On the relevance of the metadata used in the semantic segmentation of indoor image spaces

作者：

Highlights：

• Quantitatively evaluate the usefulness of contextual information for a U-Net.

• Prove that learning systems rely heavily on contextual info for identification tasks.

• The Importance of Metadata Applied to Semantic Segmentation for Indoor Scenes.

• Deploying an efficient and robust FCN for Semantic Segmentation of Indoor Imagery.

摘要

•Quantitatively evaluate the usefulness of contextual information for a U-Net.•Prove that learning systems rely heavily on contextual info for identification tasks.•The Importance of Metadata Applied to Semantic Segmentation for Indoor Scenes.•Deploying an efficient and robust FCN for Semantic Segmentation of Indoor Imagery.

论文关键词：Deep learning,U-net,Semantic segmentation,Metadata preprocessing,Fully convolutional network,Indoor scenes

论文评审过程：Received 22 March 2020, Revised 14 June 2021, Accepted 24 June 2021, Available online 6 July 2021, Version of Record 14 July 2021.

论文官网地址：https://doi.org/10.1016/j.eswa.2021.115486