A Fully-Convolutional Framework for Semantic Segmentation

Yalong Jiang, Zheru Chi

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

4 Citations (Scopus)


In this paper we propose a deep learning technique to improve the performance of semantic segmentation tasks. Previously proposed algorithms generally suffer from the over-dependence on a single modality as well as a lack of training data. We made three contributions to improve the performance. Firstly, we adopt two models which are complementary in our framework to enrich field-of-views and features to make segmentation more reliable. Secondly, we repurpose the datasets form other tasks to the segmentation task by training the two models in our framework on different datasets. This brings the benefits of data augmentation while saving the cost of image annotation. Thirdly, the number of parameters in our framework is minimized to reduce the complexity of the framework and to avoid over- fitting. Experimental results show that our framework significantly outperforms the current state-of-the-art methods with a smaller number of parameters and better generalization ability.
Original languageEnglish
Title of host publicationDICTA 2017 - 2017 International Conference on Digital Image Computing
Subtitle of host publicationTechniques and Applications
Number of pages7
ISBN (Electronic)9781538628393
Publication statusPublished - 19 Dec 2017
Event2017 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2017 - Sydney, Australia
Duration: 29 Nov 20171 Dec 2017


Conference2017 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2017


  • Complement models
  • Deep learning
  • Neural network complexity
  • Over-fitting
  • Semantic segmentation

ASJC Scopus subject areas

  • Computer Science Applications
  • Signal Processing


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