People re-identification task has seen enormous improvements in the latest
years, mainly due to the development of better image features extraction from
deep Convolutional Neural Networks (CNN) and the availability of large
datasets. However, little research has been conducted on animal identification
and re-identification, even if this knowledge may be useful in a rich variety
of different scenarios. Here, we tackle cattle re-identification exploiting
deep CNN and show how this task is poorly related with the human one,
presenting unique challenges that makes it far from being solved. We present
various baselines, both based on deep architectures or on standard machine
learning algorithms, and compared them with our solution. Finally, a rich
ablation study has been conducted to further investigate the unique
peculiarities of this task.

研究使用深度卷积神经网络实现牲畜重新识别，将该任务与人类重新识别任务进行比较，并探讨其独特挑战，并通过与基于深度架构和标准机器学习算法的不同基线进行比较，并展示了丰富的剖析研究以进一步研究该任务的独特特性。