Reading-in-the-Wild-Columbus/CITATION.cff at main · AIoT-MLSys-Lab/Reading-in-the-Wild-Columbus · GitHub

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# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!

cff-version: 1.2.0
title: Reading Recognition in the Wild
message: 'If you use this software, please cite it as below.'
type: dataset
authors:
  - given-names: Charig
    family-names: Yang
  - given-names: Samiul
    family-names: Alam
    email: alam.140@osu.edu
    affiliation: OSU
    orcid: 'https://orcid.org/0000-0002-8458-4642'
  - given-names: Shakhrul Iman
    family-names: Siam
  - given-names: Michael J.
    family-names: Proulx
  - given-names: Lambert
    family-names: Mathias
  - given-names: Kiran
    family-names: Somasundaram
  - given-names: Luis
    family-names: Pesqueira
  - given-names: James
    family-names: Fort
  - given-names: Sheroze
    family-names: Sheriffdeen
  - given-names: Omkar
    family-names: Parkhi
  - given-names: Carl
    family-names: Ren
  - given-names: Mi
    family-names: Zhang
  - given-names: Yuning
    family-names: Chai
  - given-names: Richard
    family-names: Newcombe
  - given-names: Hyo Jin
    family-names: Kim
identifiers:
  - type: doi
    value: 10.48550/arXiv.2505.24848
repository-code: >-
  https://github.com/AIoT-MLSys-Lab/Reading-in-the-Wild-Columbu
url: 'https://www.projectaria.com/datasets/reading-in-the-wild/'
repository-artifact: 'https://huggingface.co/datasets/OSU-AIoT-MLSys-Lab'
abstract: >-
  To enable egocentric contextual AI in always-on smart
  glasses, it is crucial to be able to keep a record of the
  user's interactions with the world, including during
  reading. In this paper, we introduce a new task of reading
  recognition to determine when the user is reading. We
  first introduce the first-of-its-kind large-scale
  multimodal Reading in the Wild dataset, containing 100
  hours of reading and non-reading videos in diverse and
  realistic scenarios. We then identify three modalities
  (egocentric RGB, eye gaze, head pose) that can be used to
  solve the task, and present a flexible transformer model
  that performs the task using these modalities, either
  individually or combined. We show that these modalities
  are relevant and complementary to the task, and
  investigate how to efficiently and effectively encode each
  modality. Additionally, we show the usefulness of this
  dataset towards classifying types of reading, extending
  current reading understanding studies conducted in
  constrained settings to larger scale, diversity and
  realism.