Video Masked

Video masked autoencoders (MAE) are a rapidly developing area of self-supervised learning focused on efficiently learning robust video representations from unlabeled data. Current research emphasizes improving masking strategies, often incorporating motion information or textual descriptions to guide the masking process, and exploring the synergy between video MAE and other modalities like audio, leveraging cross-modal information for enhanced representation learning. These advancements are significantly improving performance on various downstream tasks, such as action recognition, video object segmentation, and audio-visual classification, demonstrating the potential of video MAE for building powerful and generalizable video foundation models.