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Moving Towards Centers: Re-ranking with Attention and Memory for Re-identification
[article]
2021
Re-ranking utilizes contextual information to optimize the initial ranking list of person or vehicle re-identification (re-ID), which boosts the retrieval performance at post-processing steps. This paper proposes a re-ranking network to predict the correlations between the probe and top-ranked neighbor samples. Specifically, all the feature embeddings of query and gallery images are expanded and enhanced by a linear combination of their neighbors, with the correlation prediction serves as
doi:10.48550/arxiv.2105.01447
fatcat:2gzsokl6pjhxdkvunyorj6yrla