Penalizing Top Performers: Conservative Loss for Semantic Segmentation Adaptation

ECCV 2018 Xinge ZhuHui ZhouCeyuan YangJianping ShiDahua Lin

Due to the expensive and time-consuming annotations (e.g., segmentation) for real-world images, recent works in computer vision resort to synthetic data. However, the performance on the real image often drops significantly because of the domain shift between the synthetic data and the real images... (read more)

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