Exploiting Spatial-Temporal Semantic Consistency for Video Scene Parsing

• URL: http://arxiv.org/abs/2109.02281v1
• Date: Mon, 6 Sep 2021 08:24:38 GMT
• Title: Exploiting Spatial-Temporal Semantic Consistency for Video Scene Parsing
• Authors: Xingjian He, Weining Wang, Zhiyong Xu, Hao Wang, Jie Jiang, Jing Liu
• Abstract summary: 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. Our method wins the 1st place on VSPW challenge at ICCV 2021.
• Score: 11.848929625911575
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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