语义尺度空间：可控图像抽象化框架
Semantic Scale Space: A Framework for Controllable Image Abstraction
提出语义尺度空间框架，将平滑强度与语义粒度解耦，并用可控制的语义边界检测器指定平滑屏障；其自适应粒度调度平滑（AGSS）结合供体门控扩散算子与由细到粗的调度，可复用于需要选择性平滑或结构保持的视觉任务（如风格化、边缘保持滤波）。
原文依据 We propose the Semantic Scale Space (SSS), a two-dimensional abstraction framework parameterized by smoothing strength and semantic granularity.
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论图像抽象化是非真实感渲染（NPR）的基本组成部分，旨在将照片简化为风格化描绘，同时保留感知上重要的结构。其核心难点在于选择性：去除细微纹理的同时保留语义上有意义的边界。现有方法通常只提供少数相互纠缠的控制参数，因此平滑程度与保留结构集合无法以清晰直观的方式调整。我们提出语义尺度空间（SSS），一个由平滑强度和语义粒度参数化的二维抽象框架。SSS通过使用可控语义边界检测器来外化停止集，从而独立于同质区域内简化的推进程度，指定哪些结构作为平滑的屏障。我们进一步引入自适应粒度调度平滑（AGSS），这是该空间中的一种具体遍历策略，结合了供体门控扩散算子与由细到粗的粒度调度。为了公平评估选择性，我们还引入了一种基于区域同质性指数的效果匹配协议，在匹配的平滑水平下比较方法。在SBD基准上，AGSS在相同平滑程度下比强基线实现了更高的边界保留和更低的几何漂移。在DIV2K上，用户研究进一步表明，基于AGSS抽象化的下游NPR结果始终更受偏好。这些结果表明，SSS和AGSS为现代创意应用中的可控图像抽象化提供了实用的框架和方法。
英文原文摘要 1642 字符
Image abstraction, a fundamental component of non-photorealistic rendering (NPR), aims to simplify photographs into stylized depictions while preserving perceptually important structures. A central difficulty is selectivity: removing fine textures while preserving semantically meaningful boundaries. Existing approaches often expose only a few entangled controls, so the amount of smoothing and the set of preserved structures cannot be adjusted in a clear and intuitive manner. We propose the Semantic Scale Space (SSS), a two-dimensional abstraction framework parameterized by smoothing strength and semantic granularity. SSS externalizes the stopping set by using a controllable semantic boundary detector to specify which structures act as barriers to smoothing, independently of how far simplification proceeds within homogeneous regions. We further introduce Adaptive Granularity Scheduling Smoothing (AGSS), a concrete traversal policy in this space that combines a donor-gated diffusion operator with a fine-to-coarse granularity schedule. To evaluate selectivity fairly, we also introduce an effect-matched protocol based on a Region Homogeneity Index, which compares methods under matched smoothing levels. On the SBD benchmark, AGSS achieves higher boundary preservation and lower geometric drift than strong baselines at the same degree of smoothing. On DIV2K, a user study further shows that downstream NPR results built on AGSS-based abstractions are consistently preferred. These results demonstrate that SSS and AGSS provide a practical framework and method for controllable image abstraction in modern creative applications.