# 5th Place Solution for VSPW 2021 Challenge

Source: [5th Place Solution for VSPW 2021 Challenge](<https://www.sota2.com/research/paper/68fa6b6c1e209131cea2e404>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T23:49:06.844Z

Authors: Jiafan Zhuang, Yixin Zhang, Xinyu Hu, Junjie Li, Zilei Wang

Published: 2021-12-13T02:27:05.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2112.06379>)

[PDF](<https://arxiv.org/pdf/2112.06379.pdf>)