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在语义分割任务中取得了显著成效，但现有的U型层次型典型分割网络仍然存在类别局部误分类以及目标边界不精确的问题。为缓解这一问题，我们针对语义分割问题提出了一种模型医生。该模型医生旨在诊断现有预训练模型中上述问题，并无需引入额外数据即可对其进行治疗，旨在通过优化参数以获得更优性能。在多个基准数据集上的广泛实验证明了我们方法的有效性。代码可在 \url{https://github.com/zhijiejia/SegDoctor} 获取。