Reading: DRRN, Zhang JNCA’20 — Deep Recursive Residual Network (Semantic Segmentation)

Original article was published on Artificial Intelligence on Medium

Reading: DRRN, Zhang JNCA’20 — Deep Recursive Residual Network (Semantic Segmentation)

Recursive Blocks to Improve Prediction of FCN While Reducing Parameters, Also Enhanced Mask R-CNN

In this story, Deep Recursive Residual Network for Image Semantic Segmentation (DRRN, Zhang JNCAA’20), is briefly presented. In this paper:

  • DRRN for image semantic segmentation is proposed.
  • Recursive blocks are introduced in Residual Block (ResNet).
  • A concatenation layer is utilized to combine each output map of the recursive convolution layers at different iteration with same resolution so that different field-of-views of feature maps can be gathered.

This is a journal paper published in 2020 JNCA (Journal of Network and Computer Applications) with high impact factor of 5.273. (Sik-Ho Tsang @ Medium)