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.

我们提出了一种多视角伪标记方法来进行视频学习，它利用外观和运动信息的互补视角进行半监督学习，以获取更可靠的伪标签并比纯监督数据学习更强的视频表示。我们的方法在多个视频识别数据集上大大优于其监督对应方法，并在自监督视频表示学习的标准基准测试中与先前的工作相比具有竞争优势，同时仍然训练一个共享外观和运动输入的模型，因此在推理时间不会产生额外的计算开销。