Person re-identification (Re-ID) has achieved great improvement with deep
learning and a large amount of labelled training data. However, it remains a
challenging task for adapting a model trained in a source domain of labelled
data to a target domain of only unlabelled data available. In this work, we
develop a self-training method with progressive augmentation framework (PAST)
to promote the model performance progressively on the target dataset.
Specially, our PAST framework consists of two stages, namely, conservative
stage and promoting stage. The conservative stage captures the local structure
of target-domain data points with triplet-based loss functions, leading to
improved feature representations. The promoting stage continuously optimizes
the network by appending a changeable classification layer to the last layer of
the model, enabling the use of global information about the data distribution.
Importantly, we propose a new self-training strategy that progressively
augments the model capability by adopting conservative and promoting stages
alternately. Furthermore, to improve the reliability of selected triplet
samples, we introduce a ranking-based triplet loss in the conservative stage,
which is a label-free objective function basing on the similarities between
data pairs. Experiments demonstrate that the proposed method achieves
state-of-the-art person Re-ID performance under the unsupervised cross-domain
setting. Code is available at: this https URL

该研究提出了一种自训练的方法，使用渐进式增强框架 (PAST) 来不断提高模型在目标数据集上的表现，并在保守阶段和促进阶段之间交替进行三元组的损失优化，最终在无监督交叉领域设置下达到了最先进的人员重新识别绩效