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Recurrent Neural Networks for Person Re-identification Revisited

机译:再造神经网络的人重新识别。

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The task of person re-identification has recently received rising attention due to the high performance achieved by new methods based on deep learning. In particular, in the context of video-based re-identification, many state-of-the-art works have explored the use of Recurrent Neural Networks (RNNs) to process input sequences. In this work, we revisit this tool by deriving an approximation which reveals the small effect of recurrent connections, leading to a much simpler feed-forward architecture. Using the same parameters as the recurrent version, our proposed feed-forward architecture obtains very similar accuracy. More importantly, our model can be combined with a new training process to significantly improve re-identification performance. Our experiments demonstrate that the proposed models converge substantially faster than recurrent ones, with accuracy improvements by up to 5% on two datasets. The performance achieved is better or on par with other RNN-based person re-identification techniques.
机译:由于基于深度学习的新方法的高性能,人员重新识别的任务近来受到越来越多的关注。特别地,在基于视频的重新识别的背景下,许多最新的工作已经探索了使用递归神经网络(RNN)来处理输入序列的方法。在这项工作中,我们通过推导近似值来重现该工具,该近似值揭示了循环连接的微小影响,从而导致更简单的前馈体系结构。使用与递归版本相同的参数,我们提出的前馈体系结构获得了非常相似的精度。更重要的是,我们的模型可以与新的训练过程结合使用,以显着提高重新识别性能。我们的实验表明,所提出的模型的收敛速度远快于递归模型,并且在两个数据集上的精度提高了5%。实现的性能更好,或与其他基于RNN的人员重新识别技术相提并论。

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