Displaying 1 to 3 from 3 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).

AdvSemiSeg - Adversarial Learning for Semi-supervised Semantic Segmentation, BMVC 2018

  •    Python

Adversarial Learning for Semi-supervised Semantic Segmentation Wei-Chih Hung, Yi-Hsuan Tsai, Yan-Ting Liou, Yen-Yu Lin, and Ming-Hsuan Yang Proceedings of the British Machine Vision Conference (BMVC), 2018. The code are heavily borrowed from a pytorch DeepLab implementation (Link). The baseline model is DeepLabv2-Resnet101 without multiscale training and CRF post processing, which yields meanIOU 73.6% on the VOC2012 validation set.





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