PARSE-Ego4D: Personal Action Recommendation Suggestions for Egocentric Videos
<|reference_start|>PARSE-Ego4D: Personal Action Recommendation Suggestions 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. 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.<|reference_end|>
@article{abreu2024parse-ego4d:,
    title={PARSE-Ego4D: Personal Action Recommendation Suggestions for Egocentric
  Videos},
    author={Steven Abreu, Tiffany D. Do, Karan Ahuja, Eric J. Gonzalez, Lee Payne,
  Daniel McDuff, Mar Gonzalez-Franco},
    journal={arXiv preprint arXiv:2407.09503},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.09503},
    primaryClass={cs.CV cs.HC cs.NE}
}