.png){kind=icon}
.png){kind=icon}
Return
Boundarymix: Generating pseudo-training images for improving segmentation with scribble annotations
.png){kind=icon}
PRE
AI
DOI：10.1016/j.patcog.2021.107924.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Weakly-supervised semantic segmentation, as a promising solution to alleviate the burden of collecting per-pixel annotations, aims to train a segmentation model from partial weak annotations. Scribble on the object is one of the commonly used weak annotations and has shown to be sufficient for learning a decent segmentation model. Despite being effective, scribble-based weakly-supervised learning methods often lead to imprecise segmentation on object boundaries. This is mainly because the scribble annotations usually locate inside the objects and the dataset lacks annotations close to the semantic boundaries. To alleviate this issue, this paper proposes a simple-but-effective solution, i.e., BoundaryMix, which generates pseudo training image-annotation pairs from the original images to supplement the missing semantic boundaries. Specifically, given a prediction of segmentation, we cut off the regions around the estimated boundaries, which are error-prone and replace them with the contents from another image, which in effect creates new samples with less ambiguity around semantic boundaries. With training on scribbles and the on-the-fly generated pseudo annotations, the network acquires better prediction capability around the boundary region and thus improves the overall segmentation performance. By conducting experiments on PASCAL VOC 2012 dataset and POTSDAM dataset with only scribble annotations, we demonstrate the excellent performance of the proposed method and the almost closed gap between scribble-supervised and fully-supervised image segmentation. (c) 2021 Elsevier Ltd. All rights reserved.
Keywords:
Weakly-supervised segmentation
Scribble
Boundary mix
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
7.6
Papers:
1.3W
Citations:
4.5W