Understanding and Improving the Intermediate Features of FCN in Semantic Segmentation
In full convolution networks, the intensive prediction results generated by intermediate features have a disadvantage of low contribution rate. This paper used the visualization tools and quantitative analysis, and proposed a new training strategy using dynamic weighting strategy which was verified by experiments. The results show that the strategy can effectively improve the accuracy of prediction intermediate features results.
2019 International Conference on Communications, Information System and Computer Engineering (CISCE)