PARSE-Ego4D: Personal Action Recommendation Suggestions for Ego-Centric Videos

Steven Abreu, Tiffany D Do, Karan Ahuja, Eric J Gonzalez, Lee Payne, Daniel McDuff, Mar Gonzalez-Franco

Keywords: augmented reality, virtual reality, human annotation, recommender systems, perception, large language models

TL;DR: A dataset of human annotations for action suggestions in AR/VR systems based on the Ego4D dataset

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.

Primary Area: datasets and benchmarks

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Submission Number: 13959