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METHOD FOR PERSON RE-IDENTIFICATION BASED ON DEEP MODEL WITH MULTI-LOSS FUSION TRAINING STRATEGY
METHOD FOR PERSON RE-IDENTIFICATION BASED ON DEEP MODEL WITH MULTI-LOSS FUSION TRAINING STRATEGY
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机译:基于深度模型的多损失融合训练策略的人员重新识别方法
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摘要
The invention relates to a method for person re-identification based on deep model with multi-loss fusion training strategy. The method uses a deep learning technology to perform preprocessing operations such as flipping, clipping, random erasing and style transfer, and then feature extraction is performed through a backbone network model; joint training of a network is performed by fusing a plurality of loss functions. Compared with other deep learning-based person re-identification algorithms, the present invention greatly improves the performance of person re-identification by adopting a plurality of preprocessing modes, the fusion of three loss functions and effective training strategy.
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