Machine learningDeep learning / NLP / CV

Multilingual Semantic Segmentation

Multilingual semantic segmentation is a pixel-level scene parsing approach that assigns a semantic class label to every pixel in an image while incorporating cross-lingual capabilities — enabling a single model to recognise scene-text elements, annotations, or training signals drawn from multiple languages. It combines deep encoder-decoder architectures with multilingual language representations, making it applicable to documents, street signs, natural scene images, and medical imagery across diverse linguistic contexts.

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Sources

  1. Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., & Adam, H. (2018). Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation. In Proceedings of ECCV 2018. link
  2. Image segmentation. Wikipedia. link

Related methods

ScholarGateMultilingual Semantic Segmentation (Multilingual Semantic Segmentation (Cross-Lingual Scene Parsing)). Retrieved 2026-06-04 from https://scholargate.app/tr/deep-learning/multilingual-semantic-segmentation