人员重新识别是从行人检测器检测到的无重叠相机视图中检索行人图像。由于源域和目标域之间的偏差,大多数现有的人物识别(重新ID)模型经常无法从训练模型的源域到没有标签的新目标域很好地概括。这个问题显着地限制了模型在现实世界中的可扩展性和可用性。提供标记的源训练集和未标记的目标训练集,本文的目的是提高重新ID模型到目标域的泛化能力。为此,我们提出了一种名为身份保持生成对抗网络(IPGAN)的图像生成网络。所提出的方法具有两个优异的特性:1)仅使用单个模型以无监督的方式将标记图像从源域转换到目标摄像机域; 2)在翻译之前和之后保留来自源域的图像的身份信息。此外,我们为人员重新识别任务提出了IBN-reID模型。它具有比基线模型更好的广泛化能力,特别是在没有任何域适应的情况下。通过监督方法对翻译的图像训练IBN-reID模型。 Market-1501和DukeMTMC-reID显示的实验结果表明,IPGAN生成的图像更适合跨域重新识别。我们的方法实现了极具竞争力的重新识别准确性。
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本文研究了在“通过翻译学习”框架下的人格识别(re-ID)领域适应问题,该框架由两部分组成:1)以无人监督的方式将标记图像从源域转换到目标域,2)学习使用翻译图像的重新ID模型。目标是在图像翻译之后保留潜在的人格信息,使得带有标签的翻译图像对于目标域上的特征学习是有效的。为此,我们提出了一个保持相似性的生成对抗网络(SPGAN)和它的终端可训练版本eSPGAN。两者都旨在保持相似性,SPGAN通过启发式约束来强制执行此属性,而eSPGAN通过最佳地促进重新ID模型学习来实现此属性。更具体地说,SPGAN分别承担“通过翻译学习”框架中的两个组成部分。它首先保留两种类型的无监督相似性,即翻译前后图像的自相似性,以及翻译后的源图像和目标图像的相似性。然后,它使用现有网络学习重新ID模型。相比之下,eSPGAN可以无缝集成图像转换和重新ID模型学习。在eSPGAN的端到端训练期间,重新ID学习指导图像翻译以保留图像的基本身份信息。同时,图像翻译通过提供目标域样式的身份保持训练样本来改进重新ID学习。在实验中,我们表明SPGAN和eSPGAN生成的伪图像的身份保存完好。在此基础上,在两个大规模的人员重新识别数据集上展示了最新的领域适应性结果。
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Person re-identification (Re-ID) aims at recognizing the same person from images taken across different cameras. To address this task, one typically requires a large amount labeled data for training an effective Re-ID model, which might not be practical for real-world applications. To alleviate this limitation, we choose to exploit a sufficient amount of pre-existing labeled data from a different (aux-iliary) dataset. By jointly considering such an auxiliary dataset and the dataset of interest (but without label information), our proposed adaptation and re-identification network (ARN) performs unsupervised domain adaptation, which leverages information across datasets and derives domain-invariant features for Re-ID purposes. In our experiments , we verify that our network performs favorably against state-of-the-art unsupervised Re-ID approaches, and even outperforms a number of baseline Re-ID methods which require fully supervised data for training.
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Person re-identification (re-ID) models trained on one domain often fail to generalize well to another. In our attempt , we present a "learning via translation" framework. In the baseline, we translate the labeled images from source to target domain in an unsupervised manner. We then train re-ID models with the translated images by supervised methods. Yet, being an essential part of this framework, un-supervised image-image translation suffers from the information loss of source-domain labels during translation. Our motivation is twofold. First, for each image, the discriminative cues contained in its ID label should be maintained after translation. Second, given the fact that two domains have entirely different persons, a translated image should be dissimilar to any of the target IDs. To this end, we propose to preserve two types of unsupervised similarities , 1) self-similarity of an image before and after translation , and 2) domain-dissimilarity of a translated source image and a target image. Both constraints are implemented in the similarity preserving generative adversarial network (SPGAN) which consists of an Siamese network and a Cy-cleGAN. Through domain adaptation experiment, we show that images generated by SPGAN are more suitable for domain adaptation and yield consistent and competitive re-ID accuracy on two large-scale datasets.
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无人监督的域自适应重新识别采用标记的源数据来促进目标域的模型训练,面对由大域移位和大型摄像机变化引起的困境。非重叠标签质疑源域和目标域具有完全不同的人,进一步增加了重新识别难度。在本文中,我们提出了一种新的算法来缩小这种域差距。我们推导出一个摄像机样式适应框架,用于学习从目标域到源域的不同摄像机视图之间基于样式的映射,然后我们可以在摄像机级别上从源域到目标域进行基于身份的分发。为了克服非重叠标签挑战并引导人员重新识别模型进一步缩小差距,提出了一种有效的,有效的软标记方法,通过建立GAN翻译源域与之间的连接,挖掘目标域的内在局域结构。目标域。在真实基准数据集上进行的实验结果表明我们的方法获得了最先进的结果。
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Person re-identification (re-ID) poses unique challenges for unsupervised domain adaptation (UDA) in that classes in the source and target sets (domains) are entirely different and that image variations are largely caused by cameras. Given a labeled source training set and an unlabeled target training set, we aim to improve the generalization ability of re-ID models on the target testing set. To this end, we introduce a Hetero-Homogeneous Learning (HHL) method. Our method enforces two properties simultaneously: 1) camera invariance, learned via positive pairs formed by unlabeled target images and their camera style transferred counterparts; 2) domain connectedness, by regarding source / target images as negative matching pairs to the target / source images. The first property is implemented by homogeneous learning because training pairs are collected from the same domain. The second property is achieved by heterogeneous learning because we sample training pairs from both the source and target domains. On Market-1501, DukeMTMC-reID and CUHK03, we show that the two properties contribute indispensably and that very competitive re-ID UDA accuracy is achieved. Code is available at: https://github.com/zhunzhong07/HHL
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如何有效地解决域适应问题是人员重新识别(reID)的挑战性任务。在这项工作中,我们根据一次性学习,努力解决这个问题。给定注释源训练集和目标训练集,每个类别只注释一个实例,我们的目标是在目标域的测试集中实现竞争性的重新ID性能。为此,我们引入了相似性引导策略,逐步将伪标签分配给具有不同置信度分数的未标记实例,而这些实力分数又随着训练的进行而被用作引导优化的权重。通过简单的自我挖掘操作,我们对re-ID的域适应任务进行了重大改进。特别是,我们在DukeMTMC-reID的适应性任务中实现了71.5%的mAP,一次性设置得到了市场1501,超出了无监督域适应的超过17.8%的现状。在五枪设置下,我们在Market-1501上实现了受监督设置的竞争准确性。代码将可用。
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人的重新识别是计算机视觉领域的一个基础课题。传统的方法在解决人员照明问题上有一些局限性,如遮挡,姿势变化和复杂背景下的特征变异。幸运的是,深度学习范式开启了人员重新识别研究的新途径,并成为该领域的热点。过去几年的遗传对抗网(GANs)在解决这些问题时引起了很多关注。本文回顾了基于GAN的人员重新识别方法,重点关注不同基于GAN的框架的相关论文,并讨论了它们的优缺点。最后,提出了未来研究的方向,特别是基于GAN的人格识别方法的前景。
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大多数现有的人员重新识别(Re-ID)方法遵循监督学习框架,其中需要大量标记的匹配对进行训练。这样的设置严重限制了它们在实际应用中的可扩展性,其中在训练阶段没有可用的标记样本。为了克服这个限制,我们为无监督的跨数据集人重新识别任务开发了一种新颖的无监督多任务中级特征对齐(MMFA)网络。在假设资源和目标数据集共享同一组中级语义属性的情况下,我们提出的模型可以在人的身份分类和属性学习任务下与交叉数据集中级特征对齐正则化项共同优化。通过这种方式,学习的特征表示可以更好地从一个数据集推广到另一个数据集,这进一步提高了人的重新识别准确性。四个基准数据集的实验结果表明,我们提出的方法优于最先进的基线。
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The main contribution of this paper is a simple semi-supervised pipeline that only uses the original training set without collecting extra data. It is challenging in 1) how to obtain more training data only from the training set and 2) how to use the newly generated data. In this work, the generative adversarial network (GAN) is used to generate unlabeled samples. We propose the label smoothing regu-larization for outliers (LSRO). This method assigns a uniform label distribution to the unlabeled images, which reg-ularizes the supervised model and improves the baseline. We verify the proposed method on a practical problem: person re-identification (re-ID). This task aims to retrieve a query person from other cameras. We adopt the deep convolutional generative adversarial network (DC-GAN) for sample generation, and a baseline convolutional neural network (CNN) for representation learning. Experiments show that adding the GAN-generated data effectively improves the discriminative ability of learned CNN embeddings. On three large-scale datasets, Market-1501, CUHK03 and DukeMTMC-reID, we obtain +4.37%, +1.6% and +2.46% improvement in rank-1 precision over the baseline CNN, respectively. We additionally apply the proposed method to fine-grained bird recognition and achieve a +0.6% improvement over a strong baseline. The code is available at https://github.com/layumi/ Person-reID_GAN .
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Being a cross-camera retrieval task, person re-identification suffers from image style variations caused by different cameras. The art implicitly addresses this problem by learning a camera-invariant descriptor subspace. In this paper, we explicitly consider this challenge by introducing camera style (CamStyle) adaptation. CamStyle can serve as a data augmentation approach that smooths the camera style disparities. Specifically, with CycleGAN, labeled training images can be style-transferred to each camera, and, along with the original training samples, form the augmented training set. This method, while increasing data diversity against over-fitting, also incurs a considerable level of noise. In the effort to alleviate the impact of noise, the label smooth regularization (LSR) is adopted. The vanilla version of our method (without LSR) performs reasonably well on few-camera systems in which over-fitting often occurs. With LSR, we demonstrate consistent improvement in all systems regardless of the extent of over-fitting. We also report competitive accuracy compared with the state of the art. Code is available at: https://github.com/zhunzhong07/CamStyle
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跨域转移学习(CDTL)对于人员重新识别(ReID)来说是一项极具挑战性的任务。给定具有注释的源域和没有注释的目标域,CDTL寻求将知识从源域转移到目标域的有效方法。然而,诸如简单的双域转移学习方法对于人来说是不可用的,其中源/目标域由若干子域组成,例如,基于相机的子域。为了解决这个棘手的问题,我们提出了多对多生成对抗性转移学习方法(M2M-GAN),它采用多个源子域和多个目标子域进行整合,并执行从源域到源域的每个子域转移映射。统一优化过程中的目标域。所提出的方法首先将源子域的图像样式转换为目标子域的图像样式,然后通过使用源域中的传递图像和相应的注释来执行监督学习。随着间隙的减少,M2M-GAN实现了跨域personReID的有希望的结果。三个基准数据集Market-1501,DukeMTMC-reID和MSMT17的实验结果显示了我们的M2M-GAN的有效性。
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深度学习提出了希望和期望,作为许多应用程序的一般解决方案;事实证明它已被证明是有效的,但它也显示出对大量数据的强烈依赖性。幸运的是,已经证明,即使数据稀缺,也可以通过重复使用priorknowledge来训练成功的模型。因此,在最广泛的定义中,开发转移学习技术是部署有效和准确的智能系统的关键因素。本文将重点研究一系列适用于视觉目标识别任务的转移学习方法,特别是图像分类。转移学习是一个通用术语,并且特定设置已经给出了特定的名称:当学习者只能访问来自目标域的标记数据和来自不同域(源)的标记数据时,问题被称为“无监督域适应”。 (DA)。这项工作的第一部分将集中在这个设置的三种方法:其中一种方法涉及特征,一种是图像,而第三种方法同时使用两种。第二部分将重点关注机器人感知的现实生活问题,特别是RGB-D识别。机器人平台通常不仅限于色彩感知;他们经常带着Depthcamera。不幸的是,深度模态很少用于视觉识别,因为缺乏预先训练的模型,从中可以传输并且很少有数据从头开始。将提出两种处理这种情况的方法:一种使用合成数据,另一种利用跨模态转移学习。
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由于数据集偏差(例如完全不同的身份和背景)和数据集内差异(例如相机不变性),人员重新识别(Re-ID)模型通常在一个数据集上进行训练并在另一个数据集上进行测试时表现出有限的性能。就这个问题而言,给定一个标记的源训练集和一个未标记的目标训练集,我们提出了一种无监督的转移学习方法,其特征在于:1)通过建议的ImitateModels同时进行桥接数据集偏差和数据集内差异; 2)关于无监督人员Re-ID问题作为由双重分类损失制定的半监督学习问题,允许跨域的判别表示; 3)从类式空间开发跨越不同领域的基础共性,以提高re-ID模型的泛化能力。在两个广泛采用的基准测试中进行了广泛的实验,包括Market-1501和Dr.DTMMC-reID,实验结果表明,所提出的方法可以实现与其他最先进的无监督Re-ID方法的竞争性能。
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无监督的跨域人员重新识别(Re-ID)面临两个关键问题。一个是源域与目标域之间的数据分布差异,另一个是目标域中缺少标签信息。本文从表示学习的角度阐述了这一点。对于第一个问题,我们强调相机级子域的存在作为人Re-ID的独特特征,并且开发相机感知域适应,以减少源和域之间以及跨这些子域之间的差异。对于第二个问题,我们利用目标域的每个摄像机的时间连续性来创建判别信息。这是通过动态生成每批中的在线三元组来实现的,以最大限度地利用在训练过程中稳步改进的特征表示。总之,上述两种方法为人Re-ID提出了一种新的无监督深域适应框架。对基准数据集的实验和消融研究证明了它的优越性和有趣的特性。
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Most existing person re-identification (re-id) methods require supervised model learning from a separate large set of pairwise labelled training data for every single camera pair. This significantly limits their scalability and usabil-ity in real-world large scale deployments with the need for performing re-id across many camera views. To address this scalability problem, we develop a novel deep learning method for transferring the labelled information of an existing dataset to a new unseen (unlabelled) target domain for person re-id without any supervised learning in the target domain. Specifically, we introduce an Transfer-able Joint Attribute-Identity Deep Learning (TJ-AIDL) for simultaneously learning an attribute-semantic and identity-discriminative feature representation space transferrable to any new (unseen) target domain for re-id tasks without the need for collecting new labelled training data from the target domain (i.e. unsupervised learning in the target domain). Extensive comparative evaluations validate the superiority of this new TJ-AIDL model for unsupervised person re-id over a wide range of state-of-the-art methods on four challenging benchmarks including VIPeR, PRID, Market-1501, and DukeMTMC-ReID.
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在re-id的典型现实应用中,在摄像机视图中需要一组目标人物(例如嫌疑人)的监视列表(画廊集)来跟踪大量非目标人群,这被称为打开 - 世界人物重新认识。与传统(封闭世界)人物不同,大部分探测样本不是来自开放世界环境中的目标人。并且,总是会发生一个非目标人物看起来与目标人物相似,因此会严重挑战一个重新识别系统。在这项工作中,我们引入了一个基于对抗性学习的深度开放世界基于群体的人物重新模型缓解类似非目标人群造成的攻击问题。主要思想是通过使用GAN生成非常类似目标的图像(冒名顶替者)来学习攻击目标人物上的特征提取器,同时该模型将使特征提取器学会通过判别性学习来容忍攻击,从而实现基于群体的验证。我们提出的框架被称为对抗性开放世界人格识别,这是由我们的对抗人员网络(APN)实现的,它共同学习生成器,人物鉴别器,目标鉴别器和特征提取器,其中特征提取器和目标鉴别器共享相同的权重,以使特征提取器学会容忍冒名顶替者的攻击,以便更好地进行基于组的验证。虽然开放世界人士的重新审视具有挑战性,但我们首次表明,基于对抗的方法有助于更有效地稳定人员重新监控系统。
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Domain Adaptation is an actively researched problem in Computer Vision. In this work, we propose an approach that leverages unsupervised data to bring the source and target distributions closer in a learned joint feature space. We accomplish this by inducing a symbiotic relationship between the learned embedding and a generative adversarial network. This is in contrast to methods which use the ad-versarial framework for realistic data generation and retraining deep models with such data. We demonstrate the strength and generality of our approach by performing experiments on three different tasks with varying levels of difficulty: (1) Digit classification (MNIST, SVHN and USPS datasets) (2) Object recognition using OFFICE dataset and (3) Domain adaptation from synthetic to real data. Our method achieves state-of-the art performance in most experimental settings and by far the only GAN-based method that has been shown to work well across different datasets such as OFFICE and DIGITS.
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The superiority of deeply learned pedestrian representations has been reported in very recent literature of person re-identification (re-ID). In this article, we consider the more pragmatic issue of learning a deep feature with no or only a few labels. We propose a progressive unsupervised learning (PUL) method to transfer pretrained deep representations to unseen domains. Our method is easy to implement and can be viewed as an effective baseline for unsupervised re-ID feature learning. Specifically, PUL iterates between (1) pedestrian clustering and (2) fine-tuning of the convolutional neural network (CNN) to improve the initialization model trained on the irrelevant labeled dataset. Since the clustering results can be very noisy, we add a selection operation between the clustering and fine-tuning. At the beginning, when the model is weak, CNN is fine-tuned on a small amount of reliable examples that locate near to cluster centroids in the feature space. As the model becomes stronger, in subsequent iterations, more images are being adaptively selected as CNN training samples. Progressively, pedestrian clustering and the CNN model are improved simultaneously until algorithm convergence. This process is naturally formulated as self-paced learning. We then point out promising directions that may lead to further improvement. Extensive experiments on three large-scale re-ID datasets demonstrate that PUL outputs discriminative features that improve the re-ID accuracy. Our code has been released at https://github.com/hehefan/Unsupervised-Person-Re-identification-Clustering-and-Fine-tuning.
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Person re-identification (Re-ID) poses a unique challenge to deep learning: how to learn a deep model with millions of parameters on a small training set of few or no labels. In this paper, a number of deep transfer learning models are proposed to address the data sparsity problem. First, a deep network architecture is designed which differs from existing deep Re-ID models in that (a) it is more suitable for transferring representations learned from large image classification datasets, and (b) classification loss and verification loss are combined, each of which adopts a different dropout strategy. Second, a two-stepped fine-tuning strategy is developed to transfer knowledge from auxiliary datasets. Third, given an unlabelled Re-ID dataset, a novel unsuper-vised deep transfer learning model is developed based on co-training. The proposed models outperform the state-of-the-art deep Re-ID models by large margins: we achieve Rank-1 accuracy of 85.4%, 83.7% and 56.3% on CUHK03, Market1501, and VIPeR respectively, whilst on VIPeR, our unsupervised model (45.1%) beats most supervised models.
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