In this paper, we aim to tackle the one-shot person re-identification problem where only one image is labelled for each person, while other images are unlabelled. This task is challenging due to the lack of sufficient labelled training data. To tackle this problem, we propose to iteratively guess pseudo labels for the unlabeled image samples, which are later used to update the re-identification model together with the labelled samples. A new sampling mechanism is designed to select unlabeled samples to pseudo labelled samples based on the distance matrix, and to form a training triplet batch including both labelled samples and pseudo labelled samples. We also design an HSoften-Triplet-Loss to soften the negative impact of the incorrect pseudo label, considering the unreliable nature of pseudo labelled samples. Finally, we deploy an adversarial learning method to expand the image samples to different camera views. Our experiments show that our framework achieves a new state-of-the-art one-shot Re-ID performance on Market-1501 (mAP 42.7%) and DukeMTMC-Reid dataset (mAP 40.3%). Code will be available soon.

中文翻译：

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用于一次性人重识别的渐进式样本挖掘和表示学习

在本文中，我们的目标是解决一次性人重识别问题，其中每个人只标记一张图像，而其他图像未标记。由于缺乏足够的标记训练数据，这项任务具有挑战性。为了解决这个问题，我们建议为未标记的图像样本迭代猜测伪标签，这些伪标签稍后用于与标记的样本一起更新重新识别模型。设计了一种新的采样机制，根据距离矩阵将未标记样本选择为伪标记样本，形成包含标记样本和伪标记样本的训练三元组batch。考虑到伪标记样本的不可靠性质，我们还设计了一个 HSoften-Triplet-Loss 来减轻错误伪标记的负面影响。最后，我们部署了一种对抗性学习方法来将图像样本扩展到不同的相机视图。我们的实验表明，我们的框架在 Market-1501（mAP 42.7%）和 DukeMTMC-Reid 数据集（mAP 40.3%）上实现了最先进的一次性 Re-ID 性能。代码将很快可用。