Domain Adaptation For Semantic Segmentation
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While approaches based on convolutional neural networks constantly break new records on different benchmarks generalizing well to diverse testing environments remains a major challenge.
Domain adaptation for semantic segmentation. Unsupervised domain adaptation for semantic segmentation of nir images through generative latent search supplementary prashant pandey 0000 0002 6594 9685 aayush kumar tyagi. Content uploaded by arpit jain. All content in this area was uploaded by arpit jain on mar 22 2018.
Maximum classifier discrepancy for domain adaptation with semantic segmentation kuniaki saito. Domain adaptation for semantic segmentation is a challenging problem for two reasons. For example the source domain can consist of synthetic images and their cor responding pixel level labels semantic segmentation and.
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. 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. Lidar semantic segmentation provides 3d semantic information about the environment an essential cue for intelligent systems during their decision making processes.
Separated semantic feature based domain adaptation network for semantic segmentation liang du 1 jingang tan1 hongye yang1 jianfeng feng2 xiangyang xue3 qibao zheng2 xiaoqing ye4 and xiaolin zhang1 5 1bionic vision system laboratory state key laboratory of transducer technology shanghai institute of microsystem and information technology chinese academy of sciences shanghai. Deep neural networks are achieving state of the art results on large public benchmarks on this task. Learning to adapt structured output space for semantic segmentation wei chih hung.
Cross city adaptation of road scene segmenters yu ting chen. Unfortunately finding models that generalize well or adapt to additional domains where data distribution is different remains a. Another reason is that the domain gap between the source and target domains limits the performance of semantic segmentation.
In numerous real world applications there is indeed a large gap between data distributions in train and test domains which results in. Semantic segmentation is a key problem for many computer vision tasks. Unsupervised domain adaptation semantic segmentation with gans pdf.
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