EgoObjects: A Large-Scale Egocentric Dataset for Fine-Grained Object
  Understanding
Chenchen Zhu Fanyi Xiao Andres Alvarado Yasmine Babaei Jiabo Hu Hichem El-Mohri Sean Chang Culatana Roshan Sumbaly Zhicheng Yan
Object understanding in egocentric visual data is arguably a fundamental
research topic in egocentric vision. However, existing object datasets are
either non-egocentric or have limitations in object categories, visual content,
and annotation granularities. In this work, we introduce EgoObjects, a
large-scale egocentric dataset for fine-grained object understanding. Its Pilot
version contains over 9K videos collected by 250 participants from 50+
countries using 4 wearable devices, and over 650K object annotations from 368
object categories. Unlike prior datasets containing only object category
labels, EgoObjects also annotates each object with an instance-level
identifier, and includes over 14K unique object instances. EgoObjects was
designed to capture the same object under diverse background complexities,
surrounding objects, distance, lighting and camera motion. In parallel to the
data collection, we conducted data annotation by developing a multi-stage
federated annotation process to accommodate the growing nature of the dataset.
To bootstrap the research on EgoObjects, we present a suite of 4 benchmark
tasks around the egocentric object understanding, including a novel instance
level- and the classical category level object detection. Moreover, we also
introduce 2 novel continual learning object detection tasks. The dataset and
API are available at https://github.com/facebookresearch/EgoObjects.