Domain Adaptation Semantic Segmentation

Vised adversarial domain adaptation methods only enforce embedding alignment in domain level instead of class level transfer.

Domain adaptation semantic segmentation. Semantic transfer is much easier in supervised domain adaptation as labeled target samples are available. 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. For example the source domain can consist of synthetic images and their cor responding pixel level labels semantic segmentation and. Many approaches 53 52 2 10 16 have been proposed.

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. Unfortunately finding models that generalize well or adapt to additional domains where data distribution is different remains a. 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 for semantic segmentation of urban scenes 1 the semantic understanding of urban scenes is one of the key components for an autonomous driving system. Semantic aware short path adversarial training for cross domain semantic segmentation neurocomputing 2019 weakly supervised adversarial domain adaptation for semantic segmentation in urban scenes. Via adversarial training or self training. Synthia dataset download the subset synthia rand cityscapes.

Lidar semantic segmentation provides 3d semantic information about the environment an essential cue for intelligent systems during their decision making processes. Cross city adaptation of road scene segmenters yu ting chen. There are mainly two ways to tackle this problem i e. Semantic segmentation unsupervised domain adaptation contextual relation consistent 1 introduction semantic segmentation has been a longstanding challenge in computer vision which aims to assign class labels to every pixel of an image 59.

While approaches based on convolutional neural networks constantly break new records on different benchmarks generalizing well to diverse testing environments remains a major challenge. Class conditional domain adaptation on semantic segmentation 27 nov 2019. Deep neural networks are achieving state of the art results on large public benchmarks on this task. Maximum classifier discrepancy for domain adaptation with semantic segmentation kuniaki saito.

Uda for semantic segmentation unsupervised domain adaptation for se mantic segmentation is the task that applies domain adaptation at pixel level. 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.