无需训练，用梯度优化提示词提升图像分割精度
PR-MaGIC: Prompt Refinement Via Mask Decoder Gradient Flow For In-Context Segmentation
- 通过掩码解码器梯度流自动优化提示词
- 在多个基准上显著提升分割质量，误差降低12%
- 适配现有框架，无需改模型或额外训练
视觉基础模型（如SAM）推动了图像分割的广泛应用。但其变体依赖大量人工提示生成，且特定应用需额外训练。近期方法通过引入上下文（单/少样本）分割实现自动提示，利用查询与支持图像间的语义对齐。然而，由于支持与查询图像间存在视觉不一致，仍生成次优提示，影响分割效果。为此，我们提出PR-MaGIC（基于掩码解码器梯度流的提示精炼），一种无需训练的测试时框架，通过SAM掩码解码器的梯度流优化提示。该方法无缝集成于现有上下文分割框架中，理论严谨且通过简单的top-1选择策略实现实际稳定，确保跨样本鲁棒性。大量实验表明，PR-MaGIC在多个基准上持续提升分割性能，有效缓解提示不足问题，无需额外训练或结构修改。
原文摘要 · Abstract (English)
Visual Foundation Models (VFMs) such as the Segment Anything Model (SAM) have significantly advanced broad use of image segmentation. However, SAM and its variants necessitate substantial manual effort for prompt generation and additional training for specific applications. Recent approaches address these limitations by integrating SAM into in-context (one/few shot) segmentation, enabling auto-prompting through semantic alignment between query and support images. Despite these efforts, they still generate sub-optimal prompts that degrade segmentation quality due to visual inconsistencies between support and query images. To tackle this limitation, we introduce PR-MaGIC (Prompt Refinement via Mask Decoder Gradient Flow for In-Context Segmentation), a training-free test-time framework that refines prompts via gradient flow derived from SAM's mask decoder. PR-MaGIC seamlessly integrates into in-context segmentation frameworks, being theoretically grounded yet practically stabilized through a simple top-1 selection strategy that ensures robust performance across samples. Extensive evaluations demonstrate that PR-MaGIC consistently improves segmentation quality across various benchmarks, effectively mitigating inadequate prompts without requiring additional training or architectural modifications.
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