Compared with image scene parsing, video scene parsing introduces temporal information, which can effectively improve the consistency and accuracy of prediction. In this paper, we propose a Spatial-Temporal Semantic Consistency method to capture class-exclusive context information. Specifically, we design a spatial-temporal consistency loss to constrain the semantic consistency in spatial and temporal dimensions. In addition, we adopt an pseudo-labeling strategy to enrich the training dataset. We obtain the scores of 59.84% and 58.85% mIoU on development (test part 1) and testing set of VSPW, respectively. And our method wins the 1st place on VSPW challenge at ICCV2021.
@article{arxiv.2109.02281,
title = {Exploiting Spatial-Temporal Semantic Consistency for Video Scene Parsing},
author = {Xingjian He and Weining Wang and Zhiyong Xu and Hao Wang and Jie Jiang and Jing Liu},
journal= {arXiv preprint arXiv:2109.02281},
year = {2021}
}
Comments
1st Place technical report for "The 1st Video Scene Parsing in the Wild Challenge" at ICCV2021