PARSE-Ego4D: Personal Action Recommendation Suggestions for Egocentric Videos

TL;DR

This paper introduces PARSE-Ego4D, a new annotated dataset of personal action recommendations for egocentric videos, created through LLM-generated suggestions and extensive human evaluation, to advance action recommendation systems.

Contribution

The paper presents PARSE-Ego4D, a novel dataset with synthetic and human-annotated action suggestions for egocentric videos, enabling new research in personalized action recommendations.

Findings

Generated over 18,000 synthetic action suggestions using LLMs.

Conducted large-scale human annotation to validate and ground suggestions.

Proposed new tasks for improving action suggestion systems.

Abstract

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.