Importance-Aware Semantic Segmentation in Self-Driving with Discrete Wasserstein Training

Xiaofeng Liu, Yuzhuo Han, Song Bai, Yi Ge, Tianxing Wang, Site Li, Jia You, Jun Lu

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review


Semantic segmentation (SS) is an important perception manner for self-driving cars and robotics, which classifies each pixel into a pre-determined class. The widely-used cross entropy (CE) loss-based deep networks has achieved significant progress w.r.t. the mean Intersection-over Union (mIoU). However, the cross entropy loss can not take the different importance of each class in an self-driving system into account. For example, pedestrians in the image should be much more important than the surrounding buildings when make a decisions in the driving, so their segmentation results are expected to be as accurate as possible. In this paper, we propose to incorporate the importance-aware inter-class correlation in a Wasserstein training framework by configuring its ground distance matrix. The ground distance matrix can be pre-defined following a priori in a specific task, and the previous importance-ignored methods can be the particular cases. From an optimization perspective, we also extend our ground metric to a linear, convex or concave increasing function w.r.t. pre-defined ground distance. We evaluate our method on CamVid and Cityscapes datasets with different backbones (SegNet, ENet, FCN and Deeplab) in a plug and play fashion. In our extenssive experiments, Wasserstein loss demonstrates superior segmentation performance on the predefined critical classes for safe-driving.
Original languageEnglish
Title of host publicationProc. AAAI Conf. on Artificial Intelligence (AAAI-2020, New York, USA, 7-12 Feb., 2020),
PublisherAAAI press
VolumeVol 34 No 07: AAAI-20 Technical Tracks 7
EditionAAAI Technical Track: Vision
Publication statusPublished - Apr 2020


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