Person re-identification (Re-ID) aims to match pedestrian images across various scenes in video surveillance. There are a few works using attribute information to boost Re-ID performance. Specifically, those methods leverage attribute information to boost Re-ID performance by introducing auxiliary tasks like verifying the image level attribute information of two pedestrian images or recognizing identity level attributes. Identity level attribute annotations cost less manpower and are well-fitted for person re-identification task compared with image-level attribute annotations. However, the identity attribute information may be very noisy due to incorrect attribute annotation or lack of discriminativeness to distinguish different persons, which is probably unhelpful for the Re-ID task. In this paper, we propose a novel Attribute Attentional Block (AAB), which can be integrated into any backbone network or framework. Our AAB adopts reinforcement learning to drop noisy attributes based on our designed reward and then utilizes aggregated attribute attention of the remaining attributes to facilitate the Re-ID task. Experimental results demonstrate that our proposed method achieves state-of-the-art results on three benchmark datasets.

中文翻译：

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具有增强属性注意选择的人员重新识别

人员重新识别（Re-ID）旨在匹配视频监控中各个场景的行人图像。有一些使用属性信息来提高Re-ID性能的作品。具体而言，这些方法通过引入辅助任务（例如，验证两个行人图像的图像级别属性信息或识别身份级别属性）来利用属性信息来提高Re-ID性能。与图像级属性注释相比，身份级别属性注释花费的人力更少，并且非常适合于人员重新识别任务。但是，由于不正确的属性注释或缺乏区分不同人的区分性，身份属性信息可能非常嘈杂，这可能对Re-ID任务无济于事。在本文中，我们提出了一种新颖的属性注意块（AAB），可以将其集成到任何骨干网或框架中。我们的AAB采用强化学习，根据我们设计的奖励来降低嘈杂的属性，然后利用剩余属性的聚集属性注意力来完成Re-ID任务。实验结果表明，我们提出的方法可以在三个基准数据集上获得最新的结果。