基于深度学习的路面裂缝检测方法通常需要大规模标签，具有详细的裂缝位置信息来学习准确的预测。然而，在实践中，由于路面裂缝的各种视觉模式，裂缝位置很难被手动注释。在本文中，我们提出了一种基于深域适应的裂缝检测网络（DDACDN），其学会利用源域知识来预测目标域中的多类别裂缝位置信息，其中仅是图像级标签可用的。具体地，DDACDN首先通过双分支权重共享骨干网络从源和目标域中提取裂缝特征。并且在实现跨域自适应的努力中，通过从每个域的特征空间聚合三尺度特征来构建中间域，以使来自源域的裂缝特征适应目标域。最后，该网络涉及两个域的知识，并接受识别和本地化路面裂缝的培训。为了便于准确的培训和验证域适应，我们使用两个具有挑战性的路面裂缝数据集CQu-BPDD和RDD2020。此外，我们构建了一个名为CQu-BPMDD的新型大型沥青路面多标签疾病数据集，其中包含38994个高分辨率路面疾病图像，以进一步评估模型的稳健性。广泛的实验表明，DDACDN优于最先进的路面裂纹检测方法，以预测目标结构域的裂缝位置。

我们提出了一个新颖的深度学习框架，称为迭代优化的补丁标签推理网络（IOPLIN），用于自动检测不仅限于特定的路面困扰，例如裂缝和坑洼。 Ioplin可以通过预期最大化启发的补丁标签蒸馏（EMIPLD）策略进行迭代训练，并通过从路面图像中推断贴片标签来很好地完成此任务。 Ioplin在最先进的单个分支CNN模型（例如Googlenet和ExcelificeNet）上享有许多理想的属性。它能够处理不同分辨率中的图像，并充分利用图像信息，尤其是对于高分辨率图像，因为Ioplin从未修复的图像贴片中提取了视觉特征，而不是整个大小的整个图像。此外，它可以在训练阶段使用任何先前的本地化信息而大致地将路面困扰定位。为了更好地评估我们方法在实践中的有效性，我们构建了一个名为CQU-BPDD的大规模沥青疾病检测数据集，该数据集由60,059个高分辨率路面图像组成，这些数据集在不同的时间从不同地区获取。该数据集的广泛结果证明了Ioplin在自动路面遇险检测中的最先进图像分类方法的优势。 The source codes of IOPLIN are released on \url{https://github.com/DearCaat/ioplin}, and the CQU-BPDD dataset is able to be accessed on \url{https://dearcaat.github.io/CQU -bpdd/}。

语义细分是一种关键技术，涉及高分辨率遥感（HRS）图像的自动解释，并引起了遥感社区的广泛关注。由于其层次表示能力，深度卷积神经网络（DCNN）已成功应用于HRS图像语义分割任务。但是，对大量培训数据的严重依赖性以及对数据分布变化的敏感性严重限制了DCNNS在HRS图像的语义分割中的潜在应用。这项研究提出了一种新型的无监督域适应性语义分割网络（MemoryAdaptnet），用于HRS图像的语义分割。 MemoryAdaptnet构建了一种输出空间对抗学习方案，以弥合源域和目标域之间的域分布差异，并缩小域移位的影响。具体而言，我们嵌入了一个不变的特征内存模块来存储不变的域级上下文信息，因为从对抗学习获得的功能仅代表当前有限输入的变体特征。该模块由类别注意力驱动的不变域级上下文集合模块集成到当前伪不变功能，以进一步增强像素表示。基于熵的伪标签滤波策略用于更新当前目标图像的高额伪不变功能的内存模块。在三个跨域任务下进行的广泛实验表明，我们提出的记忆ADAPTNET非常优于最新方法。

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.

在域移位下，跨域几个射击对象检测旨在通过一些注释的目标数据适应目标域中的对象检测器。存在两个重大挑战：（1）高度不足的目标域数据； （2）潜在的过度适应和误导性是由不当放大的目标样本而没有任何限制引起的。为了应对这些挑战，我们提出了一种由两个部分组成的自适应方法。首先，我们提出了一种自适应优化策略，以选择类似于目标样本的增强数据，而不是盲目增加数量。具体而言，我们过滤了增强的候选者，这些候选者在一开始就显着偏离了目标特征分布。其次，为了进一步释放数据限制，我们提出了多级域感知数据增强，以增加增强数据的多样性和合理性，从而利用了跨图像前景 - 背景混合物。实验表明，所提出的方法在多个基准测试中实现了最先进的性能。

高分辨率卫星图像可以为土地覆盖分类提供丰富的详细空间信息，这对于研究复杂的建筑环境尤为重要。但是，由于覆盖范围复杂的覆盖模式，昂贵的训练样品收集以及卫星图像的严重分布变化，很少有研究应用高分辨率图像来大规模详细类别的覆盖地图。为了填补这一空白，我们提出了一个大规模的土地盖数据集，即五亿像素。它包含超过50亿个标记的像素，这些像素由150个高分辨率Gaofen-2（4 M）卫星图像，在24类系统中注释，涵盖人工结构，农业和自然阶层。此外，我们提出了一种基于深度学习的无监督域适应方法，该方法可以转移在标记的数据集（称为源域）上训练的分类模型，以获取大型土地覆盖映射的无标记数据（称为目标域） 。具体而言，我们采用动态伪标签分配和班级平衡策略来介绍一个端到端的暹罗网络，以执行自适应领域联合学习。为了验证我们的数据集的普遍性以及在不同的传感器和不同地理区域中提出的方法，我们对中国的五个大城市和其他五个亚洲国家的五个城市进行了土地覆盖地图，以下情况下使用：Planetscope（3 m），Gaofen-1，Gaofen-1 （8 m）和Sentinel-2（10 m）卫星图像。在总研究区域为60,000平方公里，即使输入图像完全未标记，实验也显示出令人鼓舞的结果。拟议的方法接受了5亿像素数据集的培训，可实现在整个中国和其他亚洲国家的高质量和详细的土地覆盖地图。

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。

最近的智能故障诊断（IFD）的进展大大依赖于深度代表学习和大量标记数据。然而，机器通常以各种工作条件操作，或者目标任务具有不同的分布，其中包含用于训练的收集数据（域移位问题）。此外，目标域中的新收集的测试数据通常是未标记的，导致基于无监督的深度转移学习（基于UDTL为基础的）IFD问题。虽然它已经实现了巨大的发展，但标准和开放的源代码框架以及基于UDTL的IFD的比较研究尚未建立。在本文中，我们根据不同的任务，构建新的分类系统并对基于UDTL的IFD进行全面审查。对一些典型方法和数据集的比较分析显示了基于UDTL的IFD中的一些开放和基本问题，这很少研究，包括特征，骨干，负转移，物理前导等的可转移性，强调UDTL的重要性和再现性 - 基于IFD，整个测试框架将发布给研究界以促进未来的研究。总之，发布的框架和比较研究可以作为扩展界面和基本结果，以便对基于UDTL的IFD进行新的研究。代码框架可用于\ url {https:/github.com/zhaozhibin/udtl}。

Domain adaptive object detection (DAOD) aims to alleviate transfer performance degradation caused by the cross-domain discrepancy. However, most existing DAOD methods are dominated by computationally intensive two-stage detectors, which are not the first choice for industrial applications. In this paper, we propose a novel semi-supervised domain adaptive YOLO (SSDA-YOLO) based method to improve cross-domain detection performance by integrating the compact one-stage detector YOLOv5 with domain adaptation. Specifically, we adapt the knowledge distillation framework with the Mean Teacher model to assist the student model in obtaining instance-level features of the unlabeled target domain. We also utilize the scene style transfer to cross-generate pseudo images in different domains for remedying image-level differences. In addition, an intuitive consistency loss is proposed to further align cross-domain predictions. We evaluate our proposed SSDA-YOLO on public benchmarks including PascalVOC, Clipart1k, Cityscapes, and Foggy Cityscapes. Moreover, to verify its generalization, we conduct experiments on yawning detection datasets collected from various classrooms. The results show considerable improvements of our method in these DAOD tasks. Our code is available on \url{https://github.com/hnuzhy/SSDA-YOLO}.

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。

Due to object detection's close relationship with video analysis and image understanding, it has attracted much research attention in recent years. Traditional object detection methods are built on handcrafted features and shallow trainable architectures. Their performance easily stagnates by constructing complex ensembles which combine multiple low-level image features with high-level context from object detectors and scene classifiers. With the rapid development in deep learning, more powerful tools, which are able to learn semantic, high-level, deeper features, are introduced to address the problems existing in traditional architectures. These models behave differently in network architecture, training strategy and optimization function, etc. In this paper, we provide a review on deep learning based object detection frameworks. Our review begins with a brief introduction on the history of deep learning and its representative tool, namely Convolutional Neural Network (CNN). Then we focus on typical generic object detection architectures along with some modifications and useful tricks to improve detection performance further. As distinct specific detection tasks exhibit different characteristics, we also briefly survey several specific tasks, including salient object detection, face detection and pedestrian detection. Experimental analyses are also provided to compare various methods and draw some meaningful conclusions. Finally, several promising directions and tasks are provided to serve as guidelines for future work in both object detection and relevant neural network based learning systems.

Domain adaptive detection aims to improve the generalization of detectors on target domain. To reduce discrepancy in feature distributions between two domains, recent approaches achieve domain adaption through feature alignment in different granularities via adversarial learning. However, they neglect the relationship between multiple granularities and different features in alignment, degrading detection. Addressing this, we introduce a unified multi-granularity alignment (MGA)-based detection framework for domain-invariant feature learning. The key is to encode the dependencies across different granularities including pixel-, instance-, and category-levels simultaneously to align two domains. Specifically, based on pixel-level features, we first develop an omni-scale gated fusion (OSGF) module to aggregate discriminative representations of instances with scale-aware convolutions, leading to robust multi-scale detection. Besides, we introduce multi-granularity discriminators to identify where, either source or target domains, different granularities of samples come from. Note that, MGA not only leverages instance discriminability in different categories but also exploits category consistency between two domains for detection. Furthermore, we present an adaptive exponential moving average (AEMA) strategy that explores model assessments for model update to improve pseudo labels and alleviate local misalignment problem, boosting detection robustness. Extensive experiments on multiple domain adaption scenarios validate the superiority of MGA over other approaches on FCOS and Faster R-CNN detectors. Code will be released at https://github.com/tiankongzhang/MGA.

X-ray imaging technology has been used for decades in clinical tasks to reveal the internal condition of different organs, and in recent years, it has become more common in other areas such as industry, security, and geography. The recent development of computer vision and machine learning techniques has also made it easier to automatically process X-ray images and several machine learning-based object (anomaly) detection, classification, and segmentation methods have been recently employed in X-ray image analysis. Due to the high potential of deep learning in related image processing applications, it has been used in most of the studies. This survey reviews the recent research on using computer vision and machine learning for X-ray analysis in industrial production and security applications and covers the applications, techniques, evaluation metrics, datasets, and performance comparison of those techniques on publicly available datasets. We also highlight some drawbacks in the published research and give recommendations for future research in computer vision-based X-ray analysis.

Single-frame InfraRed Small Target (SIRST) detection has been a challenging task due to a lack of inherent characteristics, imprecise bounding box regression, a scarcity of real-world datasets, and sensitive localization evaluation. In this paper, we propose a comprehensive solution to these challenges. First, we find that the existing anchor-free label assignment method is prone to mislabeling small targets as background, leading to their omission by detectors. To overcome this issue, we propose an all-scale pseudo-box-based label assignment scheme that relaxes the constraints on scale and decouples the spatial assignment from the size of the ground-truth target. Second, motivated by the structured prior of feature pyramids, we introduce the one-stage cascade refinement network (OSCAR), which uses the high-level head as soft proposals for the low-level refinement head. This allows OSCAR to process the same target in a cascade coarse-to-fine manner. Finally, we present a new research benchmark for infrared small target detection, consisting of the SIRST-V2 dataset of real-world, high-resolution single-frame targets, the normalized contrast evaluation metric, and the DeepInfrared toolkit for detection. We conduct extensive ablation studies to evaluate the components of OSCAR and compare its performance to state-of-the-art model-driven and data-driven methods on the SIRST-V2 benchmark. Our results demonstrate that a top-down cascade refinement framework can improve the accuracy of infrared small target detection without sacrificing efficiency. The DeepInfrared toolkit, dataset, and trained models are available at https://github.com/YimianDai/open-deepinfrared to advance further research in this field.

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。

在图像分类中，获得足够的标签通常昂贵且耗时。为了解决这个问题，域适应通常提供有吸引力的选择，给出了来自类似性质但不同域的大量标记数据。现有方法主要对准单个结构提取的表示的分布，并且表示可以仅包含部分信息，例如，仅包含部分饱和度，亮度和色调信息。在这一行中，我们提出了多代表性适应，这可以大大提高跨域图像分类的分类精度，并且特别旨在对准由名为Inception Adaption Adationation模块（IAM）提取的多个表示的分布。基于此，我们呈现多色自适应网络（MRAN）来通过多表示对准完成跨域图像分类任务，该任向性可以捕获来自不同方面的信息。此外，我们扩展了最大的平均差异（MMD）来计算适应损耗。我们的方法可以通过扩展具有IAM的大多数前进模型来轻松实现，并且网络可以通过反向传播有效地培训。在三个基准图像数据集上进行的实验证明了备的有效性。代码已在https://github.com/easezyc/deep-transfer -learning上获得。

With the rapid development of artificial intelligence (AI) in medical image processing, deep learning in color fundus photography (CFP) analysis is also evolving. Although there are some open-source, labeled datasets of CFPs in the ophthalmology community, large-scale datasets for screening only have labels of disease categories, and datasets with annotations of fundus structures are usually small in size. In addition, labeling standards are not uniform across datasets, and there is no clear information on the acquisition device. Here we release a multi-annotation, multi-quality, and multi-device color fundus image dataset for glaucoma analysis on an original challenge -- Retinal Fundus Glaucoma Challenge 2nd Edition (REFUGE2). The REFUGE2 dataset contains 2000 color fundus images with annotations of glaucoma classification, optic disc/cup segmentation, as well as fovea localization. Meanwhile, the REFUGE2 challenge sets three sub-tasks of automatic glaucoma diagnosis and fundus structure analysis and provides an online evaluation framework. Based on the characteristics of multi-device and multi-quality data, some methods with strong generalizations are provided in the challenge to make the predictions more robust. This shows that REFUGE2 brings attention to the characteristics of real-world multi-domain data, bridging the gap between scientific research and clinical application.

主观认知下降（SCD）是阿尔茨海默氏病（AD）的临床前阶段，甚至在轻度认知障碍（MCI）之前就发生。渐进式SCD将转换为MCI，并有可能进一步发展为AD。因此，通过神经成像技术（例如，结构MRI）对进行性SCD的早期鉴定对于AD的早期干预具有巨大的临床价值。但是，现有的基于MRI的机器/深度学习方法通​​常会遇到小样本大小的问题，这对相关的神经影像学分析构成了巨大挑战。我们旨在解决本文的主要问题是如何利用相关领域（例如AD/NC）协助SCD的进展预测。同时，我们担心哪些大脑区域与进行性SCD的识别更加紧密相关。为此，我们提出了一个注意引导自动编码器模型，以进行有效的跨域适应，以促进知识转移从AD到SCD。所提出的模型由四个关键组成部分组成：1）用于学习不同域的共享子空间表示的功能编码模块，2）用于自动定义大脑中定义的兴趣障碍区域的注意模块，3）用于重构的解码模块原始输入，4）用于鉴定脑疾病的分类模块。通过对这四个模块的联合培训，可以学习域不变功能。同时，注意机制可以强调与脑部疾病相关的区域。公开可用的ADNI数据集和私人CLAS数据集的广泛实验证明了该方法的有效性。提出的模型直接可以在CPU上仅5-10秒进行训练和测试，并且适用于具有小数据集的医疗任务。

Domain adaptation aims to bridge the domain shifts between the source and the target domain. These shifts may span different dimensions such as fog, rainfall, etc. However, recent methods typically do not consider explicit prior knowledge about the domain shifts on a specific dimension, thus leading to less desired adaptation performance. In this paper, we study a practical setting called Specific Domain Adaptation (SDA) that aligns the source and target domains in a demanded-specific dimension. Within this setting, we observe the intra-domain gap induced by different domainness (i.e., numerical magnitudes of domain shifts in this dimension) is crucial when adapting to a specific domain. To address the problem, we propose a novel Self-Adversarial Disentangling (SAD) framework. In particular, given a specific dimension, we first enrich the source domain by introducing a domainness creator with providing additional supervisory signals. Guided by the created domainness, we design a self-adversarial regularizer and two loss functions to jointly disentangle the latent representations into domainness-specific and domainness-invariant features, thus mitigating the intra-domain gap. Our method can be easily taken as a plug-and-play framework and does not introduce any extra costs in the inference time. We achieve consistent improvements over state-of-the-art methods in both object detection and semantic segmentation.

从卷积神经网络的快速发展中受益，汽车牌照检测和识别的性能得到了很大的改善。但是，大多数现有方法分别解决了检测和识别问题，并专注于特定方案，这阻碍了现实世界应用的部署。为了克服这些挑战，我们提出了一个有效而准确的框架，以同时解决车牌检测和识别任务。这是一个轻巧且统一的深神经网络，可以实时优化端到端。具体而言，对于不受约束的场景，采用了无锚方法来有效检测车牌的边界框和四个角，这些框用于提取和纠正目标区域特征。然后，新型的卷积神经网络分支旨在进一步提取角色的特征而不分割。最后，将识别任务视为序列标记问题，这些问题通过连接派时间分类（CTC）解决。选择了几个公共数据集，包括在各种条件下从不同方案中收集的图像进行评估。实验结果表明，所提出的方法在速度和精度上都显着优于先前的最新方法。