Model Doctor for Diagnosing and Treating Segmentation Error
This addresses segmentation errors for computer vision applications, but it is incremental as it refines existing models rather than introducing a new paradigm.
The paper tackles local misclassification and inaccurate boundaries in U-shaped semantic segmentation networks by proposing a Model Doctor that diagnoses and treats these issues in pre-trained models without extra data, achieving improved performance on benchmark datasets.
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}.