Speeding up the annotation process in semantic segmentation industrial applications
Current machine learning models commonly require large and well-annotated datasets. However, the annotation process often becomes a bottleneck, with increased complexity leading to higher chances of human errors. Within this context, our goal in this paper is to leverage unsupervised algorithms to improve data annotation efficiency for complex semantic segmentation problems in industrial materials science. Previous research has quantified labeling time and others explored unsupervised methods. However, to the best of our knowledge, this is the first study to quantify how much unsupervised algorithms accelerate the labeling process. We aim to validate the extent to which this laborious pro...
| Authors | Marta Fernandez-Moreno, Margarita Guerrero, Rosalia Rementeria et al. |
|---|---|
| Primary category | cs.CV |
| Published | 18 June 2026 |
| Last updated at source | 18 June 2026 |
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