的推荐，我们开展了大规模人工标注研究，为PARSE-Ego4D所有数据建立了基于人类偏好的基准。我们分析了标注者间一致性，并评估了参与者的主观偏好。基于合成数据集与完整人工标注，我们提出了若干基于第一人称视频的行动建议新任务，鼓励探索降低延迟与能耗的创新解决方案。PARSE-Ego4D的标注将助力研究人员与开发者构建适用于增强现实与虚拟现实系统的行动推荐系统。