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- W4293138620 abstract "Supervised distance metric learning-based methods for person re-id are specifically popular because of their robustness toward large color variations and fast training speed. However, like other supervised methods, metric learning algorithms have their own burden of human labeling effort especially for large camera networks. The total number of training pairs assumed to be available by these algorithms increases tremendously with network size and number of persons in each camera. Manual labeling of such huge number of pairs is a tedious and expensive process. So, naturally a question arises: given a camera network, can we come up with a strategy of choosing a minimal subset of image pairs for labeling without compromising on recognition performance? This is a problem of considerable significance in the context of person re-id in multi-camera networks, especially in larger ones." @default.
- W4293138620 created "2022-08-26" @default.
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- W4293138620 date "2021-01-01" @default.
- W4293138620 modified "2023-10-15" @default.
- W4293138620 title "Supervised Re-identification: Optimizing the Annotation Effort" @default.
- W4293138620 doi "https://doi.org/10.1007/978-3-031-01825-1_2" @default.
- W4293138620 hasPublicationYear "2021" @default.
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