Learning high-quality video representation has shown significant applications
in computer vision and remains challenging. Previous work based on mask
autoencoders such as ImageMAE and VideoMAE has proven the effectiveness of
learning representations in images and videos through reconstruction strategy
in the visual modality. However, these models exhibit inherent limitations,
particularly in scenarios where extracting features solely from the visual
modality proves challenging, such as when dealing with low-resolution and
blurry original videos. Based on this, we propose AV-MaskEnhancer for learning
high-quality video representation by combining visual and audio information.
Our approach addresses the challenge by demonstrating the complementary nature
of audio and video features in cross-modality content. Moreover, our result of
the video classification task on the UCF101 dataset outperforms the existing
work and reaches the state-of-the-art, with a top-1 accuracy of 98.8% and a
top-5 accuracy of 99.9%.

通过结合视听信息，我们提出了 AV-MaskEnhancer 方法来学习高质量的视频表示，解决了从低分辨率和模糊的原始视频中提取特征的挑战，并在 UCF101 数据集上的视频分类任务中取得了 98.8% 的 top-1 准确率和 99.9% 的 top-5 准确率，超越了现有工作并达到了最先进水平。