Few-example learning for video person re-ID is a challenging issue. Some studies use large unlabeled samples to mine more discriminative cues to overcome the visual scarcity. But how to develop a robust model to avoid overfitting and overcome noisy labels is still a remained problem. In this letter we focus on bridging between few labeled tracklets and numerous unlabeled tracklets. We leverage unlabeled tracklets by generating pseudo labels and adaptively joins them into the training set which consists of the labeled and unlabeled data. This work is distinguished by two key contributions. First, a novel model PAM (Part Attention Model) tailored for progressive learning is proposed. It has high accuracy and fast convergence. Second, we propose a novel sampling strategy ARS (Adaptively Relative Sampling). ARS adaptively filters out noisy labels and enlarges the training set. ARS-PAM achieves significant performance gains on four mainstream datasets. On the PRID2011 and iLIDS-VID dataset, ARS-PAM reaches 89.8%, 56.1% rank-1 accuracy, which exceeds the state-of-the-art by 5.5%, 17.5% respectively.

中文翻译：

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基于部分注意模型自适应地利用未标记的 Tracklet 用于少数示例 Re-ID

视频人员重新识别的少数示例学习是一个具有挑战性的问题。一些研究使用大型未标记样本来挖掘更具辨别力的线索，以克服视觉稀缺性。但是如何开发一个健壮的模型来避免过度拟合并克服嘈杂的标签仍然是一个悬而未决的问题。在这封信中，我们专注于在少数标记轨迹和众多未标记轨迹之间进行桥接。我们通过生成伪标签来利用未标记的轨迹并将它们自适应地加入由标记和未标记数据组成的训练集。这项工作有两个关键贡献。首先，提出了一种为渐进式学习量身定制的新型模型 PAM（部分注意力模型）。精度高，收敛速度快。其次，我们提出了一种新颖的采样策略 ARS（自适应相对采样）。ARS 自适应地过滤掉嘈杂的标签并扩大训练集。ARS-PAM 在四个主流数据集上取得了显着的性能提升。在 PRID2011 和 iLIDS-VID 数据集上，ARS-PAM 达到了 89.8%、56.1% rank-1 准确率，分别超过了 state-of-the-art 5.5%、17.5%。