Semantic scene understanding is an essential task for self-driving vehicles
and mobile robots. In our work, we aim to estimate a semantic segmentation map,
in which the foreground objects are removed and semantically inpainted with
background classes, from a single RGB image. This semantic foreground
inpainting task is performed by a single-stage convolutional neural network
(CNN) that contains our novel max-pooling as inpainting (MPI) module, which is
trained with weak supervision, i.e., it does not require manual background
annotations for the foreground regions to be inpainted. Our approach is
inherently more efficient than the previous two-stage state-of-the-art method,
and outperforms it by a margin of 3% IoU for the inpainted foreground regions
on Cityscapes. The performance margin increases to 6% IoU, when tested on the
unseen KITTI dataset. The code and the manually annotated datasets for testing
are shared with the research community at
this https URL

本文介绍了使用一种新型的卷积神经网络（MPI）完成语义场景理解中的语义前景修复任务的方法，该方法不需要手动标注背景，并且其比之前最先进的两阶段方法提高了 3% 的 IoU，并在 Cityscapes 测试中表现得更有效率。