An unsupervised domain adaptation target re-identification method. The method comprises: constructing a multiscale domain adaptation attention learning network utilizing a source domain dataset and a target domain dataset to train the multiscale domain adaptation attention learning network, calculating multitasking losses of the multiscale domain adaptation attention learning network, and when the values of the multitasking losses converge, producing a trained multiscale domain adaptation attention learning network; utilizing the trained multiscale domain adaptation attention learning network to construct an unsupervised domain adaptation target re-identification model, and utilizing the unsupervised domain adaptation target re-identification model for target re-identification processing of an inputted image. The method reduces domain differences by splitting a feature map into a target-related feature map and a domain-related feature map, maps the feature maps in different scales, performs splitting in multiple scales, and allows multiscale feature expression that is only domain-related to be learned, thus achieving optimal performance.
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