Displaying 1 to 2 from 2 results

AdaptSegNet - Learning to Adapt Structured Output Space for Semantic Segmentation, CVPR 2018 (spotlight)

  •    Python

Pytorch implementation of our method for adapting semantic segmentation from the synthetic dataset (source domain) to the real dataset (target domain). Based on this implementation, our result is ranked 3rd in the VisDA Challenge. Learning to Adapt Structured Output Space for Semantic Segmentation Yi-Hsuan Tsai*, Wei-Chih Hung*, Samuel Schulter, Kihyuk Sohn, Ming-Hsuan Yang and Manmohan Chandraker IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (spotlight) (* indicates equal contribution).

deep-transfer-learning - A collection of implementations of deep domain adaptation algorithms

  •    Python

Note that the results without this comes from paper. The results with this are run by myself with the code.