  We present a multiview pseudo-labeling approach to video learning, a novel
framework that uses complementary views in the form of appearance and motion
information for semi-supervised learning in video. The complementary views help
obtain more reliable pseudo-labels on unlabeled video, to learn stronger video
representations than from purely supervised data. Though our method capitalizes
on multiple views, it nonetheless trains a model that is shared across
appearance and motion input and thus, by design, incurs no additional
computation overhead at inference time. On multiple video recognition datasets,
our method substantially outperforms its supervised counterpart, and compares
favorably to previous work on standard benchmarks in self-supervised video
representation learning.
