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, unsupervised image-image translation suffers from the information loss of source-domain labels during translation.Our motivation is two-fold. 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.

最近，无监督的人重新识别（RE-ID）引起了人们的关注，因为其开放世界情景设置有限，可用的带注释的数据有限。现有的监督方法通常无法很好地概括在看不见的域上，而无监督的方法（大多数缺乏多范围的信息），并且容易患有确认偏见。在本文中，我们旨在从两个方面从看不见的目标域上找到更好的特征表示形式，1）在标记的源域上进行无监督的域适应性和2）2）在未标记的目标域上挖掘潜在的相似性。此外，提出了一种协作伪标记策略，以减轻确认偏见的影响。首先，使用生成对抗网络将图像从源域转移到目标域。此外，引入了人身份和身份映射损失，以提高生成图像的质量。其次，我们提出了一个新颖的协作多元特征聚类框架（CMFC），以学习目标域的内部数据结构，包括全局特征和部分特征分支。全球特征分支（GB）在人体图像的全球特征上采用了无监督的聚类，而部分特征分支（PB）矿山在不同人体区域内的相似性。最后，在两个基准数据集上进行的广泛实验表明，在无监督的人重新设置下，我们的方法的竞争性能。

无监督的域自适应人重新识别（重新ID）任务是一个挑战，因为与常规域自适应任务不同，人物重新ID中的源域数据和目标域数据之间没有重叠，这导致一个重要的领域差距。最先进的无监督的RE-ID方法使用基于内存的对比损耗训练神经网络。然而，通过将每个未标记的实例视为类来执行对比学习，作为类将导致阶级冲突的问题，并且由于在存储库中更新时不同类别的实例数量的差异，更新强度是不一致的。为了解决此类问题，我们提出了对人的重新ID的原型字典学习，其能够通过一个训练阶段利用源域数据和目标域数据，同时避免类碰撞问题和群集更新强度不一致的问题原型字典学习。为了减少模型上域间隙的干扰，我们提出了一个本地增强模块，以改善模型的域适应而不增加模型参数的数量。我们在两个大型数据集上的实验证明了原型字典学习的有效性。 71.5 \％地图是在市场到Duke任务中实现的，这是与最先进的无监督域自适应RE-ID方法相比的2.3 \％的改进。它在Duke-to-Market任务中实现了83.9 \％地图，而与最先进的无监督的自适应重新ID方法相比，该任务在4.4 \％中提高了4.4％。

Although the performance of person Re-Identification (ReID) has been significantly boosted, many challenging issues in real scenarios have not been fully investigated, e.g., the complex scenes and lighting variations, viewpoint and pose changes, and the large number of identities in a camera network. To facilitate the research towards conquering those issues, this paper contributes a new dataset called MSMT17 with many important features, e.g., 1) the raw videos are taken by an 15-camera network deployed in both indoor and outdoor scenes, 2) the videos cover a long period of time and present complex lighting variations, and 3) it contains currently the largest number of annotated identities, i.e., 4,101 identities and 126,441 bounding boxes. We also observe that, domain gap commonly exists between datasets, which essentially causes severe performance drop when training and testing on different datasets. This results in that available training data cannot be effectively leveraged for new testing domains. To relieve the expensive costs of annotating new training samples, we propose a Person Transfer Generative Adversarial Network (PTGAN) to bridge the domain gap. Comprehensive experiments show that the domain gap could be substantially narrowed-down by the PTGAN.

未经监督的域适应（UDA）用于重新识别（RE-ID）是一个具有挑战性的任务：避免昂贵的附加数据的注释，它旨在将知识从域转移到仅具有未标记数据的域的带注释数据。已证明伪标签方法已对UDA重新ID有效。然而，这些方法的有效性大量取决于通过聚类影响影响伪标签的一些超参数（HP）的选择。兴趣领域缺乏注释使得这一选择是非微不足道的。目前的方法只需重复使用所有适应任务的相同的经验值，并且无论通过伪标记培训阶段都会改变的目标数据表示。由于这种简单的选择可能会限制其性能，我们的目标是解决这个问题。我们提出了对聚类UDA RE-ID进行培训选择的新理论基础以及伪标签UDA聚类的自动和循环HP调谐方法：丘比巴。 Hyprass在伪标记方法中包含两个模块：（i）基于标记源验证集的HP选择和（ii）特征歧视的条件域对齐，以改善基于源样本的HP选择。关于常用的人员重新ID和车辆重新ID数据集的实验表明，与常用的经验HP设置相比，我们所提出的次数始终如一地提高RE-ID中最先进的方法。

域适应是一种解决未经看线环境中缺乏大量标记数据的技术。提出了无监督的域适应，以使模型适用于使用单独标记的源数据和未标记的目标域数据的新模式。虽然已经提出了许多图像空间域适配方法来捕获像素级域移位，但是这种技术可能无法维持分割任务的高电平语义信息。对于生物医学图像的情况，在域之间的图像转换操作期间，诸如血管的细细节可能会丢失。在这项工作中，我们提出了一种模型，它使用周期 - 一致丢失在域之间适应域，同时通过在适应过程中强制执行基于边缘的损耗来维持原始图像的边缘细节。我们通过将其与其他两只眼底血管分割数据集的其他方法进行比较来证明我们的算法的有效性。与SOTA和〜5.2增量相比，我们达到了1.1〜9.2递增的骰子分数。

We propose a general framework for unsupervised domain adaptation, which allows deep neural networks trained on a source domain to be tested on a different target domain without requiring any training annotations in the target domain. This is achieved by adding extra networks and losses that help regularize the features extracted by the backbone encoder network. To this end we propose the novel use of the recently proposed unpaired image-toimage translation framework to constrain the features extracted by the encoder network. Specifically, we require that the features extracted are able to reconstruct the images in both domains. In addition we require that the distribution of features extracted from images in the two domains are indistinguishable. Many recent works can be seen as specific cases of our general framework. We apply our method for domain adaptation between MNIST, USPS, and SVHN datasets, and Amazon, Webcam and DSLR Office datasets in classification tasks, and also between GTA5 and Cityscapes datasets for a segmentation task. We demonstrate state of the art performance on each of these datasets.

The main contribution of this paper is a simple semisupervised 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 regularization for outliers (LSRO). This method assigns a uniform label distribution to the unlabeled images, which regularizes 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 (DCGAN) 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 .

最近，由于受监督人员重新识别（REID）的表现不佳，域名概括（DG）人REID引起了很多关注，旨在学习一个不敏感的模型，并可以抵抗域的影响偏见。在本文中，我们首先通过实验验证样式因素是域偏差的重要组成部分。基于这个结论，我们提出了一种样式变量且无关紧要的学习方法（SVIL）方法，以消除样式因素对模型的影响。具体来说，我们在SVIL中设计了样式的抖动模块（SJM）。 SJM模块可以丰富特定源域的样式多样性，并减少各种源域的样式差异。这导致该模型重点关注与身份相关的信息，并对样式变化不敏感。此外，我们将SJM模块与元学习算法有机结合，从而最大程度地提高了好处并进一步提高模型的概括能力。请注意，我们的SJM模块是插件和推理，无需成本。广泛的实验证实了我们的SVIL的有效性，而我们的方法的表现优于DG-REID基准测试的最先进方法。

In this paper, we investigate a challenging unsupervised domain adaptation setting -unsupervised model adaptation. We aim to explore how to rely only on unlabeled target data to improve performance of an existing source prediction model on the target domain, since labeled source data may not be available in some real-world scenarios due to data privacy issues. For this purpose, we propose a new framework, which is referred to as collaborative class conditional generative adversarial net to bypass the dependence on the source data. Specifically, the prediction model is to be improved through generated target-style data, which provides more accurate guidance for the generator. As a result, the generator and the prediction model can collaborate with each other without source data. Furthermore, due to the lack of supervision from source data, we propose a weight constraint that encourages similarity to the source model. A clustering-based regularization is also introduced to produce more discriminative features in the target domain. Compared to conventional domain adaptation methods, our model achieves superior performance on multiple adaptation tasks with only unlabeled target data, which verifies its effectiveness in this challenging setting.

Person re-identification (Re-ID) aims at retrieving a person of interest across multiple non-overlapping cameras. With the advancement of deep neural networks and increasing demand of intelligent video surveillance, it has gained significantly increased interest in the computer vision community. By dissecting the involved components in developing a person Re-ID system, we categorize it into the closed-world and open-world settings. The widely studied closed-world setting is usually applied under various research-oriented assumptions, and has achieved inspiring success using deep learning techniques on a number of datasets. We first conduct a comprehensive overview with in-depth analysis for closed-world person Re-ID from three different perspectives, including deep feature representation learning, deep metric learning and ranking optimization. With the performance saturation under closed-world setting, the research focus for person Re-ID has recently shifted to the open-world setting, facing more challenging issues. This setting is closer to practical applications under specific scenarios. We summarize the open-world Re-ID in terms of five different aspects. By analyzing the advantages of existing methods, we design a powerful AGW baseline, achieving state-of-the-art or at least comparable performance on twelve datasets for FOUR different Re-ID tasks. Meanwhile, we introduce a new evaluation metric (mINP) for person Re-ID, indicating the cost for finding all the correct matches, which provides an additional criteria to evaluate the Re-ID system for real applications. Finally, some important yet under-investigated open issues are discussed.

域概括人员重新识别旨在将培训的模型应用于未经看明域。先前作品将所有培训域中的数据组合以捕获域不变的功能，或者采用专家的混合来调查特定域的信息。在这项工作中，我们争辩说，域特定和域不变的功能对于提高重新ID模型的泛化能力至关重要。为此，我们设计了一种新颖的框架，我们命名为两流自适应学习（tal），同时模拟这两种信息。具体地，提出了一种特定于域的流以捕获具有批量归一化（BN）参数的训练域统计，而自适应匹配层被设计为动态聚合域级信息。同时，我们在域不变流中设计一个自适应BN层，以近似各种看不见域的统计信息。这两个流自适应地和协作地工作，以学习更广泛的重新ID功能。我们的框架可以应用于单源和多源域泛化任务，实验结果表明我们的框架显着优于最先进的方法。

无监督的人重新识别（RE-ID）由于其可扩展性和对现实世界应用的可能性而吸引了增加的研究兴趣。最先进的无监督的重新ID方法通常遵循基于聚类的策略，该策略通过聚类来生成伪标签，并维护存储器以存储实例功能并代表群集的质心进行对比​​学习。这种方法遇到了两个问题。首先，无监督学习产生的质心可能不是一个完美的原型。强迫图像更接近质心，强调了聚类的结果，这可能会在迭代过程中积累聚类错误。其次，以前的方法利用在不同的训练迭代中获得的功能代表一种质心，这与当前的训练样本不一致，因为这些特征不是直接可比的。为此，我们通过随机学习策略提出了一种无监督的重新ID方法。具体来说，我们采用了随机更新的内存，其中使用集群的随机实例来更新群集级内存以进行对比度学习。这样，学会了随机选择的图像对之间的关​​系，以避免由不可靠的伪标签引起的训练偏见。随机内存也始终是最新的，以保持一致性。此外，为了减轻摄像机方差的问题，在聚类过程中提出了一个统一的距离矩阵，其中减少了不同摄像头域的距离偏置，并强调了身份的差异。

近年来，随着对公共安全的需求越来越多，智能监测网络的快速发展，人员重新识别（RE-ID）已成为计算机视野领域的热门研究主题之一。人员RE-ID的主要研究目标是从不同的摄像机中检索具有相同身份的人。但是，传统的人重新ID方法需要手动标记人的目标，这消耗了大量的劳动力成本。随着深度神经网络的广泛应用，出现了许多基于深入的基于学习的人物的方法。因此，本文促进研究人员了解最新的研究成果和该领域的未来趋势。首先，我们总结了对几个最近公布的人的研究重新ID调查，并补充了系统地分类基于深度学习的人的重新ID方法的最新研究方法。其次，我们提出了一种多维分类，根据度量标准和表示学习，将基于深度学习的人的重新ID方法分为四类，包括深度度量学习，本地特征学习，生成的对抗学习和序列特征学习的方法。此外，我们根据其方法和动机来细分以上四类，讨论部分子类别的优缺点。最后，我们讨论了一些挑战和可能的研究方向的人重新ID。

This work focuses on unsupervised representation learning in person re-identification (ReID). Recent self-supervised contrastive learning methods learn invariance by maximizing the representation similarity between two augmented views of a same image. However, traditional data augmentation may bring to the fore undesirable distortions on identity features, which is not always favorable in id-sensitive ReID tasks. In this paper, we propose to replace traditional data augmentation with a generative adversarial network (GAN) that is targeted to generate augmented views for contrastive learning. A 3D mesh guided person image generator is proposed to disentangle a person image into id-related and id-unrelated features. Deviating from previous GAN-based ReID methods that only work in id-unrelated space (pose and camera style), we conduct GAN-based augmentation on both id-unrelated and id-related features. We further propose specific contrastive losses to help our network learn invariance from id-unrelated and id-related augmentations. By jointly training the generative and the contrastive modules, our method achieves new state-of-the-art unsupervised person ReID performance on mainstream large-scale benchmarks.

使用合成数据来训练在现实世界数据上实现良好性能的神经网络是一项重要任务，因为它可以减少对昂贵数据注释的需求。然而，合成和现实世界数据具有域间隙。近年来，已经广泛研究了这种差距，也称为域的适应性。通过直接执行两者之间的适应性来缩小源（合成）和目标数据之间的域间隙是具有挑战性的。在这项工作中，我们提出了一个新颖的两阶段框架，用于改进图像数据上的域适应技术。在第一阶段，我们逐步训练一个多尺度神经网络，以从源域到目标域进行图像翻译。我们将新的转换数据表示为“目标中的源”（SIT）。然后，我们将生成的SIT数据插入任何标准UDA方法的输入。该新数据从所需的目标域缩小了域间隙，这有助于应用UDA进一步缩小差距的方法。我们通过与其他领先的UDA和图像对图像翻译技术进行比较来强调方法的有效性，当时用作SIT发电机。此外，我们通过三种用于语义分割的最先进的UDA方法（HRDA，daformer and proda）在两个UDA任务上，GTA5到CityScapes和Synthia to CityScapes来证明我们的框架的改进。

Deep domain adaptation has emerged as a new learning technique to address the lack of massive amounts of labeled data. Compared to conventional methods, which learn shared feature subspaces or reuse important source instances with shallow representations, deep domain adaptation methods leverage deep networks to learn more transferable representations by embedding domain adaptation in the pipeline of deep learning. There have been comprehensive surveys for shallow domain adaptation, but few timely reviews the emerging deep learning based methods. In this paper, we provide a comprehensive survey of deep domain adaptation methods for computer vision applications with four major contributions. First, we present a taxonomy of different deep domain adaptation scenarios according to the properties of data that define how two domains are diverged. Second, we summarize deep domain adaptation approaches into several categories based on training loss, and analyze and compare briefly the state-of-the-art methods under these categories. Third, we overview the computer vision applications that go beyond image classification, such as face recognition, semantic segmentation and object detection. Fourth, some potential deficiencies of current methods and several future directions are highlighted.

由于源域和目标域之间的巨大差距，对于人重新识别的无监督域适应（UDA）是具有挑战性的。典型的自我训练方法是使用群集算法生成的伪标签来迭代优化目标域上的模型。然而，对此的缺点是嘈杂的伪标签通常在学习时造成麻烦。为了解决这个问题，已经开发了双网络的相互学习方法来生产可靠的软标签。然而，随着两个神经网络逐渐收敛，它们的互补性被削弱，并且它们可能变得偏向相同的噪音。本文提出了一种新颖的轻量级模块，细小波块（AWB），可以集成到相互学习的双网络中，以增强伪标签中的互补性和进一步抑制噪声。具体而言，我们首先介绍一种无参数模块，该波块通过不同的方式挥动特征映射块的两个网络创造了两个网络之间的差异。然后，利用注意机制来扩大创建的差异并发现更多互补特征。此外，探讨了两种组合策略，即探讨了与后关注。实验表明，该方法实现了最先进的性能，具有对多个UDA人重新识别任务的显着改进。我们还通过将其应用于车辆重新识别和图像分类任务来证明所提出的方法的一般性。我们的代码和模型可在https://github.com/wangwenhao0716/attentive-waveblock上使用。

最先进的无监督的RE-ID方法使用基于内存的非参数软制AX丢失训练神经网络。存储在存储器中的实例特征向量通过群集和更新在实例级别中分配伪标签。然而，不同的簇大小导致每个群集的更新进度中的不一致。为了解决这个问题，我们呈现了存储特征向量的集群对比度，并计算群集级别的对比度损耗。我们的方法采用唯一的群集表示来描述每个群集，从而产生群集级存储字典。以这种方式，可以有效地保持聚类的一致性，在整个阶段，可以显着降低GPU存储器消耗。因此，我们的方法可以解决集群不一致的问题，并且适用于较大的数据集。此外，我们采用不同的聚类算法来展示我们框架的鲁棒性和泛化。与标准无监督的重新ID管道的集群对比的应用达到了9.9％，8.3％，12.1％的显着改善，而最新的无人纯粹无监督的重新ID方法和5.5％，4.8％，4.4％地图相比与市场，公爵和MSMT17数据集上的最先进的无监督域适应重新ID方法相比。代码可在https://github.com/alibaba/cluster-contrast获得。

尽管具有生成对抗网络（GAN）的图像到图像（I2I）翻译的显着进步，但使用单对生成器和歧视器将图像有效地转换为多个目标域中的一组不同图像仍然具有挑战性。现有的I2i翻译方法采用多个针对不同域的特定于域的内容编码，其中每个特定于域的内容编码器仅经过来自同一域的图像的训练。然而，我们认为应从所有域之间的图像中学到内容（域变相）特征。因此，现有方案的每个特定于域的内容编码器都无法有效提取域不变特征。为了解决这个问题，我们提出了一个灵活而通用的Sologan模型，用于在多个域之间具有未配对数据的多模式I2I翻译。与现有方法相反，Solgan算法使用具有附加辅助分类器的单个投影鉴别器，并为所有域共享编码器和生成器。因此，可以使用来自所有域的图像有效地训练Solgan，从而可以有效提取域 - 不变性内容表示。在多个数据集中，针对多个同行和sologan的变体的定性和定量结果证明了该方法的优点，尤其是对于挑战i2i翻译数据集的挑战，即涉及极端形状变化的数据集或在翻译后保持复杂的背景，需要保持复杂的背景。此外，我们通过消融研究证明了Sogan中每个成分的贡献。