Diffusion-based re-ranking methods are effective in modeling the data manifolds through similarity propagation in affinity graphs. However, positive signals tend to diminish over several steps away from the source, reducing discriminative power beyond local regions. To address this issue, we introduce the Locality Preserving Markovian Transition (LPMT) framework, which employs a long-term thermodynamic transition process with multiple states for accurate manifold distance measurement. The proposed LPMT first integrates diffusion processes across separate graphs using Bidirectional Collaborative Diffusion (BCD) to establish strong similarity relationships. Afterwards, Locality State Embedding (LSE) encodes each instance into a distribution for enhanced local consistency. These distributions are interconnected via the Thermodynamic Markovian Transition (TMT) process, enabling efficient global retrieval while maintaining local effectiveness. Experimental results across diverse tasks confirm the effectiveness of LPMT for instance retrieval.

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.

Object Re-identification (ReID) is a task focused on retrieving a probe object from a multitude of gallery images using a ReID model trained on a stationary, camera-free dataset. This training involves associating and aggregating identities across various camera views. However, when deploying ReID algorithms in real-world scenarios, several challenges, such as storage constraints, privacy considerations, and dynamic changes in camera setups, can hinder their generalizability and practicality. To address these challenges, we introduce a novel ReID task called Camera-Incremental Object Re-identification (CIOR). In CIOR, we treat each camera's data as a separate source and continually optimize the ReID model as new data streams come from various cameras. By associating and consolidating the knowledge of common identities, our aim is to enhance discrimination capabilities and mitigate the problem of catastrophic forgetting. Therefore, we propose a novel Identity Knowledge Evolution (IKE) framework for CIOR, consisting of Identity Knowledge Association (IKA), Identity Knowledge Distillation (IKD), and Identity Knowledge Update (IKU). IKA is proposed to discover common identities between the current identity and historical identities, facilitating the integration of previously acquired knowledge. IKD involves distilling historical identity knowledge from common identities, enabling rapid adaptation of the historical model to the current camera view. After each camera has been trained, IKU is applied to continually expand identity knowledge by combining historical and current identity memories. Market-CL and Veri-CL evaluations show the effectiveness of Identity Knowledge Evolution (IKE) for CIOR.Code:https://github.com/htyao89/Camera-Incremental-Object-ReID