A Brief Survey on Weakly Supervised Semantic Segmentation

Authors

  • Youssef Ouassit Faculty of sciences ben m'sik , Hassan II University - Casablanca
  • Soufiane Ardchir National School of Management and Marketing , Hassan II University - Casablanca
  • Mohammed Yassine El Ghoumari National School of Management and Marketing , Hassan II University - Casablanca
  • Mohamed Azouazi Faculty of sciences ben m'sik , Hassan II University - Casablanca

DOI:

https://doi.org/10.3991/ijoe.v18i10.31531

Keywords:

Deep learning, Weakly supervision, Semantic segmentation, Computer vision

Abstract


Semantic Segmentation is the process of assigning a label to every pixel in the image that share same semantic properties and stays a challenging task in computer vision. In recent years, and due to the large availability of training data the performance of semantic segmentation has been greatly improved by using deep learning techniques. A large number of novel methods have been proposed. However, in some crucial fields we can't assure sufficient data to learn a deep model and achieves high accuracy. This paper aims to provide a brief survey of research efforts on deep-learning-based semantic segmentation methods on limited labeled data and focus our survey on weakly-supervised methods. This survey is expected to familiarize readers with the progress and challenges of weakly supervised semantic segmentation research in the deep learning era and present several valuable growing research points in this field.

Covr iJOE Vol. 18 No. 10 (2022)

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Published

2022-07-26

How to Cite

Ouassit, Y., Ardchir, S., El Ghoumari, M. Y., & Azouazi, M. (2022). A Brief Survey on Weakly Supervised Semantic Segmentation. International Journal of Online and Biomedical Engineering (iJOE), 18(10), pp. 83–113. https://doi.org/10.3991/ijoe.v18i10.31531

Issue

Vol. 18 No. 10 (2022)

Section

Papers

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Copyright (c) 2022 Youssef Ouassit, Soufiane Ardchir, Mohammed Yassine El Ghoumari, Mohamed Azouazi

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