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

Semantic Segmentation on VSPW Dataset through Contrastive Loss and Multi-dataset Training Approach