5th Place Solution for VSPW 2021 Challenge
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.
PDF AbstractTasks
Datasets
Results from the Paper
Submit
results from this paper
to get state-of-the-art GitHub badges and help the
community compare results to other papers.
Methods
Absolute Position Encodings •
Adam •
BPE •
Dense Connections •
Dropout •
Label Smoothing •
Layer Normalization •
Linear Layer •
Multi-Head Attention •
Position-Wise Feed-Forward Layer •
Residual Connection •
Scaled Dot-Product Attention •
Softmax •
Stochastic Depth •
Stochastic Weight Averaging •
Swin Transformer •
Transformer
