| Summary: | Most unsupervised methods of person re-identification (Re-ID) obtain pseudo-labels through clustering. However, in the process of clustering, the hard quantization loss caused by clustering errors will make the model produce false pseudo-labels. In order to solve this problem, an unsupervised model based on softened labels training method is proposed. The innovation of this method is that the correlation among image features is used to find the reliable positive samples and train them in a smooth manner. To further explore the correlation among image features, some modules are carefully designed in this article. The dynamic adaptive label allocation (DALA) method which generates pseudo-labels of adaptive size according to different metric relationships among features is proposed. The channel attention and transformer architecture (CATA) auxiliary module is designed, which, associated with convolutional neural network (CNN), functioned as the feature extractor of the model aimed to capture long range dependencies and acquire more distinguishable features. The proposed model is evaluated on the Market-1501 and the DukeMTMC-reID. The experimental results of the proposed method achieve 60.8 mAP on Market-1501 and 49.6 mAP on DukeMTMC-reID respectively, which outperform most state-of-the-art models in fully unsupervised Re-ID task.
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