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Object understanding in egocentric visual data is arguably a fundamentalresearch topic in egocentric vision. However, existing object datasets areeither non-egocentric or have limitations in object categories, visual content,and annotation granularities. In this work, we introduce EgoObjects, alarge-scale egocentric dataset for fine-grained object understanding. Its Pilotversion contains over 9K videos collected by 250 participants from 50+countries using 4 wearable devices, and over 650K object annotations from 368object categories. Unlike prior datasets containing only object categorylabels, EgoObjects also annotates each object with an instance-levelidentifier, and includes over 14K unique object instances. EgoObjects wasdesigned to capture the same object under diverse background complexities,surrounding objects, distance, lighting and camera motion. In parallel to thedata collection, we conducted data annotation by developing a multi-stagefederated annotation process to accommodate the growing nature of the dataset.To bootstrap the research on EgoObjects, we present a suite of 4 benchmarktasks around the egocentric object understanding, including a novel instancelevel- and the classical category level object detection. Moreover, we alsointroduce 2 novel continual learning object detection tasks. The dataset andAPI are available at https://github.com/facebookresearch/EgoObjects.

EgoObjects: A Large-Scale Egocentric Dataset for Fine-Grained Object Understanding