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- W3010056644 abstract "To improve the accuracy of person re-identification and reduce the size of models, a lightweight and accurate (LWA) unsupervised method is proposed. Firstly, a lightweight image classifier (LWC) is proposed. It combines DenseNet's convolutional layer and batch normalization layer to simplify its model structure. Then the spatio-temporal information of datasets is added to form a powerful classifier. Secondly, an optimization learning algorithm is proposed. A feeble classifier (LWB) is implemented by constructing a binary network. For the training of the network, the ranking information of a powerful classifier is used to enhance the recognition capability of the LWB. Furthermore, spatio-temporal information of datasets is added again to construct LWA classifier. The results show that the model size of LWB is about 80% smaller than that of the Siamese network model on the CUHK01 and VIPeR datasets. At the same time, the recognition accuracy of the LWA classifier is slightly improved on the Market-1501 and DukeMTMC target datasets." @default.
- W3010056644 created "2020-03-13" @default.
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- W3010056644 date "2019-11-01" @default.
- W3010056644 modified "2023-09-23" @default.
- W3010056644 title "LWA: A lightweight and accurate method for unsupervised Person Re-identification" @default.
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- W3010056644 doi "https://doi.org/10.1109/icsai48974.2019.9010093" @default.
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