I notice a lot of the contest winners in semantic segmentation use ensemble models, so if you wanna check out the technical reports of the leaderboards for MS COCO Stuff or Pascal VOC 2012 or Cityscapes, I bet you could find some generic descriptions of how they do it. Often they train different models for different image scales, and then at test time resort to multi-scale predictions by averaging the interpolated logits to the desired output size, i.e. model A predicts a mask for an image of size 600 times 600, model B for an image of size 800 times 800, etc… then you need a final prediction of size 500 times 500 which you can get by bilinear/bicubic interpolation to that final size, and average the prediction values at each pixel.