Semantic Segmentation on VSPW Dataset through Contrastive Loss and Multi-dataset Training Approach
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 11 of its 14 references that are in the map. First posted on arXiv 6 Jun 2023.
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11 of 14 references.
References · 11
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