  Semantic segmentation is a challenging computer vision task demanding a
significant amount of pixel-level annotated data. Producing such data is a
time-consuming and costly process, especially for domains with a scarcity of
experts, such as medicine or forensic anthropology. While numerous
semi-supervised approaches have been developed to make the most from the
limited labeled data and ample amount of unlabeled data, domain-specific
real-world datasets often have characteristics that both reduce the
effectiveness of off-the-shelf state-of-the-art methods and also provide
opportunities to create new methods that exploit these characteristics. We
propose and evaluate a semi-supervised method that reuses available labels for
unlabeled images of a dataset by exploiting existing similarities, while
dynamically weighting the impact of these reused labels in the training
process. We evaluate our method on a large dataset of human decomposition
images and find that our method, while conceptually simple, outperforms
state-of-the-art consistency and pseudo-labeling-based methods for the
segmentation of this dataset. This paper includes graphic content of human
decomposition.
