摘要翻译：
人的重新识别（Re-ID）是指在视觉监控领域中，利用非重叠视图对不同摄像机之间的人进行匹配的任务。与其他计算机视觉任务一样，由于深度学习方法的使用，该任务也取得了很大的成果。然而，现有的基于深度学习的解决方案通常是在相同数据集的样本上进行训练和测试，而在实践中，需要为无法获得标记数据的新相机集部署Re-ID系统。在这里，我们针对一个最先进的模型，即使用三重损失函数训练的度量嵌入，缓解了这个问题，尽管我们的结果可以推广到其他模型。我们的工作的贡献在于开发了一种在多个数据集上训练模型的方法，以及一种在线的实际无监督微调模型的方法。这些方法在交叉数据集评估中的Rank-1得分提高了19.1%。
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英文标题：
《Improving Deep Models of Person Re-identification for Cross-Dataset
  Usage》
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作者：
Sergey Rodionov, Alexey Potapov, Hugo Latapie, Enzo Fenoglio, Maxim
  Peterson
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最新提交年份：
2018
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Computer Vision and Pattern Recognition        计算机视觉与模式识别
分类描述：Covers image processing, computer vision, pattern recognition, and scene understanding. Roughly includes material in ACM Subject Classes I.2.10, I.4, and I.5.
涵盖图像处理、计算机视觉、模式识别和场景理解。大致包括ACM课程I.2.10、I.4和I.5中的材料。
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一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Image and Video Processing        图像和视频处理
分类描述：Theory, algorithms, and architectures for the formation, capture, processing, communication, analysis, and display of images, video, and multidimensional signals in a wide variety of applications. Topics of interest include: mathematical, statistical, and perceptual image and video modeling and representation; linear and nonlinear filtering, de-blurring, enhancement, restoration, and reconstruction from degraded, low-resolution or tomographic data; lossless and lossy compression and coding; segmentation, alignment, and recognition; image rendering, visualization, and printing; computational imaging, including ultrasound, tomographic and magnetic resonance imaging; and image and video analysis, synthesis, storage, search and retrieval.
用于图像、视频和多维信号的形成、捕获、处理、通信、分析和显示的理论、算法和体系结构。感兴趣的主题包括：数学，统计，和感知图像和视频建模和表示；线性和非线性滤波、去模糊、增强、恢复和重建退化、低分辨率或层析数据；无损和有损压缩编码；分割、对齐和识别；图像渲染、可视化和打印；计算成像，包括超声、断层和磁共振成像；以及图像和视频的分析、合成、存储、搜索和检索。
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英文摘要：
  Person re-identification (Re-ID) is the task of matching humans across cameras with non-overlapping views that has important applications in visual surveillance. Like other computer vision tasks, this task has gained much with the utilization of deep learning methods. However, existing solutions based on deep learning are usually trained and tested on samples taken from same datasets, while in practice one need to deploy Re-ID systems for new sets of cameras for which labeled data is unavailable. Here, we mitigate this problem for one state-of-the-art model, namely, metric embedding trained with the use of the triplet loss function, although our results can be extended to other models. The contribution of our work consists in developing a method of training the model on multiple datasets, and a method for its online practically unsupervised fine-tuning. These methods yield up to 19.1% improvement in Rank-1 score in the cross-dataset evaluation.
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PDF链接：
https://arxiv.org/pdf/1807.08526