VideoMAC: Video Masked Autoencoders Meet ConvNets

• URL: http://arxiv.org/abs/2402.19082v1
• Date: Thu, 29 Feb 2024 12:09:25 GMT
• Title: VideoMAC: Video Masked Autoencoders Meet ConvNets
• Authors: Gensheng Pei, Tao Chen, Xiruo Jiang, Huafeng Liu, Zeren Sun, Yazhou Yao
• Abstract summary: VideoMAC employs symmetric masking on randomly sampled pairs of video frames. We present a simple yet effective masked video modeling (MVM) approach, a dual encoder architecture. VideoMAC, empowering classical (ResNet) / modern (ConvNeXt) convolutional encoders, outperforms ViT-based approaches on downstream tasks.
• Score: 26.723998063596635
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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).

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