语义分割在广泛的计算机视觉应用中起着基本作用,提供了全球对图像​​的理解的关键信息。然而,最先进的模型依赖于大量的注释样本,其比在诸如图像分类的任务中获得更昂贵的昂贵的样本。由于未标记的数据替代地获得更便宜,因此无监督的域适应达到了语义分割社区的广泛成功并不令人惊讶。本调查致力于总结这一令人难以置信的快速增长的领域的五年,这包含了语义细分本身的重要性,以及将分段模型适应新环境的关键需求。我们提出了最重要的语义分割方法;我们对语义分割的域适应技术提供了全面的调查;我们揭示了多域学习,域泛化,测试时间适应或无源域适应等较新的趋势;我们通过描述在语义细分研究中最广泛使用的数据集和基准测试来结束本调查。我们希望本调查将在学术界和工业中提供具有全面参考指导的研究人员,并有助于他们培养现场的新研究方向。
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深度学习已成为解决不同领域中现实世界中问题的首选方法,部分原因是它能够从数据中学习并在广泛的应用程序上实现令人印象深刻的性能。但是,它的成功通常取决于两个假设:(i)精确模型拟合需要大量标记的数据集,并且(ii)培训和测试数据是独立的且分布相同的。因此,不能保证它在看不见的目标域上的性能,尤其是在适应阶段遇到分布数据的数据时。目标域中数据的性能下降是部署深层神经网络的关键问题,这些网络已成功地在源域中的数据训练。通过利用标记的源域数据和未标记的目标域数据来执行目标域中的各种任务,提出了无监督的域适应(UDA)来对抗这一点。 UDA在自然图像处理,视频分析,自然语言处理,时间序列数据分析,医学图像分析等方面取得了令人鼓舞的结果。在本综述中,作为一个快速发展的主题,我们对其方法和应用程序进行了系统的比较。此外,还讨论了UDA与其紧密相关的任务的联系,例如域的概括和分布外检测。此外,突出显示了当前方法和可能有希望的方向的缺陷。
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Deep learning has produced state-of-the-art results for a variety of tasks. While such approaches for supervised learning have performed well, they assume that training and testing data are drawn from the same distribution, which may not always be the case. As a complement to this challenge, single-source unsupervised domain adaptation can handle situations where a network is trained on labeled data from a source domain and unlabeled data from a related but different target domain with the goal of performing well at test-time on the target domain. Many single-source and typically homogeneous unsupervised deep domain adaptation approaches have thus been developed, combining the powerful, hierarchical representations from deep learning with domain adaptation to reduce reliance on potentially-costly target data labels. This survey will compare these approaches by examining alternative methods, the unique and common elements, results, and theoretical insights. We follow this with a look at application areas and open research directions.
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在过去的十年中,许多深入学习模型都受到了良好的培训,并在各种机器智能领域取得了巨大成功,特别是对于计算机视觉和自然语言处理。为了更好地利用这些训练有素的模型在域内或跨域转移学习情况下,提出了知识蒸馏(KD)和域适应(DA)并成为研究亮点。他们旨在通过原始培训数据从训练有素的模型转移有用的信息。但是,由于隐私,版权或机密性,原始数据并不总是可用的。最近,无数据知识转移范式吸引了吸引人的关注,因为它涉及从训练有素的模型中蒸馏宝贵的知识,而无需访问培训数据。特别是,它主要包括无数据知识蒸馏(DFKD)和源无数据域适应(SFDA)。一方面,DFKD旨在将域名域内知识从一个麻烦的教师网络转移到一个紧凑的学生网络,以进行模型压缩和有效推论。另一方面,SFDA的目标是重用存储在训练有素的源模型中的跨域知识并将其调整为目标域。在本文中,我们对知识蒸馏和无监督域适应的视角提供了全面的数据知识转移,以帮助读者更好地了解目前的研究状况和想法。分别简要审查了这两个领域的应用和挑战。此外,我们对未来研究的主题提供了一些见解。
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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.
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Unsupervised domain adaptation (UDA) via deep learning has attracted appealing attention for tackling domain-shift problems caused by distribution discrepancy across different domains. Existing UDA approaches highly depend on the accessibility of source domain data, which is usually limited in practical scenarios due to privacy protection, data storage and transmission cost, and computation burden. To tackle this issue, many source-free unsupervised domain adaptation (SFUDA) methods have been proposed recently, which perform knowledge transfer from a pre-trained source model to unlabeled target domain with source data inaccessible. A comprehensive review of these works on SFUDA is of great significance. In this paper, we provide a timely and systematic literature review of existing SFUDA approaches from a technical perspective. Specifically, we categorize current SFUDA studies into two groups, i.e., white-box SFUDA and black-box SFUDA, and further divide them into finer subcategories based on different learning strategies they use. We also investigate the challenges of methods in each subcategory, discuss the advantages/disadvantages of white-box and black-box SFUDA methods, conclude the commonly used benchmark datasets, and summarize the popular techniques for improved generalizability of models learned without using source data. We finally discuss several promising future directions in this field.
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对分布(OOD)数据的概括是人类自然的能力,但对于机器而言挑战。这是因为大多数学习算法强烈依赖于i.i.d.〜对源/目标数据的假设,这在域转移导致的实践中通常会违反。域的概括(DG)旨在通过仅使用源数据进行模型学习来实现OOD的概括。在过去的十年中,DG的研究取得了长足的进步,导致了广泛的方法论,例如,基于域的一致性,元学习,数据增强或合奏学习的方法,仅举几例;还在各个应用领域进行了研究,包括计算机视觉,语音识别,自然语言处理,医学成像和强化学习。在本文中,首次提供了DG中的全面文献综述,以总结过去十年来的发展。具体而言,我们首先通过正式定义DG并将其与其他相关领域(如域适应和转移学习)联系起来来涵盖背景。然后,我们对现有方法和理论进行了彻底的审查。最后,我们通过有关未来研究方向的见解和讨论来总结这项调查。
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Image segmentation is a key topic in image processing and computer vision with applications such as scene understanding, medical image analysis, robotic perception, video surveillance, augmented reality, and image compression, among many others. Various algorithms for image segmentation have been developed in the literature. Recently, due to the success of deep learning models in a wide range of vision applications, there has been a substantial amount of works aimed at developing image segmentation approaches using deep learning models. In this survey, we provide a comprehensive review of the literature at the time of this writing, covering a broad spectrum of pioneering works for semantic and instance-level segmentation, including fully convolutional pixel-labeling networks, encoder-decoder architectures, multi-scale and pyramid based approaches, recurrent networks, visual attention models, and generative models in adversarial settings. We investigate the similarity, strengths and challenges of these deep learning models, examine the most widely used datasets, report performances, and discuss promising future research directions in this area.
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最近,面部生物识别是对传统认证系统的方便替代的巨大关注。因此,检测恶意尝试已经发现具有重要意义,导致面部抗欺骗〜(FAS),即面部呈现攻击检测。与手工制作的功能相反,深度特色学习和技术已经承诺急剧增加FAS系统的准确性,解决了实现这种系统的真实应用的关键挑战。因此,处理更广泛的发展以及准确的模型的新研究区越来越多地引起了研究界和行业的关注。在本文中,我们为自2017年以来对与基于深度特征的FAS方法相关的文献综合调查。在这一主题上阐明,基于各种特征和学习方法的语义分类。此外,我们以时间顺序排列,其进化进展和评估标准(数据集内集和数据集互联集合中集)覆盖了FAS的主要公共数据集。最后,我们讨论了开放的研究挑战和未来方向。
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由于其在保护面部识别系统免于演示攻击(PAS)中的至关重要的作用,因此面部抗散热器(FAS)最近引起了人们的关注。随着越来越现实的PA随着新颖类型的发展,由于其表示能力有限,基于手工特征的传统FAS方法变得不可靠。随着近十年来大规模学术数据集的出现,基于深度学习的FA实现了卓越的性能并占据了这一领域。但是,该领域的现有评论主要集中在手工制作的功能上,这些功能过时,对FAS社区的进步没有任何启发。在本文中,为了刺激未来的研究,我们对基于深度学习的FAS的最新进展进行了首次全面综述。它涵盖了几个新颖且有见地的组成部分:1)除了使用二进制标签的监督(例如,``0'''for pas vs.'1'),我们还通过像素智能监督(例如,伪深度图)调查了最新方法; 2)除了传统的数据内评估外,我们还收集和分析专门为域概括和开放式FAS设计的最新方法; 3)除了商用RGB摄像机外,我们还总结了多模式(例如,深度和红外线)或专门(例如,光场和闪存)传感器下的深度学习应用程序。我们通过强调当前的开放问题并突出潜在的前景来结束这项调查。
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深度学习的快速发展在分割方面取得了长足的进步,这是计算机视觉的基本任务之一。但是,当前的细分算法主要取决于像素级注释的可用性,这些注释通常昂贵,乏味且费力。为了减轻这一负担,过去几年见证了越来越多的关注,以建立标签高效,深度学习的细分算法。本文对标签有效的细分方法进行了全面的审查。为此,我们首先根据不同类型的弱标签提供的监督(包括没有监督,粗略监督,不完整的监督和嘈杂的监督和嘈杂的监督),首先开发出一种分类法来组织这些方法,并通过细分类型(包括语义细分)补充,实例分割和全景分割)。接下来,我们从统一的角度总结了现有的标签有效的细分方法,该方法讨论了一个重要的问题:如何弥合弱监督和密集预测之间的差距 - 当前的方法主要基于启发式先导,例如交叉像素相似性,跨标签约束,跨视图一致性,跨图像关系等。最后,我们分享了对标签有效深层细分的未来研究方向的看法。
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在许多图像引导的临床方法中,医学图像分割是一个基本和关键的步骤。基于深度学习的细分方法的最新成功通常取决于大量标记的数据,这特别困难且昂贵,尤其是在医学成像领域中,只有专家才能提供可靠和准确的注释。半监督学习已成为一种吸引人的策略,并广泛应用于医学图像分割任务,以训练注释有限的深层模型。在本文中,我们对最近提议的半监督学习方法进行了全面综述,并总结了技术新颖性和经验结果。此外,我们分析和讨论现有方法的局限性和几个未解决的问题。我们希望这篇评论可以激发研究界探索解决这一挑战的解决方案,并进一步促进医学图像细分领域的发展。
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虽然在许多域内生成并提供了大量的未标记数据,但对视觉数据的自动理解的需求高于以往任何时候。大多数现有机器学习模型通常依赖于大量标记的训练数据来实现高性能。不幸的是,在现实世界的应用中,不能满足这种要求。标签的数量有限,手动注释数据昂贵且耗时。通常需要将知识从现有标记域传输到新域。但是,模型性能因域之间的差异(域移位或数据集偏差)而劣化。为了克服注释的负担,域适应(DA)旨在在将知识从一个域转移到另一个类似但不同的域中时减轻域移位问题。无监督的DA(UDA)处理标记的源域和未标记的目标域。 UDA的主要目标是减少标记的源数据和未标记的目标数据之间的域差异,并在培训期间在两个域中学习域不变的表示。在本文中,我们首先定义UDA问题。其次,我们从传统方法和基于深度学习的方法中概述了不同类别的UDA的最先进的方法。最后,我们收集常用的基准数据集和UDA最先进方法的报告结果对视觉识别问题。
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Semantic segmentation is a key problem for many computer vision tasks. While approaches based on convolutional neural networks constantly break new records on different benchmarks, generalizing well to diverse testing environments remains a major challenge. In numerous real world applications, there is indeed a large gap between data distributions in train and test domains, which results in severe performance loss at run-time. In this work, we address the task of unsupervised domain adaptation in semantic segmentation with losses based on the entropy of the pixel-wise predictions. To this end, we propose two novel, complementary methods using (i) an entropy loss and (ii) an adversarial loss respectively. We demonstrate state-of-theart performance in semantic segmentation on two challenging "synthetic-2-real" set-ups 1 and show that the approach can also be used for detection.
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机器学习系统通常假设训练和测试分布是相同的。为此,关键要求是开发可以概括到未经看不见的分布的模型。领域泛化(DG),即分销概括,近年来引起了越来越令人利益。域概括处理了一个具有挑战性的设置,其中给出了一个或几个不同但相关域,并且目标是学习可以概括到看不见的测试域的模型。多年来,域概括地区已经取得了巨大进展。本文提出了对该地区最近进步的首次审查。首先,我们提供了域泛化的正式定义,并讨论了几个相关领域。然后,我们彻底审查了与域泛化相关的理论,并仔细分析了泛化背后的理论。我们将最近的算法分为三个类:数据操作,表示学习和学习策略,并为每个类别详细介绍几种流行的算法。第三,我们介绍常用的数据集,应用程序和我们的开放源代码库进行公平评估。最后,我们总结了现有文学,并为未来提供了一些潜在的研究主题。
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While deep learning methods hitherto have achieved considerable success in medical image segmentation, they are still hampered by two limitations: (i) reliance on large-scale well-labeled datasets, which are difficult to curate due to the expert-driven and time-consuming nature of pixel-level annotations in clinical practices, and (ii) failure to generalize from one domain to another, especially when the target domain is a different modality with severe domain shifts. Recent unsupervised domain adaptation~(UDA) techniques leverage abundant labeled source data together with unlabeled target data to reduce the domain gap, but these methods degrade significantly with limited source annotations. In this study, we address this underexplored UDA problem, investigating a challenging but valuable realistic scenario, where the source domain not only exhibits domain shift~w.r.t. the target domain but also suffers from label scarcity. In this regard, we propose a novel and generic framework called ``Label-Efficient Unsupervised Domain Adaptation"~(LE-UDA). In LE-UDA, we construct self-ensembling consistency for knowledge transfer between both domains, as well as a self-ensembling adversarial learning module to achieve better feature alignment for UDA. To assess the effectiveness of our method, we conduct extensive experiments on two different tasks for cross-modality segmentation between MRI and CT images. Experimental results demonstrate that the proposed LE-UDA can efficiently leverage limited source labels to improve cross-domain segmentation performance, outperforming state-of-the-art UDA approaches in the literature. Code is available at: https://github.com/jacobzhaoziyuan/LE-UDA.
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Transfer learning aims at improving the performance of target learners on target domains by transferring the knowledge contained in different but related source domains. In this way, the dependence on a large number of target domain data can be reduced for constructing target learners. Due to the wide application prospects, transfer learning has become a popular and promising area in machine learning. Although there are already some valuable and impressive surveys on transfer learning, these surveys introduce approaches in a relatively isolated way and lack the recent advances in transfer learning. Due to the rapid expansion of the transfer learning area, it is both necessary and challenging to comprehensively review the relevant studies. This survey attempts to connect and systematize the existing transfer learning researches, as well as to summarize and interpret the mechanisms and the strategies of transfer learning in a comprehensive way, which may help readers have a better understanding of the current research status and ideas. Unlike previous surveys, this survey paper reviews more than forty representative transfer learning approaches, especially homogeneous transfer learning approaches, from the perspectives of data and model. The applications of transfer learning are also briefly introduced. In order to show the performance of different transfer learning models, over twenty representative transfer learning models are used for experiments. The models are performed on three different datasets, i.e., Amazon Reviews, Reuters-21578, and Office-31. And the experimental results demonstrate the importance of selecting appropriate transfer learning models for different applications in practice.
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在深度学习研究中,自学学习(SSL)引起了极大的关注,引起了计算机视觉和遥感社区的兴趣。尽管计算机视觉取得了很大的成功,但SSL在地球观测领域的大部分潜力仍然锁定。在本文中,我们对在遥感的背景下为计算机视觉的SSL概念和最新发展提供了介绍,并回顾了SSL中的概念和最新发展。此外,我们在流行的遥感数据集上提供了现代SSL算法的初步基准,从而验证了SSL在遥感中的潜力,并提供了有关数据增强的扩展研究。最后,我们确定了SSL未来研究的有希望的方向的地球观察(SSL4EO),以铺平了两个领域的富有成效的相互作用。
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Domain adaptation is critical for success in new, unseen environments. Adversarial adaptation models applied in feature spaces discover domain invariant representations, but are difficult to visualize and sometimes fail to capture pixel-level and low-level domain shifts. Recent work has shown that generative adversarial networks combined with cycle-consistency constraints are surprisingly effective at mapping images between domains, even without the use of aligned image pairs. We propose a novel discriminatively-trained Cycle-Consistent Adversarial Domain Adaptation model. CyCADA adapts representations at both the pixel-level and feature-level, enforces cycle-consistency while leveraging a task loss, and does not require aligned pairs. Our model can be applied in a variety of visual recognition and prediction settings. We show new state-of-the-art results across multiple adaptation tasks, including digit classification and semantic segmentation of road scenes demonstrating transfer from synthetic to real world domains.
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With the development of convolutional neural networks, hundreds of deep learning based dehazing methods have been proposed. In this paper, we provide a comprehensive survey on supervised, semi-supervised, and unsupervised single image dehazing. We first discuss the physical model, datasets, network modules, loss functions, and evaluation metrics that are commonly used. Then, the main contributions of various dehazing algorithms are categorized and summarized. Further, quantitative and qualitative experiments of various baseline methods are carried out. Finally, the unsolved issues and challenges that can inspire the future research are pointed out. A collection of useful dehazing materials is available at \url{https://github.com/Xiaofeng-life/AwesomeDehazing}.
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