SPARK: 基于亲和力引导的随机游走进行无监督分割的随机传播
SPARK: Stochastic Propagation via Affinity-guided Random walK for training-free unsupervised segmentation
AI总结：
SPARK通过随机传播和亲和力引导的随机游走，实现无监督分割的高效且稳定的性能提升。
摘要：
我们主张现有的无监督分割方法依赖于一个隐含且限制性的假设，即分割是扩散导出的亲和力上的谱图划分问题。此类方法基于全局图划分和基于特征矩阵的特征向量公式，存在几个根本性缺点，它们需要预先选择聚类数量，由于谱松弛导致边界过度平滑，且对噪声或多模态的亲和力分布高度敏感。此外，许多先前的工作忽视了局部邻域结构的重要性，这在稳定亲和力传播和保持细粒度轮廓中起着关键作用。为了解决这些限制，我们将无监督分割重新表述为扩散诱导亲和力图上的随机流平衡问题，其中分割从一种随机传播过程产生，该过程结合了全局扩散注意力与从稳定扩散中提取的局部邻域，产生稀疏但表达性强的亲和力结构。基于这一公式，我们引入了一种马尔可夫传播方案，进行基于随机游走的标签扩散，并采用自适应修剪策略，以抑制不可靠的转换，同时强化自信的亲和力路径。在七个广泛使用的语义分割基准上的实验表明，我们的方法实现了最先进的零样本性能，产生更清晰的边界，更连贯的区域，并且比先前基于谱聚类的方法显著更稳定的掩码。
英文摘要：
We argue that existing training-free segmentation methods rely on an implicit and limiting assumption, that segmentation is a spectral graph partitioning problem over diffusion-derived affinities. Such approaches, based on global graph partitioning and eigenvector-based formulations of affinity matrices, suffer from several fundamental drawbacks, they require pre-selecting the number of clusters, induce boundary oversmoothing due to spectral relaxation, and remain highly sensitive to noisy or multi-modal affinity distributions. Moreover, many prior works neglect the importance of local neighborhood structure, which plays a crucial role in stabilizing affinity propagation and preserving fine-grained contours. To address these limitations, we reformulate training-free segmentation as a stochastic flow equilibrium problem over diffusion-induced affinity graphs, where segmentation emerges from a stochastic propagation process that integrates global diffusion attention with local neighborhoods extracted from stable diffusion, yielding a sparse yet expressive affinity structure. Building on this formulation, we introduce a Markov propagation scheme that performs random-walk-based label diffusion with an adaptive pruning strategy that suppresses unreliable transitions while reinforcing confident affinity paths. Experiments across seven widely used semantic segmentation benchmarks demonstrate that our method achieves state-of-the-art zero-shot performance, producing sharper boundaries, more coherent regions, and significantly more stable masks compared to prior spectral-clustering-based approaches.
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