Semantic segmentation with less annotation efforts - Zhang, Tianyi (Nanyang Technological University, 2020)
Semantic segmentation is a pixel-wise classification task, which is to predict class label to every pixel within an image. However, one of the obstacles limiting the development of semantic segmentation is that the pixel-wise segmentation annotations for the training images are quite difficult and expensive to obtain. Thus, it is difficult to apply the segmentation methods to new datasets or semantic classes since it is labor-intensive to obtain new annotated data. The target of this thesis is to reduce the annotation work load for semantic segmentation tasks on images. From the view of data domain, the approaches could be roughly classified into intra-domain approaches and inter-domain approaches.