In this paper, we present a new cross-architecture contrastive learning (CACL) framework for self-supervised video representation learning. CACL consists of a 3D CNN and a video transformer which are used in parallel to generate diverse positive pairs for contrastive learning. This allows the model to learn strong representations from such diverse yet meaningful pairs. Furthermore, we introduce a temporal self-supervised learning module able to predict an Edit distance explicitly between two video sequences in the temporal order. This enables the model to learn a rich temporal representation that compensates strongly to the video-level representation learned by the CACL. We evaluate our method on the tasks of video retrieval and action recognition on UCF101 and HMDB51 datasets, where our method achieves excellent performance, surpassing the state-of-the-art methods such as VideoMoCo and MoCo+BE by a large margin. The code is made available at https://github.com/guoshengcv/CACL.

本文提出一种新的跨架构对比学习（CACL）框架，用于自监督视频表示学习，使用3D CNN和视频变换器并行生成对比学习中多样化的正对，同时引入了一个具有明确视频序列编辑距离预测能力的时间自监督学习模块，用于学习强大的时序性表示，在UCF101和HMDB51数据集上表现卓越，超越VideoMoCo和MoCo + BE等现有技术。

跨架构自监督视频表示学习