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Jan, 2019
基于合成和真实数据的课程模型自适应用于语义模糊场景理解
Curriculum Model Adaptation with Synthetic and Real Data for Semantic Foggy Scene Understanding
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Dengxin Dai, Christos Sakaridis, Simon Hecker, Luc Van Gool
TL;DR
提出一种名为CMAda的方法,它可以逐步地将语义分割模型从轻度合成雾适应到多个步骤中的浓雾,从而解决了在不良天气下对物体场景的语义识别问题,并可以将其扩展到其他恶劣条件中。
Abstract
This work addresses the problem of
semantic scene understanding
under
fog
. Although marked progress has been made in
semantic scene understanding
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