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Metric learning is an important issue in person re-identification, and Mahalanobis-distance based metric learning methods prevail in this field. All of these approaches can be considered as equivalently projecting all samples to a new metric space and calculating the Euclidean distance there. However, the performance of distinguishing similar samples from dissimilar ones via absolute distance is limited...
Person re-identification, aiming to identify images of the same person from various cameras configured in different places, has attracted much attention in the multimedia retrieval community. In this problem, choosing a proper distance metric is a crucial aspect, and many classic methods utilize a uniform learnt metric. However, their performance is limited due to ignoring the zero-shot and fine-grained...
Matching individuals across a group of spatially non-overlapping surveillance cameras, also known as person re-identification, has recently attracted a lot of research interests. Current methods mainly focus on feature extraction or metric learning, which directly compare person images captured by different cameras, but seldom consider device differences caused by various surveillance conditions,...
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