  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.
