基于视频游戏真实感增强的领域自适应语义分割

Kaito Nakajima, Takafumi Katayama, Tian Song, Xiantao Jiang, T. Shimamoto
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引用次数: 1

摘要

无监督域自适应被认为是减少大量监督数据的有效方法。为了解决这一问题,无监督域自适应被认为是一种有效的方法。本文通过引入三种类型的领域自适应:图像级领域自适应、域间自适应和域内自适应来达到更好的语义分割精度。该方法的平均欠条为45.0%。此外,通过将该方法与域内自适应相结合,与以前的工作相比,平均IoU提高了1.2%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Domain Adaptive Semantic Segmentation through Photorealistic Enhancement of Video Game
Unsupervised domain adaptation is considered as an effective technique to reduce the large amount supervised data. In order to solve this problem, unsupervised domain adaptation is considered to be an effective technique. In this work, three types of domain adaptation: image-level domain adaptation, inter-domain adaptation, and intra-domain adaptation are introduced to achieve better semantic segmentation accuracy. The proposed method achieved an mean IoU of 45.0%. Furthermore, by combining the proposed method with intra-domain adaptation, an mean IoU improvement of 1.2% is achieved compared to previous work.
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