  We present EMBED (Egocentric Models Built with Exocentric Data), a method
designed to transform exocentric video-language data for egocentric video
representation learning. Large-scale exocentric data covers diverse activities
with significant potential for egocentric learning, but inherent disparities
between egocentric and exocentric data pose challenges in utilizing one view
for the other seamlessly. Egocentric videos predominantly feature close-up
hand-object interactions, whereas exocentric videos offer a broader perspective
on human activities. Additionally, narratives in egocentric datasets are
typically more action-centric and closely linked with the visual content, in
contrast to the narrative styles found in exocentric datasets. To address these
challenges, we employ a data transformation framework to adapt exocentric data
for egocentric training, focusing on identifying specific video clips that
emphasize hand-object interactions and transforming narration styles to align
with egocentric perspectives. By applying both vision and language style
transfer, our framework creates a new egocentric dataset derived from
exocentric video-language data. Through extensive evaluations, we demonstrate
the effectiveness of EMBED, achieving state-of-the-art results across various
egocentric downstream tasks, including an absolute improvement of 4.7% on the
Epic-Kitchens-100 multi-instance retrieval and 6.2% on the EGTEA classification
benchmarks in zero-shot settings. Furthermore, EMBED enables egocentric
video-language models to perform competitively in exocentric tasks. Finally, we
showcase EMBED's application across various exocentric datasets, exhibiting
strong generalization capabilities when applied to different exocentric
datasets.
