Benchmarking the Robustness of Semantic Segmentation Models
CVPR(2020)
摘要
When designing a semantic segmentation module for a practical application, such as autonomous driving, it is crucial to understand the robustness of the module with respect to a wide range of image corruptions. While there are recent robustness studies for full-image classification, we are the first to present an exhaustive study for semantic segmentation, based on the state-of-the-art model DeepLabv3+. To increase the realism of our study, we utilize almost 400,000 images generated from PASCAL VOC 2012, Cityscapes, and ADE20K. Based on the benchmark study, we gain several new insights. Firstly, model robustness increases with model performance, in most cases. Secondly, some architecture properties affect robustness significantly, such as a Dense Prediction Cell, which was designed to maximize performance on clean data only.
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关键词
Cityscapes,benchmark study,ADE20K,DeepLabv3+,PASCAL VOC 2012,full-image classification,image corruptions,autonomous driving,semantic segmentation module
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