用对抗扰动生成真实错误模式，提升分割掩码精度。
Learning from Adversity: Semantic-Aware Mask Refinement through Adversarial Perturbation
- 通过嵌入攻击生成语义相关的噪声，模拟真实模型错误。
- 对比学习构建三向框架，保持类别内一致性与类间分离性。
- 在多个任务中超越现有方法，适合改进高阶分割模型。
尽管图像分割技术进展显著，当前最先进模型仍存在边界不精确、语义不一致和结构错误等问题。掩码精修旨在解决这些缺陷，但现有方法依赖于简单的合成噪声，无法捕捉真实分割模型的复杂错误模式。我们提出Phoenix框架，利用对抗学习生成语义相关的噪声，并结合对比学习建模精修关系。核心创新包括：(1) 对抗掩码扰动，通过嵌入攻击生成模仿真实错误的语义感知噪声；(2) 对比掩码精修学习，建立三向框架，在语义区域内保持特征一致性，同时确保类别间分离。实验表明，Phoenix在多种任务中显著优于现有方法，且能持续提升现有先进分割模型，带来显著性能增益。代码与项目页已公开：https://phoenix-eccv26.github.io。
原文摘要 · Abstract (English)
Despite significant advances in image segmentation, even state-of-the-art models produce masks with imperfect boundaries, semantic inconsistencies, and structural errors. Mask refinement addresses these limitations, yet current approaches rely on simplistic synthetic noise that fails to capture the complex error patterns of real segmentation models. We introduce Phoenix, a novel framework that leverages adversarial learning to generate semantically meaningful noise patterns and contrastive learning to model refinement relationships. Our approach consists of two key innovations: (1) Adversarial Mask Perturbation, which employs embedding attacks to create semantic-aware noise that mimics real segmentation errors, and (2) Contrastive Mask Refinement Learning, which establishes a tri-directional framework that ensures feature consistency within semantic regions while maintaining separation between classes. Experiments demonstrate that Phoenix significantly outperforms existing methods across diverse tasks, while consistently enhancing state-of-the-art segmentation models with substantial improvements. Our code and project page are publicly available at https://phoenix-eccv26.github.io.
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