PARSE-Ego4D: Toward Bidirectionally Aligned Action Recommendations for Egocentric Videos

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. The suggestion of meaningful and valuable actions to users requires better alignment of the AI assistant to human preferences than for merely understanding user actions. To address this gap, we release PARSE-Ego4D, a novel set of personal action recommendation annotations for the Ego4D dataset. We generated over 18,000 context-aware action suggestions using a large language model and filtered these through 33,000 human annotations for AI-to-human alignment. We propose new benchmarks and emphasize personalization, adaptability, and efficiency for AI assistants in the real world. For AI assistants to be most useful, we argue that continual learning and adaptation of the AI to the human user - and vice versa - is necessary. The annotations in PARSE-Ego4D support researchers and developers working towards always-on, bi-directionally aligned action recommendation systems for augmented and virtual reality systems.