Cross-Architecture Self-supervised Video Representation Learning
This work addresses the problem of learning robust video representations without labeled data for computer vision researchers, though it appears incremental as it builds on existing contrastive learning methods.
The paper tackles self-supervised video representation learning by introducing a cross-architecture contrastive learning framework and a temporal self-supervised module, achieving state-of-the-art performance on UCF101 and HMDB51 datasets for video retrieval and action recognition.
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.