本文提出了一种基于生成的对抗网络（GAN）的解决方案，用于求解拼图游戏。问题假定图像被分成相等的方块，并要求根据碎片提供的信息恢复图像。传统的拼图拼写求解器通常根据拼写的边界确定关系，这忽略了重要的语义信息。在本文中，我们提出了一种基于GaN的辅助学习方法，用于用未配对的图像求解拼图拼图的GaN的辅助学习方法（没有初始图像的先验知识）。我们设计了一个多任务管道，包括（1）分类分支来对拼图排列，并且（2）GaN分支以正确的顺序恢复图像的图像。分类分支由根据洗片件产生的伪标签约束。 GaN分支专注于图像语义信息，其中发电机产生自然图像以欺骗鉴别器，而判别器区分给定图像是否属于合成或真实目标域。这两个分支通过流动的扭曲模块连接，该模块应用于扭曲特征以根据分类结果校正订单。所提出的方法可以通过同时利用语义信息和边界信息来更有效地解决拼图难题。针对几个代表性拼图求解器的定性和定量比较证明了我们方法的优越性。

本文提出了一种有效融合多暴露输入并使用未配对数据集生成高质量的高动态范围（HDR）图像的方法。基于深度学习的HDR图像生成方法在很大程度上依赖于配对的数据集。地面真相图像在生成合理的HDR图像中起着领导作用。没有地面真理的数据集很难应用于训练深层神经网络。最近，在没有配对示例的情况下，生成对抗网络（GAN）证明了它们将图像从源域X转换为目标域y的潜力。在本文中，我们提出了一个基于GAN的网络，用于解决此类问题，同时产生愉快的HDR结果，名为Uphdr-Gan。提出的方法放松了配对数据集的约束，并了解了从LDR域到HDR域的映射。尽管丢失了这些对数据，但UPHDR-GAN可以借助修改后的GAN丢失，改进的歧视器网络和有用的初始化阶段正确处理由移动对象或未对准引起的幽灵伪像。所提出的方法保留了重要区域的细节并提高了总图像感知质量。与代表性方法的定性和定量比较证明了拟议的UPHDR-GAN的优越性。

基于深度学习的低光图像增强方法通常需要巨大的配对训练数据，这对于在现实世界的场景中捕获是不切实际的。最近，已经探索了无监督的方法来消除对成对训练数据的依赖。然而，由于没有前衣，它们在不同的现实情景中表现得不稳定。为了解决这个问题，我们提出了一种基于先前（HEP）的有效预期直方图均衡的无监督的低光图像增强方法。我们的作品受到了有趣的观察，即直方图均衡增强图像的特征图和地面真理是相似的。具体而言，我们制定了HEP，提供了丰富的纹理和亮度信息。嵌入一​​个亮度模块（LUM），它有助于将低光图像分解为照明和反射率图，并且反射率图可以被视为恢复的图像。然而，基于Retinex理论的推导揭示了反射率图被噪声污染。我们介绍了一个噪声解剖学模块（NDM），以解除反射率图中的噪声和内容，具有不配对清洁图像的可靠帮助。通过直方图均衡的先前和噪声解剖，我们的方法可以恢复更精细的细节，更有能力抑制现实世界低光场景中的噪声。广泛的实验表明，我们的方法对最先进的无监督的低光增强算法有利地表现出甚至与最先进的监督算法匹配。

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}.

Deep Convolutional Neural Networks (DCNNs) have exhibited impressive performance on image super-resolution tasks. However, these deep learning-based super-resolution methods perform poorly in real-world super-resolution tasks, where the paired high-resolution and low-resolution images are unavailable and the low-resolution images are degraded by complicated and unknown kernels. To break these limitations, we propose the Unsupervised Bi-directional Cycle Domain Transfer Learning-based Generative Adversarial Network (UBCDTL-GAN), which consists of an Unsupervised Bi-directional Cycle Domain Transfer Network (UBCDTN) and the Semantic Encoder guided Super Resolution Network (SESRN). First, the UBCDTN is able to produce an approximated real-like LR image through transferring the LR image from an artificially degraded domain to the real-world LR image domain. Second, the SESRN has the ability to super-resolve the approximated real-like LR image to a photo-realistic HR image. Extensive experiments on unpaired real-world image benchmark datasets demonstrate that the proposed method achieves superior performance compared to state-of-the-art methods.

Image manipulation localization aims at distinguishing forged regions from the whole test image. Although many outstanding prior arts have been proposed for this task, there are still two issues that need to be further studied: 1) how to fuse diverse types of features with forgery clues; 2) how to progressively integrate multistage features for better localization performance. In this paper, we propose a tripartite progressive integration network (TriPINet) for end-to-end image manipulation localization. First, we extract both visual perception information, e.g., RGB input images, and visual imperceptible features, e.g., frequency and noise traces for forensic feature learning. Second, we develop a guided cross-modality dual-attention (gCMDA) module to fuse different types of forged clues. Third, we design a set of progressive integration squeeze-and-excitation (PI-SE) modules to improve localization performance by appropriately incorporating multiscale features in the decoder. Extensive experiments are conducted to compare our method with state-of-the-art image forensics approaches. The proposed TriPINet obtains competitive results on several benchmark datasets.

从医用试剂染色图像中分割牙齿斑块为诊断和确定随访治疗计划提供了宝贵的信息。但是，准确的牙菌斑分割是一项具有挑战性的任务，需要识别牙齿和牙齿斑块受到语义腔区域的影响（即，在牙齿和牙齿斑块之间的边界区域中存在困惑的边界）以及实例形状的复杂变化，这些变化均未完全解决。现有方法。因此，我们提出了一个语义分解网络（SDNET），该网络介绍了两个单任务分支，以分别解决牙齿和牙齿斑块的分割，并设计了其他约束，以学习每个分支的特定类别特征，从而促进语义分解并改善该类别的特征牙齿分割的性能。具体而言，SDNET以分裂方式学习了两个单独的分割分支和牙齿的牙齿，以解除它们之间的纠缠关系。指定类别的每个分支都倾向于产生准确的分割。为了帮助这两个分支更好地关注特定类别的特征，进一步提出了两个约束模块：1）通过最大化不同类别表示之间的距离来学习判别特征表示，以了解判别特征表示形式，以减少减少负面影响关于特征提取的语义腔区域； 2）结构约束模块（SCM）通过监督边界感知的几何约束提供完整的结构信息，以提供各种形状的牙菌斑。此外，我们构建了一个大规模的开源染色牙菌斑分割数据集（SDPSEG），该数据集为牙齿和牙齿提供高质量的注释。 SDPSEG数据集的实验结果显示SDNET达到了最新的性能。

本文的目标是对面部素描合成（FSS）问题进行全面的研究。然而，由于获得了手绘草图数据集的高成本，因此缺乏完整的基准，用于评估过去十年的FSS算法的开发。因此，我们首先向FSS引入高质量的数据集，名为FS2K，其中包括2,104个图像素描对，跨越三种类型的草图样式，图像背景，照明条件，肤色和面部属性。 FS2K与以前的FSS数据集不同于难度，多样性和可扩展性，因此应促进FSS研究的进展。其次，我们通过调查139种古典方法，包括34个手工特征的面部素描合成方法，37个一般的神经式传输方法，43个深映像到图像翻译方法，以及35个图像 - 素描方法。此外，我们详细说明了现有的19个尖端模型的综合实验。第三，我们为FSS提供了一个简单的基准，名为FSGAN。只有两个直截了当的组件，即面部感知屏蔽和风格矢量扩展，FSGAN将超越所提出的FS2K数据集的所有先前最先进模型的性能，通过大边距。最后，我们在过去几年中汲取的经验教训，并指出了几个未解决的挑战。我们的开源代码可在https://github.com/dengpingfan/fsgan中获得。

Deep learning techniques have made considerable progress in image inpainting, restoration, and reconstruction in the last few years. Image outpainting, also known as image extrapolation, lacks attention and practical approaches to be fulfilled, owing to difficulties caused by large-scale area loss and less legitimate neighboring information. These difficulties have made outpainted images handled by most of the existing models unrealistic to human eyes and spatially inconsistent. When upsampling through deconvolution to generate fake content, the naive generation methods may lead to results lacking high-frequency details and structural authenticity. Therefore, as our novelties to handle image outpainting problems, we introduce structural prior as a condition to optimize the generation quality and a new semantic embedding term to enhance perceptual sanity. we propose a deep learning method based on Generative Adversarial Network (GAN) and condition edges as structural prior in order to assist the generation. We use a multi-phase adversarial training scheme that comprises edge inference training, contents inpainting training, and joint training. The newly added semantic embedding loss is proved effective in practice.

随着深度学习技术的快速发展和计算能力的提高，深度学习已广泛应用于高光谱图像（HSI）分类领域。通常，深度学习模型通常包含许多可训练参数，并且需要大量标记的样品来实现最佳性能。然而，关于HSI分类，由于手动标记的难度和耗时的性质，大量标记的样本通常难以获取。因此，许多研究工作侧重于建立一个少数标记样本的HSI分类的深层学习模型。在本文中，我们专注于这一主题，并对相关文献提供系统审查。具体而言，本文的贡献是双重的。首先，相关方法的研究进展根据学习范式分类，包括转移学习，积极学习和少量学习。其次，已经进行了许多具有各种最先进的方法的实验，总结了结果以揭示潜在的研究方向。更重要的是，虽然深度学习模型（通常需要足够的标记样本）和具有少量标记样本的HSI场景之间存在巨大差距，但是通过深度学习融合，可以很好地表征小样本集的问题方法和相关技术，如转移学习和轻量级模型。为了再现性，可以在HTTPS://github.com/shuguoj/hsi-classification中找到纸张中评估的方法的源代码.git。

The International Workshop on Reading Music Systems (WoRMS) is a workshop that tries to connect researchers who develop systems for reading music, such as in the field of Optical Music Recognition, with other researchers and practitioners that could benefit from such systems, like librarians or musicologists. The relevant topics of interest for the workshop include, but are not limited to: Music reading systems; Optical music recognition; Datasets and performance evaluation; Image processing on music scores; Writer identification; Authoring, editing, storing and presentation systems for music scores; Multi-modal systems; Novel input-methods for music to produce written music; Web-based Music Information Retrieval services; Applications and projects; Use-cases related to written music. These are the proceedings of the 3rd International Workshop on Reading Music Systems, held in Alicante on the 23rd of July 2021.

Recent years witnessed the breakthrough of face recognition with deep convolutional neural networks. Dozens of papers in the field of FR are published every year. Some of them were applied in the industrial community and played an important role in human life such as device unlock, mobile payment, and so on. This paper provides an introduction to face recognition, including its history, pipeline, algorithms based on conventional manually designed features or deep learning, mainstream training, evaluation datasets, and related applications. We have analyzed and compared state-of-the-art works as many as possible, and also carefully designed a set of experiments to find the effect of backbone size and data distribution. This survey is a material of the tutorial named The Practical Face Recognition Technology in the Industrial World in the FG2023.

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.

Unsupervised domain adaptation (UDA) for semantic segmentation is a promising task freeing people from heavy annotation work. However, domain discrepancies in low-level image statistics and high-level contexts compromise the segmentation performance over the target domain. A key idea to tackle this problem is to perform both image-level and feature-level adaptation jointly. Unfortunately, there is a lack of such unified approaches for UDA tasks in the existing literature. This paper proposes a novel UDA pipeline for semantic segmentation that unifies image-level and feature-level adaptation. Concretely, for image-level domain shifts, we propose a global photometric alignment module and a global texture alignment module that align images in the source and target domains in terms of image-level properties. For feature-level domain shifts, we perform global manifold alignment by projecting pixel features from both domains onto the feature manifold of the source domain; and we further regularize category centers in the source domain through a category-oriented triplet loss and perform target domain consistency regularization over augmented target domain images. Experimental results demonstrate that our pipeline significantly outperforms previous methods. In the commonly tested GTA5$\rightarrow$Cityscapes task, our proposed method using Deeplab V3+ as the backbone surpasses previous SOTA by 8%, achieving 58.2% in mIoU.

通过对抗训练的雾霾图像转换的关键程序在于仅涉及雾度合成的特征，即表示不变语义内容的特征，即内容特征。以前的方法通过利用它在培训过程中对Haze图像进行分类来分开单独的内容。然而，在本文中，我们认识到在这种技术常规中的内容式解剖学的不完整性。缺陷的样式功能与内容信息纠缠不可避免地引导阴霾图像的呈现。要解决，我们通过随机线性插值提出自我监督的风格回归，以减少风格特征中的内容信息。烧蚀实验表明了静态感知雾度图像合成中的解开的完整性及其优越性。此外，所产生的雾度数据应用于车辆检测器的测试概括。雾度和检测性能之间的进一步研究表明，雾度对车辆探测器的概括具有明显的影响，并且这种性能降低水平与雾度水平线性相关，反过来验证了该方法的有效性。

由于波长依赖性的光衰减，折射和散射，水下图像通常遭受颜色变形和模糊的细节。然而，由于具有未变形图像的数量有限数量的图像作为参考，培训用于各种降解类型的深度增强模型非常困难。为了提高数据驱动方法的性能，必须建立更有效的学习机制，使得富裕监督来自有限培训的示例资源的信息。在本文中，我们提出了一种新的水下图像增强网络，称为Sguie-net，其中我们将语义信息引入了共享常见语义区域的不同图像的高级指导。因此，我们提出了语义区域 - 明智的增强模块，以感知不同语义区域从多个尺度的劣化，并将其送回从其原始比例提取的全局注意功能。该策略有助于实现不同的语义对象的强大和视觉上令人愉快的增强功能，这应该由于对差异化增强的语义信息的指导应该。更重要的是，对于在训练样本分布中不常见的那些劣化类型，指导根据其语义相关性与已经良好的学习类型连接。对公共数据集的广泛实验和我们拟议的数据集展示了Sguie-Net的令人印象深刻的表现。代码和建议的数据集可用于：https：//trentqq.github.io/sguie-net.html

深度学习已被广​​泛用于医学图像分割，并且录制了录制了该领域深度学习的成功的大量论文。在本文中，我们使用深层学习技术对医学图像分割的全面主题调查。本文进行了两个原创贡献。首先，与传统调查相比，直接将深度学习的文献分成医学图像分割的文学，并为每组详细介绍了文献，我们根据从粗略到精细的多级结构分类目前流行的文献。其次，本文侧重于监督和弱监督的学习方法，而不包括无监督的方法，因为它们在许多旧调查中引入而且他们目前不受欢迎。对于监督学习方法，我们分析了三个方面的文献：骨干网络的选择，网络块的设计，以及损耗功能的改进。对于虚弱的学习方法，我们根据数据增强，转移学习和交互式分割进行调查文献。与现有调查相比，本调查将文献分类为比例不同，更方便读者了解相关理由，并将引导他们基于深度学习方法思考医学图像分割的适当改进。

基于3DCNN，ConvlSTM或光流的先前方法在视频显着对象检测（VSOD）方面取得了巨大成功。但是，它们仍然遭受高计算成本或产生的显着图质量较差的困扰。为了解决这些问题，我们设计了一个基于时空存储器（STM）网络，该网络从相邻帧中提取当前帧的有用时间信息作为VSOD的时间分支。此外，以前的方法仅考虑无时间关联的单帧预测。结果，模型可能无法充分关注时间信息。因此，我们最初将框架间的对象运动预测引入VSOD。我们的模型遵循标准编码器 - 编码器体系结构。在编码阶段，我们通过使用电流及其相邻帧的高级功能来生成高级的时间特征。这种方法比基于光流的方法更有效。在解码阶段，我们提出了一种有效的空间和时间分支融合策略。高级特征的语义信息用于融合低级特征中的对象细节，然后逐步获得时空特征以重建显着性图。此外，受图像显着对象检测（ISOD）中常用的边界监督的启发，我们设计了一种运动感知损失，用于预测对象边界运动，并同时对VSOD和对象运动预测执行多任务学习，这可以进一步促进模型以提取提取的模型时空特征准确并保持对象完整性。在几个数据集上进行的广泛实验证明了我们方法的有效性，并且可以在某些数据集上实现最新指标。所提出的模型不需要光流或其他预处理，并且在推理过程中可以达到近100 fps的速度。

作为许多自主驾驶和机器人活动的基本组成部分，如自我运动估计，障碍避免和场景理解，单眼深度估计（MDE）引起了计算机视觉和机器人社区的极大关注。在过去的几十年中，已经开发了大量方法。然而，据我们所知，对MDE没有全面调查。本文旨在通过审查1970年至2021年之间发布的197个相关条款来弥补这一差距。特别是，我们为涵盖各种方法的MDE提供了全面的调查，介绍了流行的绩效评估指标并汇总公开的数据集。我们还总结了一些代表方法的可用开源实现，并比较了他们的表演。此外，我们在一些重要的机器人任务中审查了MDE的应用。最后，我们通过展示一些有希望的未来研究方向来结束本文。预计本调查有助于读者浏览该研究领域。

Deep learning-based methods have achieved significant performance for image defogging. However, existing methods are mainly developed for land scenes and perform poorly when dealing with overwater foggy images, since overwater scenes typically contain large expanses of sky and water. In this work, we propose a Prior map Guided CycleGAN (PG-CycleGAN) for defogging of images with overwater scenes. To promote the recovery of the objects on water in the image, two loss functions are exploited for the network where a prior map is designed to invert the dark channel and the min-max normalization is used to suppress the sky and emphasize objects. However, due to the unpaired training set, the network may learn an under-constrained domain mapping from foggy to fog-free image, leading to artifacts and loss of details. Thus, we propose an intuitive Upscaling Inception Module (UIM) and a Long-range Residual Coarse-to-fine framework (LRC) to mitigate this issue. Extensive experiments on qualitative and quantitative comparisons demonstrate that the proposed method outperforms the state-of-the-art supervised, semi-supervised, and unsupervised defogging approaches.