  Generic Event Boundary Detection (GEBD) tasks aim at detecting generic,
taxonomy-free event boundaries that segment a whole video into chunks. In this
paper, we apply Masked Autoencoders to improve algorithm performance on the
GEBD tasks. Our approach mainly adopted the ensemble of Masked Autoencoders
fine-tuned on the GEBD task as a self-supervised learner with other base
models. Moreover, we also use a semi-supervised pseudo-label method to take
full advantage of the abundant unlabeled Kinetics-400 data while training. In
addition, we propose a soft-label method to partially balance the positive and
negative samples and alleviate the problem of ambiguous labeling in this task.
Lastly, a tricky segmentation alignment policy is implemented to refine
boundaries predicted by our models to more accurate locations. With our
approach, we achieved 85.94% on the F1-score on the Kinetics-GEBD test set,
which improved the F1-score by 2.31% compared to the winner of the 2021
Kinetics-GEBD Challenge. Our code is available at
https://github.com/ContentAndMaterialPortrait/MAE-GEBD.
