trivial questions and suggestions

It is an awesome and developing repo., containing more SOTA methods.
Question 1
For obtaining mIoU with backgroud , computing mIoU with backgroud, i,e,. 60 classes for PContext, fisrt need to get mIoU without backgroud (mIoU_59) as you did in repo, then mIoU_60 is directly equal to mIoU_59 * 59 / 60 and will be slightly weaker than mIoU_59. Do I understand correctly?
Question 2
For multisize evaluation, will you consider employing dense crop on feature map rather than on input image? It drastically reduces computational overhead and may further boost the performance.
Question 3
Do you consdier the usage of accumulation gradient strategy to update param. due to limited GPU memory (small batchsize)?
Question 4
Your work appeals to me. When will you release your cvpr2019 paper Co-occurrent Features in Semantic Segmentation ?

Thank you for your consideration and I am looking forward to your reply.

The issue names no files, tests, or entry points. Start by reviewing the repository's existing mIoU_59 and multisize evaluation paths, then determine whether the metric questions require documentation and whether the crop or gradient-accumulation suggestions warrant separate feature issues; done means the questions and proposed directions have clear, documented decisions.