Hi @grantword8 ,
Thank you for your contribution to solving the problem of imbalanced number of samples in different categories in segmentation tasks.
I applied BlvLoss to the unsupervised domain adaptation segmentation task from Potsdam to Vaihingen. Indeed, IoU is significantly improved in categories with a small number of samples(Car and Clutter). However, the segmentation performance of the categories with a large number of samples has dropped significantly(Impervious surface from 80 to 60). I observed that the unsupervised domain adaptation segmentation task in your paper did not have such a problem.
I am wondering what caused this problem, and hope to get your advice. Thank you very much!
Hi @grantword8 , Thank you for your contribution to solving the problem of imbalanced number of samples in different categories in segmentation tasks. I applied BlvLoss to the unsupervised domain adaptation segmentation task from Potsdam to Vaihingen. Indeed, IoU is significantly improved in categories with a small number of samples(Car and Clutter). However, the segmentation performance of the categories with a large number of samples has dropped significantly(Impervious surface from 80 to 60). I observed that the unsupervised domain adaptation segmentation task in your paper did not have such a problem. I am wondering what caused this problem, and hope to get your advice. Thank you very much!