卫星图像中的云的检测是遥感中的大数据的基本预处理任务。卷积神经网络（CNNS）在检测卫星图像中的云中大大提升了最先进的，但是现有的基于CNN的方法昂贵，因为它们需要大量具有昂贵的像素级云标签的训练图像。为了减轻这种成本，我们提出了针对云检测（FCD）的定点GaN，这是一种弱监督的方法。只有图像级标签训练，我们学习在清晰和阴天的图像之间的固定点转换，因此在翻译期间只影响云。这样做使我们的方法能够通过将卫星图像转换为清除并将阈值设置为两个图像之间的差异来预测像素级云标签。此外，我们提出了FCD +，在那里我们利用CNN的标签噪声稳健性来改进FCD的预测，从而进一步改进。我们展示了我们对Landsat-8生物群落云检测数据集的方法的有效性，在那里我们将性能接近与昂贵的像素级标签一起列车的现有全监督方法。通过微调我们的FCD +，只有1％的可用像素级标签，我们符合完全监督方法的性能。

Figure 1. Multi-domain image-to-image translation results on the CelebA dataset via transferring knowledge learned from the RaFD dataset. The first and sixth columns show input images while the remaining columns are images generated by StarGAN. Note that the images are generated by a single generator network, and facial expression labels such as angry, happy, and fearful are from RaFD, not CelebA.

For satellite images, the presence of clouds presents a problem as clouds obscure more than half to two-thirds of the ground information. This problem causes many issues for reliability in a noise-free environment to communicate data and other applications that need seamless monitoring. Removing the clouds from the images while keeping the background pixels intact can help address the mentioned issues. Recently, deep learning methods have become popular for researching cloud removal by demonstrating promising results, among which Generative Adversarial Networks (GAN) have shown considerably better performance. In this project, we aim to address cloud removal from satellite images using AttentionGAN and then compare our results by reproducing the results obtained using traditional GANs and auto-encoders. We use RICE dataset. The outcome of this project can be used to develop applications that require cloud-free satellite images. Moreover, our results could be helpful for making further research improvements.

由于技术成本的降低和卫星发射的增加，卫星图像变得越来越流行和更容易获得。除了提供仁慈的目的外，还可以出于恶意原因（例如错误信息）使用卫星数据。事实上，可以依靠一般图像编辑工具来轻松操纵卫星图像。此外，随着深层神经网络（DNN）的激增，可以生成属于各种领域的现实合成图像，与合成生成的卫星图像的扩散有关的其他威胁正在出现。在本文中，我们回顾了关于卫星图像的产生和操纵的最新技术（SOTA）。特别是，我们既关注从头开始的合成卫星图像的产生，又要通过图像转移技术对卫星图像进行语义操纵，包括从一种类型的传感器到另一种传感器获得的图像的转换。我们还描述了迄今已研究的法医检测技术，以对合成图像伪造进行分类和检测。虽然我们主要集中在法医技术上明确定制的，该技术是针对AI生成的合成内容物的检测，但我们还审查了一些用于一般剪接检测的方法，这些方法原则上也可以用于发现AI操纵图像

Recently deep neural networks, which require a large amount of annotated samples, have been widely applied in nuclei instance segmentation of H\&E stained pathology images. However, it is inefficient and unnecessary to label all pixels for a dataset of nuclei images which usually contain similar and redundant patterns. Although unsupervised and semi-supervised learning methods have been studied for nuclei segmentation, very few works have delved into the selective labeling of samples to reduce the workload of annotation. Thus, in this paper, we propose a novel full nuclei segmentation framework that chooses only a few image patches to be annotated, augments the training set from the selected samples, and achieves nuclei segmentation in a semi-supervised manner. In the proposed framework, we first develop a novel consistency-based patch selection method to determine which image patches are the most beneficial to the training. Then we introduce a conditional single-image GAN with a component-wise discriminator, to synthesize more training samples. Lastly, our proposed framework trains an existing segmentation model with the above augmented samples. The experimental results show that our proposed method could obtain the same-level performance as a fully-supervised baseline by annotating less than 5% pixels on some benchmarks.

图像和视频中的消防本地化是自治系统对抗火事故的重要一步。基于深神经网络的最先进的图像分割方法需要大量的像素注释样本以以完全监督的方式训练卷积神经网络（CNNS）。在本文中，我们考虑了图像中的火灾的弱监管，其中仅使用图像标签来训练网络。我们示出在火灾分割的情况下，这是二进制分割问题，分类中的中层中的特征的平均值可以比传统的类激活映射（CAM）方法更好。我们还建议通过在上次卷积层的特征上添加旋转等值正则化损耗来进一步提高分割精度。我们的结果表明，对弱势监督的火灾细分的基线方法显着改善。

如今，表面裂缝是公共基础设施的常见景象。最近的工作通过支持使用背景曲面裂缝的机器学习方法支持结构维护措施，解决了这个问题，使它们易于本地化。然而，这些方法的常见问题是创建一个良好的运行算法，训练数据需要详细地注释属于裂缝的像素。我们的工作提出了一种弱监督的方法，它利用CNN分类器来创建曲面裂纹分割图。我们使用此分类器通过使用其类激活映射和基于贴片的分类方法来创建粗糙的裂缝本地化地图，并用基于阈值的方法熔断器来融合它，以分段为大多数较暗的裂纹像素。分类器有助于抑制背景区域的噪声，这通常是通过标准阈值处理方法被错误地突出显示的裂缝。我们专注于我们的方法的易于实现，并且显示在几个表面裂纹数据集上表现良好，即使用于训练的唯一数据是简单的分类标签，也可以有效地进行分割裂缝。

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。

我们提出了一种新颖的暗影拆除深层学习方法。灵感来自暗影形成的物理模型，我们使用线性照明变换来模拟图像中的阴影效果，允许阴影图像表示为无影子图像，阴影参数和遮罩层的组合。我们使用两个深网络，即SP-Net和M-Net，分别预测阴影参数和阴影遮罩。该系统允许我们删除图像的影子效果。然后，我们采用了一个素食网络，I-Net，以进一步改进结果。我们在最具挑战性的阴影删除数据集（ISTD）上培训并测试我们的框架。我们的方法通过20 \％的阴影区域的根均线误差（RMSE）来改善最先进的。此外，这种分解允许我们制定基于补丁的弱监督暗影去除方法。这种型号可以培训，没有任何暗影图像（非常麻烦的图像），与使用完全配对的阴影和无影子图像训练的最先进的方法相比，实现了竞争阴影去除结果。最后，我们介绍了SBU-timelapse，一个视频阴影删除数据集，用于评估阴影清除方法。

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。

自动驾驶汽车必须能够可靠地处理不利的天气条件（例如，雪地）安全运行。在本文中，我们研究了以不利条件捕获的转动传感器输入（即图像）的想法，将其下游任务（例如，语义分割）可以达到高精度。先前的工作主要将其作为未配对的图像到图像翻译问题，因为缺乏在完全相同的相机姿势和语义布局下捕获的配对图像。虽然没有完美对准的图像，但可以轻松获得粗配上的图像。例如，许多人每天在好天气和不利的天气中驾驶相同的路线；因此，在近距离GPS位置捕获的图像可以形成一对。尽管来自重复遍历的数据不太可能捕获相同的前景对象，但我们认为它们提供了丰富的上下文信息来监督图像翻译模型。为此，我们提出了一个新颖的训练目标，利用了粗糙的图像对。我们表明，我们与一致的训练方案可提高更好的图像翻译质量和改进的下游任务，例如语义分割，单眼深度估计和视觉定位。

Diabetic Retinopathy (DR) is a leading cause of vision loss in the world, and early DR detection is necessary to prevent vision loss and support an appropriate treatment. In this work, we leverage interactive machine learning and introduce a joint learning framework, termed DRG-Net, to effectively learn both disease grading and multi-lesion segmentation. Our DRG-Net consists of two modules: (i) DRG-AI-System to classify DR Grading, localize lesion areas, and provide visual explanations; (ii) DRG-Expert-Interaction to receive feedback from user-expert and improve the DRG-AI-System. To deal with sparse data, we utilize transfer learning mechanisms to extract invariant feature representations by using Wasserstein distance and adversarial learning-based entropy minimization. Besides, we propose a novel attention strategy at both low- and high-level features to automatically select the most significant lesion information and provide explainable properties. In terms of human interaction, we further develop DRG-Net as a tool that enables expert users to correct the system's predictions, which may then be used to update the system as a whole. Moreover, thanks to the attention mechanism and loss functions constraint between lesion features and classification features, our approach can be robust given a certain level of noise in the feedback of users. We have benchmarked DRG-Net on the two largest DR datasets, i.e., IDRID and FGADR, and compared it to various state-of-the-art deep learning networks. In addition to outperforming other SOTA approaches, DRG-Net is effectively updated using user feedback, even in a weakly-supervised manner.

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.

Weakly-supervised learning (WSL) has been proposed to alleviate the conflict between data annotation cost and model performance through employing sparsely-grained (i.e., point-, box-, scribble-wise) supervision and has shown promising performance, particularly in the image segmentation field. However, it is still a very challenging problem due to the limited supervision, especially when only a small number of labeled samples are available. Additionally, almost all existing WSL segmentation methods are designed for star-convex structures which are very different from curvilinear structures such as vessels and nerves. In this paper, we propose a novel sparsely annotated segmentation framework for curvilinear structures, named YoloCurvSeg, based on image synthesis. A background generator delivers image backgrounds that closely match real distributions through inpainting dilated skeletons. The extracted backgrounds are then combined with randomly emulated curves generated by a Space Colonization Algorithm-based foreground generator and through a multilayer patch-wise contrastive learning synthesizer. In this way, a synthetic dataset with both images and curve segmentation labels is obtained, at the cost of only one or a few noisy skeleton annotations. Finally, a segmenter is trained with the generated dataset and possibly an unlabeled dataset. The proposed YoloCurvSeg is evaluated on four publicly available datasets (OCTA500, CORN, DRIVE and CHASEDB1) and the results show that YoloCurvSeg outperforms state-of-the-art WSL segmentation methods by large margins. With only one noisy skeleton annotation (respectively 0.14%, 0.02%, 1.4%, and 0.65% of the full annotation), YoloCurvSeg achieves more than 97% of the fully-supervised performance on each dataset. Code and datasets will be released at https://github.com/llmir/YoloCurvSeg.

夜间热红外（NTIR）图像着色，也称为NTIR图像转换为白天颜色图像（NTIR2DC），是一个有希望的研究方向，可促进对人类和不利条件下的智能系统的夜间现场感知（例如，完整的黑暗）。但是，先前开发的方法对于小样本类别的着色性能差。此外，降低伪标签中的高置信度噪声并解决翻译过程中图像梯度消失的问题仍然不足，并且在翻译过程中防止边缘扭曲也很具有挑战性。为了解决上述问题，我们提出了一个新颖的学习框架，称为记忆引导的协作关注生成对抗网络（MORNGAN），该框架受到人类的类似推理机制的启发。具体而言，设计了记忆引导的样本选择策略和自适应协作注意力丧失，以增强小样本类别的语义保存。此外，我们提出了一个在线语义蒸馏模块，以挖掘并完善NTIR图像的伪标记。此外，引入条件梯度修复损失，以减少翻译过程中边缘失真。在NTIR2DC任务上进行的广泛实验表明，在语义保存和边缘一致性方面，提出的Morngan明显优于其他图像到图像翻译方法，这有助于显着提高对象检测精度。

从众包标签或公开的数据创建的大规模数据集已经至关重要，为大规模学习算法提供培训数据。虽然这些数据集更容易获取，但数据经常嘈杂和不可靠，这是对弱监督学习技术的激励研究。在本文中，我们提出了原始想法，帮助我们在变更检测的背景下利用此类数据集。首先，我们提出了引导的各向异性扩散（GAD）算法，其使用输入图像改善语义分割结果作为执行边缘保留滤波的引导件。然后，我们展示了它在改变检测中量身定制的两个弱监督的学习策略中的潜力。第一策略是一种迭代学习方法，它将模型优化和数据清理使用GAD从开放矢量数据生成的大规模改变检测数据集中提取有用信息。第二个在新的空间注意层内包含GAD，其增加训练训练的弱监管网络的准确性，以从图像级标签执行像素级预测。在4个不同的公共数据集上展示了关于最先进的最先进的改进。

Conditional Generative Adversarial Networks (GANs) for cross-domain image-to-image translation have made much progress recently [7,8,21,12,4,18]. Depending on the task complexity, thousands to millions of labeled image pairs are needed to train a conditional GAN. However, human labeling is expensive, even impractical, and large quantities of data may not always be available. Inspired by dual learning from natural language translation [23], we develop a novel dual-GAN mechanism, which enables image translators to be trained from two sets of unlabeled images from two domains. In our architecture, the primal GAN learns to translate images from domain U to those in domain V , while the dual GAN learns to invert the task. The closed loop made by the primal and dual tasks allows images from either domain to be translated and then reconstructed. Hence a loss function that accounts for the reconstruction error of images can be used to train the translators. Experiments on multiple image translation tasks with unlabeled data show considerable performance gain of Du-alGAN over a single GAN. For some tasks, DualGAN can even achieve comparable or slightly better results than conditional GAN trained on fully labeled data.

生成的对抗网络（GANS）已经促进了解决图像到图像转换问题的新方向。不同的GANS在目标函数中使用具有不同损耗的发电机和鉴别器网络。仍然存在差距来填补所生成的图像的质量并靠近地面真理图像。在这项工作中，我们介绍了一个名为循环辨别生成的对抗网络（CDGAN）的新的图像到图像转换网络，填补了上述空白。除了加速本的原始架构之外，所提出的CDGAN通过结合循环图像的附加鉴别器网络来产生高质量和更现实的图像。所提出的CDGAN在三个图像到图像转换数据集上进行测试。分析了定量和定性结果，并与最先进的方法进行了比较。在三个基线图像到图像转换数据集中，所提出的CDGAN方法优于最先进的方法。该代码可在https://github.com/kishankancharagunta/cdgan获得。

This work proposes a framework developed to generalize Critical Heat Flux (CHF) detection classification models using an Unsupervised Image-to-Image (UI2I) translation model. The framework enables a typical classification model that was trained and tested on boiling images from domain A to predict boiling images coming from domain B that was never seen by the classification model. This is done by using the UI2I model to transform the domain B images to look like domain A images that the classification model is familiar with. Although CNN was used as the classification model and Fixed-Point GAN (FP-GAN) was used as the UI2I model, the framework is model agnostic. Meaning, that the framework can generalize any image classification model type, making it applicable to a variety of similar applications and not limited to the boiling crisis detection problem. It also means that the more the UI2I models advance, the better the performance of the framework.