  Recently, the advancement of self-supervised learning techniques, like masked
autoencoders (MAE), has greatly influenced visual representation learning for
images and videos. Nevertheless, it is worth noting that the predominant
approaches in existing masked image / video modeling rely excessively on
resource-intensive vision transformers (ViTs) as the feature encoder. In this
paper, we propose a new approach termed as \textbf{VideoMAC}, which combines
video masked autoencoders with resource-friendly ConvNets. Specifically,
VideoMAC employs symmetric masking on randomly sampled pairs of video frames.
To prevent the issue of mask pattern dissipation, we utilize ConvNets which are
implemented with sparse convolutional operators as encoders. Simultaneously, we
present a simple yet effective masked video modeling (MVM) approach, a dual
encoder architecture comprising an online encoder and an exponential moving
average target encoder, aimed to facilitate inter-frame reconstruction
consistency in videos. Additionally, we demonstrate that VideoMAC, empowering
classical (ResNet) / modern (ConvNeXt) convolutional encoders to harness the
benefits of MVM, outperforms ViT-based approaches on downstream tasks,
including video object segmentation (+\textbf{5.2\%} / \textbf{6.4\%}
$\mathcal{J}\&\mathcal{F}$), body part propagation (+\textbf{6.3\%} /
\textbf{3.1\%} mIoU), and human pose tracking (+\textbf{10.2\%} /
\textbf{11.1\%} PCK@0.1).
