  Video scene parsing incorporates temporal information, which can enhance the
consistency and accuracy of predictions compared to image scene parsing. The
added temporal dimension enables a more comprehensive understanding of the
scene, leading to more reliable results. This paper presents the winning
solution of the CVPR2023 workshop for video semantic segmentation, focusing on
enhancing Spatial-Temporal correlations with contrastive loss. We also explore
the influence of multi-dataset training by utilizing a label-mapping technique.
And the final result is aggregating the output of the above two models. Our
approach achieves 65.95% mIoU performance on the VSPW dataset, ranked 1st place
on the VSPW challenge at CVPR 2023.
