This paper presents Probabilistic Video Contrastive Learning, a
self-supervised representation learning method that bridges contrastive
learning with probabilistic representation. We hypothesize that the clips
composing the video have different distributions in short-term duration, but
can represent the complicated and sophisticated video distribution through
combination in a common embedding space. Thus, the proposed method represents
video clips as normal distributions and combines them into a Mixture of
Gaussians to model the whole video distribution. By sampling embeddings from
the whole video distribution, we can circumvent the careful sampling strategy
or transformations to generate augmented views of the clips, unlike previous
deterministic methods that have mainly focused on such sample generation
strategies for contrastive learning. We further propose a stochastic
contrastive loss to learn proper video distributions and handle the inherent
uncertainty from the nature of the raw video. Experimental results verify that
our probabilistic embedding stands as a state-of-the-art video representation
learning for action recognition and video retrieval on the most popular
benchmarks, including UCF101 and HMDB51.

本文提出了一种概率视频对比学习的自我监督表示学习方法，该方法基于混合高斯将视频剪辑呈现为正常分布并将它们组合成一个高斯分布混合物来建模整个视频分布，通过此方法我们可以避免对剪辑进行转换以生成增强剪辑视图的不必要策略，实验证明该方法在 UCF101 和 HMDB51 等最流行的基准测试上是最先进的视频表示学习方法。