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Asymmetric double networks mutual teaching for unsupervised person Re-identification
Pages 744–755https://doi.org/10.1016/j.neunet.2023.11.001
Highlights
- The ADNMT uses two asymmetric networks to generate pseudo-labels for each other by clustering and iterative training.
- Based on alternate training and mutual teaching to optimize the pseudo-labels.
- The SCIC compensates the ...
Unsupervised person re-identification (Re-ID) has always been challenging in computer vision. It has received much attention from researchers because it does not require any labeled information and can be freely deployed to new scenarios. Most ...
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