The visual and audio modalities are highly correlated yet they contain different information. Their strong correlation makes it possible to predict the semantics of one from the other with good accuracy. Their intrinsic differences make cross-modal prediction a potentially more rewarding pretext task for self-supervised learning of video and audio representations compared to within-modality learning. Based on this intuition, we propose Cross-Modal Deep Clustering (XDC), a novel self-supervised method that leverages unsupervised clustering in one modality (e.g. audio) as a supervisory signal for the other modality (e.g. video). This cross-modal supervision helps XDC utilize the semantic correlation and the differences between the two modalities. Our experiments show that XDC significantly outperforms single-modality clustering and other multi-modal variants. Our XDC achieves state-of-the-art accuracy among self-supervised methods on several video and audio benchmarks including HMDB51, UCF101, ESC50, and DCASE. Most importantly, the video model pretrained with XDC significantly outperforms the same model pretrained with full-supervision on both ImageNet and Kinetics in action recognition on HMDB51 and UCF101. To the best of our knowledge, XDC is the first method to demonstrate that self-supervision outperforms large-scale full-supervision in representation learning for action recognition.

实现了基于交叉模态预测、自监督学习和深度聚类的方法，通过将一种模态的非监督聚类用作对另一种模态的监督信号来利用视觉和音频之间的语义相关性和差异，实现了在多个视频和音频数据集上优于其他方法的预训练模型，特别是通过仅使用大规模无标签数据预训练的视频模型，相比使用 ImageNet 和 Kinetics 数据进行了全监督预训练的同一架构，更显著地提高了在 HMDB51 和 UCF101 上的动作识别精度。

跨模态音视频聚类的自监督学习