Despite the remarkable progress in semantic segmentation tasks with the advancement of deep neural networks, existing U-shaped hierarchical typical segmentation networks still suffer from local misclassification of categories and inaccurate target boundaries. In an effort to alleviate this issue, we propose a Model Doctor for semantic segmentation problems. The Model Doctor is designed to diagnose the aforementioned problems in existing pre-trained models and treat them without introducing additional data, with the goal of refining the parameters to achieve better performance. Extensive experiments on several benchmark datasets demonstrate the effectiveness of our method. Code is available at \url{https://github.com/zhijiejia/SegDoctor}.

翻译：尽管深度神经网络的进步使语义分割任务取得了显著进展，但现有的U型层次化典型分割网络仍存在类别局部误分类及目标边界不精确的问题。为缓解这一难题，我们提出了一种面向语义分割问题的"模型医生"（Model Doctor）。该"模型医生"旨在诊断现有预训练模型中的上述问题，并在不引入额外数据的情况下对其进行"治疗"，通过优化参数以提升性能。在多个基准数据集上的大量实验验证了该方法的有效性。代码已开源至 \url{https://github.com/zhijiejia/SegDoctor}。