Weaklier Supervised: Semi-automatic Scribble Generation Applied to Semantic Segmentation

  • João Pedro Klock Ferreira UFMG
  • João Paulo Lara Pinto UFMG
  • Cristiano Leite Castro UFMG


With many applications regarding semantic segmentation arising, along with the advent of the Deep Semantic Segmentation Networks, the need for large labeled datasets has also largely increased. But labeling thousands of images can be very expensive and time-consuming. Approaches such as weak and semi supervision try do deal with this problem, but the first cannot deal with large datasets and the latter is hard to deal with semantic segmentation. Therefore, in this work we propose a combination of both to create a novel pipeline of weak supervision, with focus in satellite imagery, capable of dealing with large datasets. We propose a pipeline to automatically generate scribbles in images, requiring that the user only label 10% of the images in a given dataset, while a classifier deal with the remaining images. Along with that, we also propose a simple semantic segmentation pipeline, that uses only images with scribbles to train a network. Results show that performance is lower, but similar to a fully supervised pipeline.


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FERREIRA, João Pedro Klock; PINTO, João Paulo Lara; CASTRO, Cristiano Leite. Weaklier Supervised: Semi-automatic Scribble Generation Applied to Semantic Segmentation. In: WORKSHOP DE TRABALHOS EM ANDAMENTO - CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI), 35. , 2022, Natal/RN. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2022 . p. 84-87. DOI: https://doi.org/10.5753/sibgrapi.est.2022.23266.