  In this article, we introduce the solution we used in the VSPW 2021
Challenge. Our experiments are based on two baseline models, Swin Transformer
and MaskFormer. To further boost performance, we adopt stochastic weight
averaging technique and design hierarchical ensemble strategy. Without using
any external semantic segmentation dataset, our solution ranked the 5th place
in the private leaderboard. Besides, we have some interesting attempts to
tackle long-tail recognition and overfitting issues, which achieves improvement
on val subset. Maybe due to distribution difference, these attempts don't work
on test subset. We will also introduce these attempts and hope to inspire other
researchers.
