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About this paper
Yuhui Yuan, Jingyi Xie, Xilin Chen, Jingdong Wang
SegFix introduces a model-agnostic post-processing method that refines segmentation boundaries by predicting boundary and direction maps from the input image, consistently improving mean IoU and boundary F-score for various state-of-the-art models on datasets like Cityscapes and ADE20K.
Key insights
- Segmentation errors are predominantly found at object boundaries, validating the strategy of leveraging reliable interior pixel predictions to correct boundary misclassifications.
- Learning explicit local boundary-to-interior pixel correspondences through a dedicated direction map provides an effective mechanism for boundary refinement.
- A lightweight, separately trained network can provide robust boundary and direction information, enabling model-agnostic post-processing without altering or retraining the base segmentation model.
Summary by alphaXiv.
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