Original article was published by Sik-Ho Tsang on Artificial Intelligence on Medium
Reading: C3 — Concentrated-Comprehensive Convolution (Semantic Segmentation)
Compared to ESPNet, ERFNet, DRN & ENet, Similar or Improved mIOU Achieved While Obtaining Smaller model sizes and fewer number of FLOPs
In this story, Concentrated-Comprehensive Convolution (C3), by Seoul National University, and CLOVA AI Research, Naver Corp., is shortly presented. In this paper:
- A new block called Concentrated-Comprehensive Convolution (C3) which applies the asymmetric convolutions before the depth-wise separable dilated convolution to compensate for the information loss due to dilated convolution.
- C3 is applied to ESPNet and achieve about 2% better performance while reducing the number of parameters by half and the number of FLOPs by 35% compared with the original ESPNet.
This is a paper in 2019 arXiv. (Sik-Ho Tsang @ Medium)