Unsupervised video class incremental learning (uVCIL) represents an important learning paradigm for learning video information without forgetting, and without considering any data labels. Prior approaches have focused on supervised class-incremental learning, relying on using the knowledge of labels and task boundaries, which is costly, requires human annotation, or is simply not a realistic option. In this paper, we propose a simple yet effective approach to address the uVCIL. We first consider a deep feature extractor network, providing a set of representative video features during each task without assuming any class or task information. We then progressively build a series of deep clusters from the extracted features. During the successive task learning, the model updated from the previous task is used as an initial state in order to transfer knowledge to the current learning task. We perform in-depth evaluations on three standard video action recognition datasets, including UCF101, HMDB51, and Something-to-Something V2, by ignoring the labels from the supervised setting. Our approach significantly outperforms other baselines on all datasets.

翻译：无监督视频类增量学习（uVCIL）是一种重要的学习范式，它能在不考虑任何数据标签的情况下学习视频信息且不遗忘。先前的方法主要集中在有监督的类增量学习上，依赖于使用标签和任务边界的知识，这成本高昂、需要人工标注，或者根本不是一个现实的选择。在本文中，我们提出了一种简单而有效的方法来解决uVCIL问题。我们首先考虑一个深度特征提取网络，它在每个任务期间提供一组具有代表性的视频特征，且不假设任何类别或任务信息。然后，我们从提取的特征中逐步构建一系列深度聚类。在连续的任务学习过程中，将先前任务更新的模型用作初始状态，以便将知识迁移到当前学习任务。我们在三个标准的视频动作识别数据集（包括UCF101、HMDB51和Something-to-Something V2）上进行了深入评估，方法是忽略有监督设置中的标签。我们的方法在所有数据集上均显著优于其他基线方法。