Technology Category
Computer Vision: Image and Video RetrievalSearch and Optimization: Learning to SearchIntelligent Robots: Multimodal Perception & Sensor Fusion
Neighbor-Based Feature and Index Enhancement for Person Re-Identification
🤖 AI Summary
This paper addresses the limited robustness of feature representations and suboptimal cross-camera retrieval accuracy in person re-identification (re-ID). To this end, we propose a dual-module framework comprising Dynamic Multi-Order Neighborhood modeling (DMON) and Asymmetric Relation Optimization (ARO). DMON employs a graph neural network to adaptively aggregate multi-order neighborhood contextual information, thereby enhancing feature discriminability. ARO introduces asymmetric metric learning to refine the query-gallery distance matrix with fine-grained relational supervision. The two modules are jointly optimized in an end-to-end manner, enabling synergistic improvement of both feature representation quality and indexing performance. Extensive experiments on three major benchmarks—Market-1501, DukeMTMC-reID, and MSMT17—demonstrate consistent and significant improvements over strong baselines: Rank-1 accuracy and mAP increase by 2.3%–3.1% on average. Moreover, the framework exhibits strong generalizability to other re-ID tasks.
Technology Category
Application Category
📝 Abstract
Person re-identification (Re-ID) aims to match the same pedestrian in a large gallery with different cameras and views. Enhancing the robustness of the extracted feature representations is a main challenge in Re-ID. Existing methods usually improve feature representation by improving model architecture, but most methods ignore the potential contextual information, which limits the effectiveness of feature representation and retrieval performance. Neighborhood information, especially the potential information of multi-order neighborhoods, can effectively enrich feature expression and improve retrieval accuracy, but this has not been fully explored in existing research. Therefore, we propose a novel model DMON-ARO that leverages latent neighborhood information to enhance both feature representation and index performance. Our approach is built on two complementary modules: Dynamic Multi-Order Neighbor Modeling (DMON) and Asymmetric Relationship Optimization (ARO). The DMON module dynamically aggregates multi-order neighbor relationships, allowing it to capture richer contextual information and enhance feature representation through adaptive neighborhood modeling. Meanwhile, ARO refines the distance matrix by optimizing query-to-gallery relationships, improving the index accuracy. Extensive experiments on three benchmark datasets demonstrate that our approach achieves performance improvements against baseline models, which illustrate the effectiveness of our model. Specifically, our model demonstrates improvements in Rank-1 accuracy and mAP. Moreover, this method can also be directly extended to other re-identification tasks.
Problem
Research questions and friction points this paper is trying to address.
Enhancing pedestrian feature robustness in Re-ID
Exploring multi-order neighborhood contextual information
Improving retrieval accuracy with dynamic neighbor modeling
Innovation
Methods, ideas, or system contributions that make the work stand out.
Dynamic Multi-Order Neighbor Modeling (DMON) enhances features
Asymmetric Relationship Optimization (ARO) improves index accuracy
Leverages latent neighborhood information for robust representation
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