Person re-identification (re-ID) remains challenging in a real-world scenario, as it requires a trained network to generalise to totally unseen target data in the presence of variations across domains. Recently, generative adversarial models have been widely adopted to enhance the diversity of training data. These approaches, however, often fail to generalise to other domains, as existing generative person re-identification models have a disconnect between the generative component and the discriminative feature learning stage. To address the on-going challenges regarding model generalisation, we propose an end-to-end domain adaptive attention network to jointly translate images between domains and learn discriminative re-id features in a single framework. To address the domain gap challenge, we introduce an attention module for image translation from source to target domains without affecting the identity of a person. More specifically, attention is directed to the background instead of the entire image of the person, ensuring identifying characteristics of the subject are preserved. The proposed joint learning network results in a significant performance improvement over state-of-the-art methods on several challenging benchmark datasets.

中文翻译：

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用于跨域行人重识别的端到端域自适应注意网络

人员重新识别 (re-ID) 在现实世界中仍然具有挑战性，因为它需要经过训练的网络在跨域存在变化的情况下泛化到完全看不见的目标数据。最近，生成对抗模型已被广泛采用以增强训练数据的多样性。然而，这些方法往往无法推广到其他领域，因为现有的生成人重新识别模型在生成组件和判别特征学习阶段之间存在脱节。为了解决有关模型泛化的持续挑战，我们提出了一个端到端的域自适应注意网络，以在单个框架中联合翻译域之间的图像并学习区分性的 re-id 特征。为了解决领域差距挑战，我们引入了一个注意力模块，用于从源域到目标域的图像转换，而不会影响一个人的身份。更具体地说，注意力集中在背景而不是整个人的图像上，从而确保保留主体的识别特征。所提出的联合学习网络在几个具有挑战性的基准数据集上比最先进的方法显着提高了性能。