Recently unsupervised person re-identification (re-ID) has drawn much attention due to its open-world scenario settings where limited annotated data is available. Existing supervised methods often fail to generalize well on unseen domains, while the unsupervised methods, mostly lack multi-granularity information and are prone to suffer from confirmation bias. In this paper, we aim at finding better feature representations on the unseen target domain from two aspects, 1) performing unsupervised domain adaptation on the labeled source domain and 2) mining potential similarities on the unlabeled target domain. Besides, a collaborative pseudo re-labeling strategy is proposed to alleviate the influence of confirmation bias. Firstly, a generative adversarial network is utilized to transfer images from the source domain to the target domain. Moreover, person identity preserving and identity mapping losses are introduced to improve the quality of generated images. Secondly, we propose a novel collaborative multiple feature clustering framework (CMFC) to learn the internal data structure of target domain, including global feature and partial feature branches. The global feature branch (GB) employs unsupervised clustering on the global feature of person images while the Partial feature branch (PB) mines similarities within different body regions. Finally, extensive experiments on two benchmark datasets show the competitive performance of our method under unsupervised person re-ID settings.
翻译:最近无人监督的人重新认同(Re-ID)因其开放世界情景设置而引起人们的极大关注,因为其开放世界情景设置中可获得的附加说明的数据有限。现有的受监督方法往往无法在无形域上广泛推广,而未经监督的方法,大多缺乏多色信息,容易受到确认偏差的影响。在本文件中,我们的目标是从两个方面找到对无形目标域更好的特征表现,1)对标签源域进行未经监督的域域适应,2)在未标记目标域进行采矿的潜在相似之处。此外,还提议采用协作的假重新标签战略来减轻确认偏见的影响。首先,利用基因对抗网络将图像从源域转移到目标域,此外,引入了个人身份保存和身份测绘损失的方法,以提高生成图像的质量。第二,我们提议了一个新型的多重特征组合框架(CMFC),以学习目标域的内部数据结构,包括全球特征和部分特征分支。全球特征分支(GB)在个人图像的全球特征上采用未经监督的组合,同时利用部分特征识别对抗网络将图像从源域转让到目标域内的图像,同时在不同的实体内重新展示各自的业绩方法。