The KITTI Vision Benchmark Suite

Method

Unsupervised Domain Adaptation for Semantic Segmentation [at] [IfN-DomAdap-Seg]

Submitted on 25 Mar. 2019 09:41 by
Jan-Aike Bolte (Institut für Nachrichtentechnik - Technische Universität Braunschweig)

Running time:		1 s
Environment:		GPU @ 2.0 Ghz (Python)

Method Description:

This method uses an unsupervised domain
adaptation with a gradient reversal layer and a
domain discriminator network. The Segmentation
network was trained on labeled Cityscapes
images and unlableed images from KITTI and
BDD100K. The method improves the segmentation
results on the unlabeled target domains (KITTI
and BDD100K) and even on the labeled source
domain (Cityscapes).

Parameters:

None

Latex Bibtex:

@InProceedings{Bolte_2019_CVPR_Workshops,
author = {J.-A. Bolte and M. Kamp and A. Breuer
and S. Homoceanu and P. Schlicht and F. Hüger and
D. Lipinski and T. Fingscheidt},
title = {{Unsupervised Domain Adaptation to
Improve Image Segmentation Quality Both in the
Source and Target Domain}},
booktitle = {Proc. of CVPR - Workshops},
year = {2019},
address = {Long Beach, CA, USA},
month = jun,
}

Detailed Results

This page provides detailed results for the method(s) selected. For the first 20 test images, we display the original image, the color-coded result and an error image. The error image contains 4 colors:
red: the pixel has the wrong label and the wrong category
yellow: the pixel has the wrong label but the correct category
green: the pixel has the correct label
black: the groundtruth label is not used for evaluation

Test Set Average

IoU class	iIoU class	IoU category	iIoU category
59.50	30.28	81.57	61.91

Error

The KITTI Vision Benchmark Suite

A project of Karlsruhe Institute of Technologyand Toyota Technological Institute at Chicago

Method

Detailed Results

Test Set Average

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A project of Karlsruhe Institute of Technology
and Toyota Technological Institute at Chicago