Adversarial perturbations are noise-like patterns that can subtly change the
data, while failing an otherwise accurate classifier. In this paper, we propose
to use such perturbations within a novel contrastive learning setup to build
negative samples, which are then used to produce improved video
representations. To this end, given a well-trained deep model for per-frame
video recognition, we first generate adversarial noise adapted to this model.
Positive and negative bags are produced using the original data features from
the full video sequence and their perturbed counterparts, respectively. Unlike
the classic contrastive learning methods, we develop a binary classification
problem that learns a set of discriminative hyperplanes -- as a subspace --
that will separate the two bags from each other. This subspace is then used as
a descriptor for the video, dubbed \emph{discriminative subspace pooling}. As
the perturbed features belong to data classes that are likely to be confused
with the original features, the discriminative subspace will characterize parts
of the feature space that are more representative of the original data, and
thus may provide robust video representations. To learn such descriptors, we
formulate a subspace learning objective on the Stiefel manifold and resort to
Riemannian optimization methods for solving it efficiently. We provide
experiments on several video datasets and demonstrate state-of-the-art results.

此篇论文提出一种新的对比学习方法 —— 用对抗性扰动来构建负样本，以生产改进后的视频表征，并使用斯蒂费尔流形上的黎曼优化方法以实现子空间学习目标，最终产生具有较好鲁棒性的视频表征。