Domain Adaptation For Semantic Segmentation

Semantic segmentation is a key problem for many computer vision tasks.

Domain adaptation for semantic segmentation. For example the source domain can consist of synthetic images and their cor responding pixel level labels semantic segmentation and. Learning to adapt structured output space for semantic segmentation wei chih hung. One reason is that annotating labels is an extremely high cost work. Unsupervised domain adaptation for semantic segmentation of urban scenes 1 the semantic understanding of urban scenes is one of the key components for an autonomous driving system.

Complex deep neural networks for this task require to be trained with a huge amount of labeled data which is difficult and expensive to acquire. Unsupervised domain adaptation uda refers to adapt ing a model trained with annotated samples from one dis tribution source to operate on a different target distribu tion for which no annotations are given. Unfortunately finding models that generalize well or adapt to additional domains where data distribution is different remains a. Unsupervised domain adaptation semantic segmentation with gans pdf.

All content in this area was uploaded by arpit jain on mar 22 2018. Synthia dataset download the subset synthia rand cityscapes. Unsupervised domain adaptation for semantic segmentation of nir images through generative latent search supplementary prashant pandey 0000 0002 6594 9685 aayush kumar tyagi. Contextual relation consistent domain adaptation for semantic segmentation jiaxing huang 1 0000 00028681 0471 shijian lu 6766 2506 dayan guan1 0000 0001 9752 1520 and xiaobing zhang2 0000 0002 8149 1424 1 nanyang technological university 50 nanyang avenue singapore 639798 fjiaxing huang shijian lu dayan guang ntu edu sg.

Cross city adaptation of road scene segmenters yu ting chen. While approaches based on convolutional neural networks constantly break new records on different benchmarks generalizing well to diverse testing environments remains a major challenge. Another reason is that the domain gap between the source and target domains limits the performance of semantic segmentation. Maximum classifier discrepancy for domain adaptation with semantic segmentation kuniaki saito.

Deep neural networks are achieving state of the art results on large public benchmarks on this task. In numerous real world applications there is indeed a large gap between data distributions in train and test domains which results in. Lidar semantic segmentation provides 3d semantic information about the environment an essential cue for intelligent systems during their decision making processes.