Diffusion-based re-ranking is a common method used for retrieving instances by performing similarity propagation in a nearest neighbor graph. However, existing techniques that construct the affinity graph based on pairwise instances can lead to the propagation of misinformation from outliers and other manifolds, resulting in inaccurate results. To overcome this issue, we propose a novel Cluster-Aware Similarity (CAS) diffusion for instance retrieval. The primary concept of CAS is to conduct similarity diffusion within local clusters, which can reduce the influence from other manifolds explicitly. To obtain a symmetrical and smooth similarity matrix, our Bidirectional Similarity Diffusion strategy introduces an inverse constraint term to the optimization objective of local cluster diffusion. Additionally, we have optimized a Neighbor-guided Similarity Smoothing approach to ensure similarity consistency among the local neighbors of each instance. Evaluations in instance retrieval and object re-identification validate the effectiveness of the proposed CAS, our code is publicly available.

翻译：基于扩散的重排序是一种常用的实例检索方法，通过在最近邻图中进行相似性传播来实现。然而，现有基于成对实例构建亲和力图的技术可能导致来自异常点及其他流形的错误信息传播，从而造成结果不准确。为克服这一问题，我们提出了一种新颖的基于聚类的相似性扩散方法用于实例检索。该方法的核心思想是在局部聚类内部进行相似性扩散，从而显式降低其他流形的影响。为获得对称且平滑的相似性矩阵，我们的双向相似性扩散策略在局部聚类扩散的优化目标中引入了逆向约束项。此外，我们优化了邻域引导的相似性平滑方法，以确保每个实例的局部邻域间具有相似性一致性。在实例检索与物体重识别任务上的评估验证了所提方法的有效性，相关代码已公开。