SegFix: Model-Agnostic Boundary Refinement for Segmentation (2007.04269v4)

Abstract: We present a model-agnostic post-processing scheme to improve the boundary quality for the segmentation result that is generated by any existing segmentation model. Motivated by the empirical observation that the label predictions of interior pixels are more reliable, we propose to replace the originally unreliable predictions of boundary pixels by the predictions of interior pixels. Our approach processes only the input image through two steps: (i) localize the boundary pixels and (ii) identify the corresponding interior pixel for each boundary pixel. We build the correspondence by learning a direction away from the boundary pixel to an interior pixel. Our method requires no prior information of the segmentation models and achieves nearly real-time speed. We empirically verify that our SegFix consistently reduces the boundary errors for segmentation results generated from various state-of-the-art models on Cityscapes, ADE20K and GTA5. Code is available at: https://github.com/openseg-group/openseg.pytorch.

• The paper proposes SegFix, a model-agnostic method that refines segmentation boundaries by replacing unreliable boundary predictions with interior pixel estimates.
• It employs a two-step process: localizing boundary pixels and correcting them using directional offsets from more reliable interior regions.
• Experimental results on Cityscapes, ADE20K, and GTA5 demonstrate significant improvements in boundary precision and overall segmentation metrics.

Overview of "SegFix: Model-Agnostic Boundary Refinement for Segmentation"

The paper "SegFix: Model-Agnostic Boundary Refinement for Segmentation" presents a novel approach to enhance boundary quality in segmentation tasks. The authors introduce a model-agnostic post-processing method applicable to results from any existing segmentation model, improving the accuracy of boundaries in semantic and instance segmentation.

Key Contributions

1. Model-Agnostic Approach: The core proposal is a post-processing technique named SegFix, which operates independently of the segmentation model that generated the initial results. It leverages the empirical observation that boundary pixel predictions are less reliable than those of interior pixels. By refining boundary predictions with more reliable interior pixel predictions, SegFix enhances segmentation quality.
2. Two-Step Process: SegFix uses a two-step process without any need for prior model knowledge:
   • Localize Boundary Pixels: Identify the boundary pixels within the output of a segmentation map.
   • Correspondence with Interior Pixels: Replace unreliable boundary predictions with predictions from more dependable interior pixels by learning directional offsets.
3. Efficiency and Practicality: The method is efficient, achieving near-real-time processing speed and can be applied directly without the need for re-training or fine-tuning the original segmentation model.

Experimental Validation

The authors validate the SegFix method on Cityscapes, ADE20K, and GTA5 datasets, demonstrating consistent improvements across various state-of-the-art segmentation models. Strong numerical results include significant reductions in boundary errors and enhancements in both mIoU and boundary F-score metrics.

• Cityscapes: Integration with models like DeepLabv3 and HRNet showed considerable enhancements in boundary precision.
• ADE20K and GTA5: Similar improvements were noted, confirming the method’s robustness across different scenarios.

Additionally, SegFix was applied successfully to instance segmentation tasks, marking its utility across multiple segmentation domains.

Technical Manifestation

The SegFix framework employs a high-resolution backbone, specifically an HRNet variant, to predict boundary and direction maps. The boundary maps predict the likelihood of a pixel being a boundary, while direction maps provide offsets for adjusting boundary pixels based on reliable interior pixel predictions. The combination of these maps refines the segmentation output without altering the input segmentation model.

Implications and Future Directions

The proposed SegFix framework offers practical implications in improving segmentation accuracy efficiently, without the computational overhead of model-specific adjustments or re-training. As a post-processing tool, it can enhance the deployment of segmentation technologies in real-world applications like autonomous driving and aerial imagery analysis.

In terms of future developments, SegFix could be integrated with more diverse models and tasks, potentially extending beyond standard segmentation to address more nuanced image processing challenges. Continued exploration into fine-tuning offset mapping and scaling across object categories could yield further performance boosts.

Overall, the SegFix method represents a significant step forward in segmentation refinement technology, offering a versatile and efficient approach to boundary enhancement.